Stretchable magnetic resonance (MR) receive coils show shifts in their resonance frequency when stretched. An in-field receiver measures the frequency response of a stretchable coil. The receiver and coil are designed to operate at 128 MHz for a 3T MR scanner. Based on the measured frequency response, we are able to detect the changes of the resonance frequency of the coil. We show a proportional-integral-derivative controller that tracks the changes in resonance frequency and retunes the stretchable coil. The settling time of the control loop is less than 3.8ms. The retuning system reduces the loss in signal-to-noise ratio of phantom images from 1.6 dB to 0.3 dB, when the coil is stretched by 40% and the coil is retuned to 128 MHz.

Cancer related fatigue is a chronic disease that may persist up to 10 years after treatment of cancer has been finished successfully and is one of the most prevalent problems of cancer survivors. Cancer related fatigue is a complex symptom that is not sufficiently understood and difficult to remedy. Patients do not necessarily follow a treatment plan with regular in-office follow-ups. As a consequence, physicians lack of knowledge how their patients are coping with their fatigue in daily life. To overcome the resulting knowledge gap, we developed a smart-phone based monitoring system providing activity data from smart-phone sensors. Furthermore, based on self-reports following the experience based sampling method, the app asks the patients to report their perceived degree of fatigue and interference of fatigue with daily life by means of digital questionnaires. To evaluate the monitoring system in an observational study, we recruited 7 patients suffering from cancer related fatigue and tracked them over 2 to 3 weeks. We collected about 2700 hours of activity data and over 500 completed questionnaires. Based on this study, we conclude that our monitoring system can be used in a clinical trial to examine cancer related fatigue.

This paper investigates a new annotation technique that reduces significantly the amount of time to annotate training data for gesture recognition. Conventionally, the annotations comprise the start and end times, and the corresponding labels of gestures in sensor recordings. In this work, we propose a one-time point annotation in which labelers do not have to select the start and end time carefully, but just mark a one-time point within the time a gesture is happening. The technique gives more freedom and reduces significantly the burden for labelers. To make the one-time point annotations applicable, we propose a novel BoundarySearch algorithm to find automatically the correct temporal boundaries of gestures by discovering data patterns around their given one-time point annotations. The corrected annotations are then used to train gesture models. We evaluate the method on three applications from wearable gesture recognition with various gesture classes (10-17 classes) recorded with different sensor modalities. The results show that training on the corrected annotations can achieve performances close to a fully supervised training on clean annotations (lower by just up to 5 percent F1-score on average). Furthermore, the BoundarySearch algorithm is also evaluated on the ChaLearn 2014 multi-modal gesture recognition challenge recorded with Kinect sensors from computer vision and achieves similar results.

We present and evaluate measurement fusion and decision fusion for recognizing apnea and periodic limb movement in sleep episodes. We used an in-bed sensor system composed of an array of strain gauges to detect pressure changes corresponding to respiration and body movement. The sensor system was placed under the bed mattress during sleep and continuously recorded pressure changes. We evaluated both fusion frameworks in a study with nine adult participants that had mixed occurrences of normal sleep, apnea, and periodic limb movement. Both frameworks yielded similar recognition accuracies of 72.1 ± ∼ 12% compared to 63.7 ± 17.4% for a rule-based detection reported in the literature. We concluded that the pattern recognition methods can outperform previous rule-based detection methods for classifying disordered breathing and period limb movements simultaneously.

Wearables find in sports one of their main applications. In recent years, many wearable devices have been commercially released such as the Babolat Play or Sony Smart Tennis Sensor that detect and classify different types of tennis shots and provide a performance analysis to the player. However, available devices focus on a single technical element of tennis only - the shot. As tennis performance is the result of a full body coordination and timing of the movement, the present work wants to take a broader view at the tennis player performance and include the simultaneous work of legs and arms with the goal to time elements of movement. We design a sensor system with three inertial measurement units, one attached to each foot as well as one at the racket. We develop a pipeline to detect and classify leg and arm movement and implement a gesture recognition for the shooting arm based on LCSS (longest common subsequence). The algorithm distinguishes between forehand and backhand (with topspin and slice, respectively) as well as a smash. Footwork is first segmented into potential steps and then classified by a support vector machine between shot and side steps. In the person-dependent case the algorithm achieved 87% recall and 89% precision. The step recognition algorithm has been able to detect 76% of the steps with a classification accuracy of 95%. Based on these results timing information within the shooting state can be robustly obtained which is crucial for a thorough analysis of the whole shot.

Epidermal sensors, which form an intimate and robust contact with the skin, are capable of providing clinically relevant information about cardiovascular health, electrophysiology and dermatology with high accuracy and in an unobtrusive manner. To enable clinical applications, however, continuous and long-term monitoring is necessary. In addition, wireless and energetically autonomous systems are highly desirable to eliminate the needs of tethers and cables for powering and data transmission. Such requirements call for devices that combine accurate and precise sensing with high performance electronics for signal treatment, communication and power management in formats which conformal laminate on the body. In this work, we present a novel system whose design leverages on the recent developments in low power wearable devices and flexible sensors. It consists of an ultra-low power smartwatch connected to flexible solar modules assembled on a strap and an array of epidermal temperature sensors which are mounted on the wrist. Preliminary experiments show how this platform is well-suited for long-term, accurate and continuous mapping of the temperature of the skin.

Automatic recognition of user context is essential for a variety of emerging applications, such as context-dependent content delivery, telemonitoring of medical patients, or quantified life-logging. Although not explicitly observable as, e.g., activities, an important aspect towards understanding user context lies in the affective state of mood.While significant work has been done to assess mood, most approaches require the use of customized sensors and controlled laboratory settings.In this work, we engineer a recognition pipeline to recognize daily activities from commercially popularized wearable electronics. In turn, we use predicted activities to learn a regression model capable of assessing user mood.Using only commercially popularized wearable devices, we enable the potential for seamless deployment to the general public.Conducting a real-world study with a prototype system, we collect and evaluate data from 18 users, who provide over 93 user-days of labelled activity data.Regressing for mood based on predicted daily activities, we are able to infer mood angles with a mean absolute error of$0.24\pi$radians on the Circumplex Model of Affect.Comparing with benchmark approaches, our approach outperforms with statistical significance and is validated for robustness against noise from activity misclassification.

The machine recognition of user trajectories and activities is fundamental to devise context-aware applications for support and monitoring in daily life. So far, tracking and activity recognition were mostly considered as orthogonal problems, which limits the richness of possible context inference. In this work, we introduce the novel unified computational and representational framework S-SMART that simultaneously models the environment state (semantic mapping), localizes the user within this map (tracking), and recognizes interactions with the environment (activity recognition). Thus, S-SMART identifies which activities the user executes where (e.g., turning a handle next to a window ), and reflects the outcome of these actions by updating the world model (e.g., the window is now open ). This in turn conditions the future possibility of executing actions at specific places (e.g., closing the window is likely to be the next action at this location). S-SMART works in a self-contained manner and iteratively builds the semantic map from wearable sensors only. This enables the seamless deployment to new environments. We characterize S-SMART in an experimental dataset with people performing hand actions as part of their usual routines at home and in office buildings. The framework combines dead reckoning from a foot-worn motion sensor with template-matching-based action recognition, identifying objects in the environment (windows, doors, water taps, phones, etc.) and tracking their state (open/closed, etc.). In real-life recordings with up to 23 action classes, S-SMART consistently outperforms independent systems for positioning and activity recognition, and constructs accurate semantic maps. This environment representation enables novel applications that build upon information about the arrangement and state of the user’s surroundings. For example, it may be possible to remind elderly people of a window that they left open before leaving the house, or of a plant they did not water yet, using solely wearable sensors.

Sensor technology that is unobtrusively integrated into the clothing and equipment of an athlete can support the training of sport activities and monitor the athlete's progress. In this paper, we propose two wearable systems that support ice hockey players in the training of skating and shooting. These assistants measure the motions of players and compare them with reference executions of the same activities by professional players. A third system that we introduce monitors the player;s activities during a hockey game and creates a match report for objective performance measurement. For each of the three proposed applications, we present a prototype setup that we evaluate with amateur and professional players. The main findings are i) that with a skate-worn motion sensor and user-dependent training, eight skating motions can be spotted with an accuracy above 90%, ii) that stick-integrated sensors enable the measurement of relevant shot features, which differentiate professional from amateur athletes, and iii) that it is possible to spot important ice hockey activities in the signals of body-worn motion sensors worn during a game.

Regions in urban environments often afford a mixture of different utilities. Their identification allows urban planners to leverage important insights on the emerging functional dynamics of cities. With the increasing availability of human mobility data and other forms of online digital breadcrumbs, we can now characterize urban regions with multi-source features. In this work, we form a comprehensive view of urban regions by fusing features depicting their temporal, spatial, and demographic aspects. Aggregating 47K explicitly stated trip purposes into their respective destination regions, we obtain multi-dimensional ground-truths on the functionalities of urban spaces. Given fused features and training labels, we can perform supervised learning, via multi-output regression, to estimate the functional composition of urban spaces. With 14 functional dimensions, our approach using crowd-augmented travel survey predictors delivers a mean absolute error of 3.9, approximately half of the error resulting from a mean-based straw man approach (mean absolute error of 7.9). Clustering estimated regional functionalities, we find highly coherent cluster assignments (adjusted Rand Index of 0.81) compared to clustering directly on regional functionality labels. Finally, we provide an illustrative case-study where clustering of estimated region functionalities can be used to intuitively differentiate prototypical spatial neighbourhoods of a large metropolitan.

We introduce a hierarchical nonparametric topic modeling approach to infer activity routines from context sensor data streams based on a distance dependent Chinese restaurant process (ddCRP). Our approach does not require labeled data at any stage. Neither does our approach depend on time-invariant sliding windows to sample context word statistics. Our activity discovery approach builds on the idea that context words occurring within one activity are semantically similar, whereas context words of different activities are less similar. Context word streams are segmented into supersamples and then semantic and temporal features are obtained to construct a segmentation prior that relates supersamples via its context words. Our hierarchical model uses the segmentation prior and ddCRP to group supersamples and the Chinese restaurant process (CRP) to discover activities. We evaluate our approach using the Opportunity dataset that contains activities of daily living. Besides being nonparametric, our ddCRP based model outperforms both, classic parametric latent Dirichlet allocation (LDA) and the nonparametric Chinese restaurant franchise (CRF). We conclude that ddCRP+CRP is an adequate approach for fully unsupervised activity discovery from context sensor data.

People with Parkinson’s disease (PD) suffer from declining mobility capabilities, which cause a prevalent risk of falling. Commonly, short periods of motor blocks occur during walking, known as freezing of gait (FoG). To slow the progressive decline of motor abilities, people with PD usually undertake stationary motor-training exercises in the clinics or supervised by physiotherapists. We present a wearable system for the support of people with PD and FoG. The system is designed for independent use. It enables motor training and gait assistance at home and other unsupervised environments. The system consists of three components. First, FoG episodes are detected in real time using wearable inertial sensors and a smartphone as the processing unit. Second, a feedback mechanism triggers a rhythmic auditory signal to the user to alleviate freeze episodes in an assistive mode. Third, the smartphone-based application features support for training exercises. Moreover, the system allows unobtrusive and long-term monitoring of the user’s clinical condition by transmitting sensing data and statistics to a telemedicine service. We investigate the at-home acceptance of the wearable system in a study with nine PD subjects. Participants deployed and used the system on their own, without any clinical support, at their homes during three protocol sessions in 1 week. Users’ feedback suggests an overall positive attitude toward adopting and using the system in their daily life, indicating that the system supports them in improving their gait. Further, in a data-driven analysis with sensing data from five participants, we study whether there is an observable effect on the gait during use of the system. In three out of five subjects, we observed a decrease in FoG duration distributions over the protocol days during gait-training exercises. Moreover, sensing data-driven analysis shows a decrease in FoG duration and FoG number in four out of five participants when they use the system as a gait-assistive tool during normal daily life activities at home.

Today's health care is difficult to imagine without the possibility to objectively measure various physiological parameters related to patients' symptoms (from temperature through blood pressure to complex tomographic procedures). Psychiatric care remains a notable exception that heavily relies on patient interviews and self-assessment. This is due to the fact that mental illnesses manifest themselves mainly in the way patients behave throughout their daily life and, until recently there were no "behavior measurement devices." This is now changing with the progress in wearable activity recognition and sensor enabled smartphones. In this paper, we introduce a system, which, based on smartphone-sensing is able to recognize depressive and manic states and detect state changes of patients suffering from bipolar disorder. Drawing upon a real-life dataset of ten patients, recorded over a time period of 12 weeks (in total over 800 days of data tracing 17 state changes) by four different sensing modalities, we could extract features corresponding to all disease-relevant aspects in behavior. Using these features, we gain recognition accuracies of 76% by fusing all sensor modalities and state change detection precision and recall of over 97%. This paper furthermore outlines the applicability of this system in the physician-patient relations in order to facilitate the life and treatment of bipolar patients.

Freezing of gait (FoG) is a common gait impairment among patients with advanced Parkinson's disease. FoG is associated with falls and negatively impacts the patient's quality of life. Wearable systems that detect FoG in real time have been developed to help patients resume walking by means of rhythmic cueing. Current methods focus on detection, which require FoG events to happen first, while their prediction opens the road to preemptive cueing, which might help subjects to avoid freeze altogether. We analyzed electrocardiography (ECG) and skin-conductance (SC) data from 11 subjects who experience FoG in daily life, and found statistically significant changes in ECG and SC data just before the FoG episodes, compared to normal walking. Based on these findings, we developed an anomaly-based algorithm for predicting gait freeze from relevant SC features. We were able to predict 71.3% from 184 FoG with an average of 4.2 s before a freeze episode happened. Our findings enable the possibility of wearable systems, which predict with few seconds before an upcoming FoG from SC, and start external cues to help the user avoid the gait freeze.

Patients with Parkinson's disease often experience freezing of gait, which bears a high risk of falling, a prevalent cause for morbidity and mortality. In this work we present GaitAssist, a wearable system for freezing of gait support in daily life. The system provides real-time auditory cueing after the onset of freezing episodes. Furthermore, GaitAssist implements training exercises to learn how to handle freezing situations. GaitAssist is the result of a design process where we considered the input of engineers, clinicians and 18 Parkinson's disease patients, in order to find an optimal trade-off between system wearability and performance. We tested the final system in a user study with 5 additional patients. They reported a reduction in the freezing of gait duration as a result of the auditory stimulation provided, and that they feel the system enhanced their confidence during walking.

We investigate the use of WiFi Received Signal Strength Information (RSSI) at a mobile phone for the recognition of situations, activities and gestures. In particular, we propose a device-free and passive activity recognition system that does not require any device carried by the user and uses ambient signals. We discuss challenges and lessons learned for the design of such a system on a mobile phone and propose appropriate features to extract activity characteristics from RSSI. We demonstrate the feasibility of recognising activities, gestures and environmental situations from RSSI obtained by a mobile phone. The case studies were conducted over a period of about two months in which about 12 hours of continuous RSSI data was sampled, in two countries and with 11 participants in total. Results demonstrate the potential to utilise RSSI for the extension of the environmental perception of a mobile device as well as for the interaction with touch-free gestures. The system achieves an accuracy of 0.51 while distinguishing as many as 11 gestures and can reach 0.72 on average for four more disparate ones.

In the past few years, many technologies for motion capturing are used to examine posture and movements of musicians. Several video and sensor based systems are developed and available for these observations. In research and for augmented instruments one parameter is underrepresented so far. It is pressure and force applied by the musician while playing the musical instrument. In this paper we show a force-sensitive-resistor (FSR) based solution for position and force sensing between the contact point of the fingers and the musical instrument exemplarily built on a fingerboard of a violin and a load cell based approach built between the finger board and the neck of the violin. Main focus was an unobtrusive integration of the sensors into a traditional musical instrument. A software tool allows the recording of the sensor data and real-time MIDI and OSC data transmission for easy integration into electronic music setups.

The process of learning a novel body movement exposes a student to multiple difficulties. Understanding the range of motion is fundamental for learning to control the involved body parts. Theory and instructions need to be mapped to body movements: a student not only needs to mimic or copy the range of motion of individual body parts, but he also needs to trigger the motion fragments in the correct order. Not only correct order is important, but also precise timing. If the movements in questions are intensified by additional load, optimality of the motion patterns becomes crucial. Sub-optimal execution of an exercise reduces the performance or can even induce failure of completion. Correct execution is a subtle interplay between the correct forces at the right times. In this paper, we present a sensor system that is able to categorize movements into multiple quality classes and athletes into two experience classes. For this work we conducted a study involving 16 athletes performing squat-presses, a simple yet non-trivial exercise requiring barbells. We calculated various features out of raw accelerometer data acquired by two inertial measurement units attached to the athletes' bodies. We evaluated exercise performances of the participants ranging from beginners to experts. We introduce the biomechanical properties of the movement and show that our system can differentiate between four quality classes (poor, fair, good, perfect) with an accuracy above 93% and discriminate between a beginner athlete and an advanced athlete with an accuracy of more than 94%.

Activity recognition systems based on body-worn motion sensors suffer from a decrease in performance during the deployment and run-time phases, because of probable changes in the sensors (e.g. displacement or rotatation), which is the case in many real-life scenarios (e.g. mobile phone in a pocket). Existing approaches to achieve robustness tend to sacrifice information (e.g. by rotation-invariant features) or reduce the weight of the anomalous sensors at the classifier fusion stage (adaptive fusion), ignoring data which might still be perfectly meaningful, although different from the training data. We propose to use adaptation to rebuild the classifier models of the sensors which have changed position by a two-step approach: in the first step, we run an anomaly detection algorithm to automatically detect which sensors are delivering unexpected data; subsequently, we trigger a system self-training process, so that the remaining classifiers retrain the “anomalous” sensors. We show the benefit of this approach in a real activity recognition dataset comprising data from 8 sensors to recognize locomotion. The approach achieves similar accuracy compared to the upper baseline, obtained by retraining the anomalous classifiers on the new data.

Periodic assessments of motor function in children with Cerebral Palsy can enable clinicians to make more informed decisions about the type and timing of treatment interventions. Current clinical practice is limited to sporadic assessments performed in a clinical environment and hence, not suitable for capturing small changes that occur longitudinally. We have developed a shoe-based wearable sensor system that allows unobtrusive long-term collection of center of pressure data in the home setting. So far the shoe-based system has been used to collect data from 15 subjects under supervised and semi-supervised settings. In this paper, we present a novel methodology, based on the analysis of center of pressure trajectories using Active Shape Models, for automated clinical assessment of gait deviations in children with Cerebral Palsy. We show that Active Shape Models can be used to effectively model characteristics of the center of pressure trajectories that are associated with specific aspects of gait deviations. A support vector machine classifier, trained on features derived from the Active Shape Models, is able to achieve an accuracy of greater than 90% at classifying clinical scores of gait deviation severity.

Firefighting is a dangerous task and many research projects have aimed at supporting firefighters during missions by developing new and often costly equipment. In contrast to previous approaches, we use the smartphone to monitor firefighters during real-world missions in order to provide objective data that can be used in post-incident briefings and trainings. In this paper, we present CoenoFire, a smartphone based sensing system aimed at monitoring temporal and behavioral performance indicators of firefighting missions. We validate the performance metrics showing that they can indicate why certain teams performed faster than others in a training scenario conducted by 16 firefighting teams. Furthermore, we deployed CoenoFire over a period of six weeks in a professional fire brigade. In total, 71 firefighters participated in our study and the collected data includes 76 real-world missions totaling to over 148 hours of mission data. Additionally, we visualize real-world mission data and show how mission feedback is supported by the data.

We present SmartActionSLAM, an Android smartphone application that performs location tracking in home and office environments. It uses the integrated motion sensors of the smartphone and an optional foot-mounted inertial measurement unit to track a person. The application implements an instance of the ActionSLAM algorithmic framework. ActionSLAM combines pedestrian dead reckoning with the observation of activities (in SmartActionSLAM: sitting and standing still) to build and update a local landmark map of the user's environment. This map is used to compensate for error accumulation of dead reckoning in a particle filter framework. We show that it is possible to execute the ActionSLAM algorithm in real-time on a smartphone without platform-specific optimizations. Furthermore, we analyze the localization performance of the application in six constrained and two real-life recordings. When using only the smartphone's internal sensors, tracking was adequate in most constrained setups, but failed in the real-world scenarios because of errors in recognizing irregular leg movements. By including the foot-mounted sensor, mapping with a mean landmark positioning error of90% was achieved in all environments. Smart-ActionSLAM is fully wearable and requires no infrastructure in the environment. The approach is therefore ideally suited for rapid deployment in home and office environments, as for example required in patient monitoring studies.

Activity annotation in videos is necessary to create a training dataset for most of activity recognition systems. This is a very time consuming and repetitive task. Crowdsourcing gains popularity to distribute annotation tasks to a large pool of taggers. We present for the first time an approach to achieve good quality for activity annotation in videos through crowdsourcing on the AmazonMechanical Turk platform (AMT). Taggers must annotate the start, end boundaries and the label of all occurrences of activities in videos. Two strategies to detect non-serious taggers according to temporal annotated results are presented. Individual filtering checks the consistence in the answers of each tagger with the characteristic of dataset to identify and remove nonserious taggers. Collaborative filtering checks the agreement in annotations among taggers. The filtering techniques detect and remove non-serious taggers and finally, the majority voting applied to AMT temporal tags to generate one final AMT activity annotation set. We conduct the experiments to get activity annotation from AMT on a subset of two rich datasets frequently used in activity recognition. The results show that our proposed filtering strategies can increase the accuracy by up to 40%. The final annotation set is of comparable quality of the annotation of experts with high accuracy (76% to 92%).

Human activity recognition systems traditionally require a manual annotation of massive training data, which is laborious and non-scalable. An alternative approach is mining existing online crowd-sourced repositories for open-ended, free annotated training data. However, differences across data sources or in observed contexts prevent a crowd-sourced based model reaching user-dependent recognition rates.

Human activity recognition is a core component of context-aware, ubiquitous computing systems. Traditionally, this task is accomplished by analyzing signals of wearable motion sensors. While such signals can effectively distinguish various low-level activities (e.g. walking or standing), two issues exist: First, high-level activities (e.g. watching movies or attending lectures) are difficult to distinguish from motion data alone. Second, instrumentation of complex body sensor network at population scale is impractical. In this work, we take an alternative approach of leveraging rich, dynamic, and crowd-generated self-report data as the basis for in-situ activity recognition. By treating the user as the "sensor", we make use of implicit signals emitted from natural use of mobile smart-phones. Applying an L1-regularized Linear SVM on features derived from textual content, semantic location, and time, we are able to infer 10 meaningful classes of daily life activities with a mean accuracy of up to 83.9%. Our work illustrates a promising first step towards comprehensive, high-level activity recognition using free, crowd-generated, social media data.

The vast availability of mobile phones with built-in movement and location sensors enable the collection of detailed information about human movement even indoors. As mobility is a key element of many processes and activities, an interesting class of information to extract is movement patterns that quantify how humans move, interact and group. In this paper we propose methods for detecting two common pedestrian movement patterns, namely individual following relations and group leadership. The proposed methods for identifying following patterns employ machine learning on features derived using similarity analysis on time lagged sequences of WiFi measurements containing either raw signal strength values or derived locations. To detect leadership we combine the individual following relations into directed graphs and detect leadership within groups by graph link analysis. Methods for detecting these movement patterns open up new possibilities in — amongst others — computational social science, reality mining, marketing research and location-based gaming. We provide evaluation results that show error rates down to 7%, improving over state of the art methods with up to eleven percentage points for following patterns and up to twenty percentage points for leadership patterns. Our method is, contrary to state of the art, also applicable in challenging indoor environments, e.g., multi-story buildings. This implies that even quite small samples allow us to detect information such as how events and campaigns in multistory shopping malls may trigger following in small groups, or which group members typically take the lead when triggered by e.g. commercials, or how rescue or police forces act during training exercises.

In this work, we introduce methods for studying psychological arousal in naturalistic daily living. We present an activity-aware arousal phase modeling approach that incorporates the additional heart rate (AHR) algorithm to estimate arousal onsets (activations) in the presence of physical activity (PA). In particular, our method filters spurious PA-induced activations from AHR activations, e.g., caused by changes in body posture, using activity primitive patterns and their distributions. Furthermore, our approach includes algorithms for estimating arousal duration and intensity, which are key to arousal assessment. We analyzed the modeling procedure in a participant study with 180 h of unconstrained daily life recordings using a multimodal wearable system comprising two acceleration sensors, a heart rate monitor, and a belt computer. We show how participants' sensor-based arousal phase estimations can be evaluated in relation to daily activity and self-report information. For example, participant-specific arousal was frequently estimated during conversations and yielded highest intensities during office work. We believe that our activity-aware arousal modeling can be used to investigate personal arousal characteristics and introduce novel options for studying human behavior in daily living.

Millions of people run. Movement scientists investigate the relationship of running kinematics to fatigue, injury, or running economy mainly using optical motion capture. It was found that running kinematics are highly individual and often cannot be summarized by single variables. We thus present a data-driven analysis of running technique using wearable technology, combining statistical features and machine learning techniques, which allows to identify non-linear, complex relationships. Wearable technology enables running kinematic analysis to a broad mass in unconstrained environments. 20 runners wore 12 sensor units during two experiments: an all out test and a fatiguing run. We used a Support Vector Machine (SVM) to distinguish skill level groups and achieved an accuracy of 76.92% with an acceleration sensor on the upper body. Sensor positions were ranked according to the movement change with fatigue using a feature selection. This ranking was consistent with visual annotations of a movement scientist. We propose a quantitative measure of movement change using a principal component analysis (PCA) and found an average correlation of 0.8369 for all runners with their perceived rating of fatigue.

Previous work on the recognition of human movement patterns has mainly focused on movements of individuals. This paper addresses the joint identification of the indoor movement of multiple persons forming a cohesive whole - specifically a flock - with clustering approaches operating on features derived from multiple sensor modalities of modern smartphones. Automatic detection of flocks has several important applications, including evacuation management and socially aware computing. The novelty of this paper is, firstly, to use data fusion techniques to combine several sensor modalities (WiFi, accelerometer and compass) to improve recognition accuracy over previous unimodal approaches. Secondly, improve the recognition of flocks using hierarchical clustering. We use a dataset comprising 16 subjects forming one to four flocks walking in a building on single and multiple floors. With the best settings, we achieve a F-score accuracy of up to 87 percent an improvement of up to twelve percent points over existing approaches.

Fingerprinting-based localization methods relying on WiFi and GSM information provide sufficient localization accuracy for many mobile phone applications. Most of the existing approaches require a training set consisting of geo-referenced fingerprints to build a reference database. We propose a collaborative, semi-supervised WiFi+GSM fingerprinting method where only a small fraction of all fingerprints needs to be geo-referenced. Our approach enables indexing of areas in the absence of GPS reception as often found in urban spaces and indoors without manual labeling of fingerprints. The method takes advantage of the characteristic that the similarity of two fingerprints correlates to the distance between their corresponding location. By applying multidimensional scaling, a topology estimation is generated and with the help of a small set of geo-referenced fingerprints anchored to physical locations. An evaluation with an urban-scale data set shows that we can locate a mobile device with a median error of 30m. While normally all fingerprints of the training set need to be geo-referenced, with our method, only 8% require geo-referencing. We further show that the localization error decreases as new fingerprints are added and converges to an accuracy comparable to related work.

Indoor localization at minimal deployment effort and with low costs is relevant for many ambient intelligence and mobile computing applications. This paper presents ActionSLAM, a novel approach to Simultaneous Localization And Mapping (SLAM) for pedestrian indoor tracking that makes use of body-mounted sensors. ActionSLAM iteratively builds a map of the environment and localizes the user within this map. A foot-mounted Inertial Measurement Unit (IMU) keeps track of the user's path, while observations of location-related actions (e.g. door-opening or sitting on a chair) are used to compensate for drift error accumulation in a particle filter framework. Location-related actions are recognizable from body-mounted IMUs that are often used in ambient-assisted living scenarios for context awareness. Thus localization relies only on on-body sensing and requires no ambient infrastructure such as Wi-Fi access points or radio beacons. We characterize ActionSLAM on a dataset of 1.69km walking in three rooms and involving 241 location-related actions. For the experimental dataset, the algorithm robustly tracked the subject with mean error of 1.2m. The simultaneously built map reflects the building layout and positions landmarks with a mean error of 0.5m. These results were achieved with a simulated action recognition system consisting of an IMU attached to the wrist of a user and a smartphone in his pocket. We found that employing more complex action recognition is not beneficial for ActionSLAM performance. Our findings are supported by evaluations in synthetic environments through simulation of IMU signals for walks in typical home scenarios.

Template matching methods using Dynamic Time Warping (DTW) have been used recently for online gesture recognition from body-worn motion sensors. However, DTW has been shown sensitive under the strong presence of noise in time series. In sensor readings, labeling temporal boundaries of daily gestures precisely is rarely achievable as they are often intertwined. Moreover, the variation in daily gesture execution always exists. Therefore, here we propose two template matching methods utilizing the Longest Common Subsequence (LCSS) to improve robustness against such noise for online gesture recognition. Segmented LCSS utilizes a sliding window to define the unknown boundaries of gestures in the continuous coming sensor readings and detects efficiently a possibly shorter gesture within it. WarpingLCSS is our novel variant of LCSS to determine occurrences of gestures without segmenting data and performs one order of magnitude faster than the Segmented LCSS. The WarpingLCSS requires low-resource settings to process new arriving samples, thus it is suitable for real-time gesture recognition implemented directly on the small wearable devices. We compare our methods with the existing template matching methods based on Dynamic Time Warping (DTW) on two real-world gesture datasets from arm-worn accelerometer data. The results demonstrate that the LCSS approaches outperform the existing template matching approaches (about 12% in accuracy) in the dataset that suffers from boundary noise and execution variation.

In Pervasive Computing research, substantial work has been directed towards radio-based sensing of human movement patterns. This research has, however, mainly been focused on movements of individuals. This paper addresses the joint identification of the movement indoors of multiple persons forming a cohesive whole - specifically flocks - with clustering approaches operating on three different feature sets derived from WiFi signals which are comparatively analysed. Automatic detection of flocks has several important applications, including social and psychological sensing and emergency research studies. We use a dataset comprising 16 subjects forming one to four flocks walking in a building on single and multiple floors. For the detection of flocks we achieved an average F-measure accuracy of up to 85 percent. We report on the advantages and drawbacks of the three different types of feature sets considering their suitability for use “in the wild” or in well-defined environments.

Wearable gesture recognition enables context aware applications and unobtrusive HCI. It is realized by applying machine learning techniques to data from on-body sensor nodes. We present an gesture recognition system minimizing power while maintaining a run-time application defined performance target through dynamic sensor selection. Compared to the non managed approach optimized for recognition accuracy (95% accuracy), our technique can extend network lifetime by 4 times with accuracy >90% and by 9 times with accuracy >70%. We characterize the approach and outline its applicability to other scenarios.

In this paper, we investigate monitoring of kinematic changes evoked by fatigue in running using wearable technology. Movement data were recorded with ETHOS devices. ETHOS is the ETH Orientation Sensor, a customized inertial measurement unit for unconstrained monitoring of human movement. We perform two real-world experiments, in which 21 runners of different skill levels participated. The real-world experiments capture two exhausting 45-min runs: one on a treadmill and one on a conventional outdoor track. We describe and evaluate algorithms to extract kinematic parameters from the sensor data. We identified parameters that change with fatigue for all runners, ones that change for runners of distinct skill levels, and ones that are dependent on an individual's running technique. Overall, we found that observations from treadmill running are not always generalizable to outdoor running. We, thus, argue for using wearable technology to provide athletes and trainers with continuous, quantitative objective measurements of running technique. These could be used to further gain insight into the complex relationship of running kinematics, injury risk, fatigue, and running economy.

We present a bilateral vibrotactile feedback system for accurate lateralization of target angles in the complete 360 degree-range. We envision integrating this system into context-aware hearing instruments (HIs) or cochlear implants (CIs) to support users that experience lateralization difficulties. As a foundation for this it is vital to investigate which kind of feedback and vibration patterns are optimal to provide support for lateralization. Our system enables to evaluate and compare different encoding schemes with respect to resolution, reaction time, intuitiveness and user dependency. The system supports bilateral vibrotactile feedback to reflect integration into HIs or CIs worn at both ears and implemented two approaches: Quantized Absolute Heading (QAH) and Continuous Guidance Feedback (CGF). We provide a detailed description of our hardware that was designed to be also applicable for generic vibrotactile feedback applications.

This paper describes the Ready-to-Live project carried out at the ETH Zurich, Electronics Laboratory in collaboration with Swiss Textile College (STF) during the 2010 spring semester. The goal of the project was to provide an interdisciplinary collaboration for technical and fashion students, and to present the final result in a real fashion show. In this paper, we show how we integrated wearable sensors into fashionable clothes in order to express different feelings and emotions. We explain first the technical implementation and then we present the design aspects of the final outfits.

Injuries in running are often provoked by fatigue or improper technique, which are both reflected in the runner's kinematics. State of the art research on kinematics in sports is using optical motion capture systems that are inaccessible to most athletes. This paper demonstrates the potential of wearable sensors for runners' kinematics analysis. We present a user study including 21 subjects of different running experience that performed an exhausting run on a conventional outside track wearing ETHOS units. For performance level assessment, training assistance, and fatigue monitoring we extracted the foot contact duration, the foot strike type, and the heel lift as kinematic parameters. A questionnaire revealed that subjects perceived the sensors as comfortable to wear and would use them on a regular basis. We concluded that wearable sensors provide a valuable tool for runners, from beginners to experts, for running technique assessment.

The field of research on activity recognition is relatively young compared to others, like computer vision. In more mature fields, algorithms are usually tested on standardized, reference datasets. This way, algorithms coming from different groups can be tested in a fair manner, which accelerates the process of developing new knowledge. Collecting a reference dataset under realistic settings for activity recognition poses many challenges due to the large amount of sensors and sensor modalities which are needed to provide a sufficiently complete playground. We here report on some lessons learned while collecting such a reference dataset with a heterogeneous setup. We argue for the importance of a few principles to obtain a clean dataset, starting from the sampling and acquisition, down to the synchronization and labeling of the data.

Human activity recognition is a thriving research field. There are lots of studies in different sub-areas of activity recognition proposing different methods. However, unlike other applications, there is lack of established benchmarking problems for activity recognition. Typically, each research group tests and reports the performance of their algorithms on their own datasets using experimental setups specially conceived for that specific purpose. In this work, we introduce a versatile human activity dataset conceived to fill that void. We illustrate its use by presenting comparative results of different classification techniques, and discuss about several metrics that can be used to assess their performance. Being an initial benchmarking, we expect that the possibility to replicate and outperform the presented results will contribute to further advances in state-of-the-art methods.

In this work, we investigate eye movement analysis as a new sensing modality for activity recognition. Eye movement data were recorded using an electrooculography (EOG) system. We first describe and evaluate algorithms for detecting three eye movement characteristics from EOG signals-saccades, fixations, and blinks-and propose a method for assessing repetitive patterns of eye movements. We then devise 90 different features based on these characteristics and select a subset of them using minimum redundancy maximum relevance (mRMR) feature selection. We validate the method using an eight participant study in an office environment using an example set of five activity classes: copying a text, reading a printed paper, taking handwritten notes, watching a video, and browsing the Web. We also include periods with no specific activity (the NULL class). Using a support vector machine (SVM) classifier and person-independent (leave-one-person-out) training, we obtain an average precision of 76.1 percent and recall of 70.5 percent over all classes and participants. The work demonstrates the promise of eye-based activity recognition (EAR) and opens up discussion on the wider applicability of EAR to other activities that are difficult, or even impossible, to detect using common sensing modalities.

We present an educational and research kit to support hands-on teaching and experience of real-time activity or gesture recognition from on-body sensors. The kit is comprised of: wireless wearable sensor nodes for motion and ECG sensing; software infrastructure for synchronized data acquisition from multiple sensors, data visualization, signal alignment, and synchronized signal/video exploration; algorithm demonstration and education software with a hidden Markov model-based activity recognition system and a low-latency gesture recognition for a platform game; support hardware for the annotation of user activities from a wireless keypad, and for prototyping of other wireless sensor nodes. All hardware and software is open-source.

Non-stationary data distributions are a challenge in activity recognition from body worn motion sensors. Classifier models have to be adapted online to maintain a high recognition performance. Typical approaches for online learning are either unsupervised and potentially unstable, or require ground truth information which may be expensive to obtain. As an alternative we propose a teacher signal that can be provided by the user in a minimally obtrusive way. It indicates if the predicted activity for a feature vector is correct or wrong. To exploit this information we propose a novel incremental online learning strategy to adapt a k-nearest-neighbor classifier from instances that are indicated to be correctly or wrongly classified. We characterize our approach on an artificial dataset with abrupt distribution change that simulates a new user of an activity recognition system. The adapted classifier reaches the same accuracy as a classifier trained specifically for the new data distribution. The learning based on the provided correct - error signal also results in a faster learning speed compared to online learning from ground truth. We validate our approach on a real world gesture recognition dataset. The adapted classifiers achieve an accuracy of 78.6% compared to the subject independent baseline of 68.3%.

We present an unsupervised speaker identification system for personal annotations of conversations and meetings. The system dynamically learns new speakers and recognizes already known speakers using one audio channel and speech-independent modeling. Multiple personal systems could collaborate in robust unsupervised speaker identification and online learning. The system was optimized for real-time operation on a DSP system that can be worn during daily activities. The system was evaluated on the freely available 24-speaker Augmented Multiparty Interaction dataset. For 5 s recognition time, the system achieves 81% recognition rate. Collaboration between four identification systems resulted in a performance increase of up to 17%, however even two collaborating systems yield an performance improvement. A prototypical wearable DSP implementation could continuously operate for more than 8 hours from a 4.1 Ah battery.

The inferred cost of work-related stress call for early prevention strategies. In this, we see a new opportunity for affective and pervasive computing by detecting early warning signs. This paper goes one step toward this goal. A collective of 33 subjects underwent a laboratory stress intervention, while a set of physiological signals was collected. In this paper, we investigate whether affective information related to stress can be found in the posture channel during office work. Following more recent work in this field, we directly associate features that are derived from the pressure distribution on a chair with affective states. We found that nervous subjects reveal higher variance of movements under stress. Furthermore, we show that a person-independent discrimination of stress from cognitive load is feasible when using pressure data only. A supervised variant of a self-organizing map, which is able to adapt to different patterns of stress responses, reaches an overall accuracy of 73.75% with unknown subjects.

In this paper, we present a wearable assistant for Parkinson's disease (PD) patients with the freezing of gait (FOG) symptom. This wearable system uses on-body acceleration sensors to measure the patients' movements. It automatically detects FOG by analyzing frequency components inherent in these movements. When FOG is detected, the assistant provides a rhythmic auditory signal that stimulates the patient to resume walking. Ten PD patients tested the system while performing several walking tasks in the laboratory. More than 8 h of data were recorded. Eight patients experienced FOG during the study, and 237 FOG events were identified by professional physiotherapists in a post hoc video analysis. Our wearable assistant was able to provide online assistive feedback for PD patients when they experienced FOG. The system detected FOG events online with a sensitivity of 73.1% and a specificity of 81.6%. The majority of patients indicated that the context-aware automatic cueing was beneficial to them. Finally, we characterize the system performance with respect to the walking style, the sensor placement, and the dominant algorithm parameters.

A fundamental challenge limiting information quality obtained from smart sensing garments is the influence of textile movement relative to limbs. We present and validate a comprehensive modeling and simulation framework to predict recognition performance in casual loose-fitting garments. A statistical posture and wrinkle-modeling approach is introduced to simulate sensor orientation errors pertained to local garment wrinkles. A metric was derived to assess fitting, the body-garment mobility. We validated our approach by analyzing simulations of shoulder and elbow rehabilitation postures with respect to experimental data using actual casual garments. Results confirmed congruent performance trends with estimation errors below 4% for all study participants. Our approach allows to estimate the impact of fitting before implementing a garment and performing evaluation studies with it. These simulations revealed critical design parameters for garment prototyping, related to performed body posture, utilized sensing modalities, and garment fitting. We concluded that our modeling approach can substantially expedite design and development of smart garments through early-stage performance analysis.

The inferred cost of work-related stress call for prevention strategies that aim at detecting early warning signs at the workplace. This paper goes one step towards the goal of developing a personal health system for detecting stress. We analyze the discriminative power of electrodermal activity (EDA) in distinguishing stress from cognitive load in an office environment. A collective of 33 subjects underwent a laboratory intervention that included mild cognitive load and two stress factors, which are relevant at the workplace: mental stress induced by solving arithmetic problems under time pressure and psychosocial stress induced by social-evaluative threat. During the experiments, a wearable device was used to monitor the EDA as a measure of the individual stress reaction. Analysis of the data showed that the distributions of the EDA peak height and the instantaneous peak rate carry information about the stress level of a person. Six classifiers were investigated regarding their ability to discriminate cognitive load from stress. A maximum accuracy of 82.8% was achieved for discriminating stress from cognitive load. This would allow keeping track of stressful phases during a working day by using a wearable EDA device.

In this paper we introduce the concept of a wearable assistant for swimmer, called SwimMaster. The SwimMaster consists of acceleration sensors with micro-controllers and feedback interface modules that swimmer wear while swimming. With four different evaluation studies and a total of 22 subjects we demonstrate the functionality and power of the SwimMaster system. We show how a wide range of swim parameters can be monitored and used for a continuous swim performance evaluation. These parameters include the time per lane, the swimming velocity and the number of strokes per lane. Also swim style specific factors like the body balance and the body rotation are extracted. Finally three feedback modalities are tested and evaluated. With these means we show the ability of the SwimMaster to assist a swimmer in achieving the desired exercise goals by constantly monitoring his/her swim performance and providing the necessary feedback to achieve the desired workout goals.

In this work we investigate eye movement analysis as a new modality for recognising human activity. We devise 90 different features based on the main eye movement characteristics: saccades, fixations and blinks. The features are derived from eye movement data recorded using a wearable electrooculographic (EOG) system. We describe a recognition methodology that combines minimum redundancy maximum relevance feature selection (mRMR) with a support vector machine (SVM) classifier. We validate the method in an eight participant study in an office environment using five activity classes: copying a text, reading a printed paper, taking hand-written notes, watching a video and browsing the web. In addition, we include periods with no specific activity. Using a person-independent (leave-one-out) training scheme, we obtain an average precision of 76.1% and recall of 70.5% over all classes and participants. We discuss the most relevant features and show that eye movement analysis is a rich and thus promising modality for activity recognition.

This paper introduces a sensing garment to support posture coaching in children. The system measures back bending postures using acceleration sensors embedded in the garment. We present a sensing garment architecture and the evaluation of garments of different sizes in a study with 21 children. A vision-based reference system was used to evaluate sensor positions and measurement accuracy for 54 back bending postures and related head positions. Then, we asked eight physiotherapists to rate the children's back postures in this study. Ratings of experts correlated significantly with the back bending measurements obtained from the garment. The garment enables an objective assessment of back postures and could form the basis of a system that provides coaching feedback to improve postural control in children.

Opportunistic sensing allows to efficiently collect information about the physical world and the persons behaving in it. This may mainstream human context and activity recognition in wearable and pervasive computing by removing requirements for a specific deployed infrastructure. In this paper we introduce the newly started European research project OPPORTUNITY within which we develop mobile opportunistic activity and context recognition systems. We outline the project's objective, the approach we follow along opportunistic sensing, data processing and interpretation, and autonomous adaptation and evolution to environmental and user changes, and we outline preliminary results.

Context processing in Body Area Networks (BANs) faces unique challenges due to the user and node mobility, the need of real-time adaptation to the dynamic topological and contextual changes, and heterogeneous processing capabilities and energy constraints present on the available devices. This paper proposes a service-oriented framework for the execution of context recognition algorithms. We describe and theoretically analyze the performance of the main framework components, including the sensor network organization, service discovery, service graph construction, service distribution and mapping. The theoretical results are followed by the simulation of the proposed framework as a whole, showing the overall cost of dynamically distributing applications on the network.

In this work we describe the design, implementation and evaluation of a novel eye tracker for context-awareness and mobile HCI applications. In contrast to common systems using video cameras, this compact device relies on Electrooculography (EOG). It consists of goggles with dry electrodes integrated into the frame and a small pocket-worn component with a DSP for real-time EOG signal processing. The device is intended for wearable and standalone use: It can store data locally for long-term recordings or stream processed EOG signals to a remote device over Bluetooth. We describe how eye gestures can be efficiently recognised from EOG signals for HCI purposes. In an experiment conducted with 11 subjects playing a computer game we show that 8 eye gestures of varying complexity can be continuously recognised with equal performance to a state-of-the-art video-based system. Physical activity leads to artefacts in the EOG signal. We describe how these artefacts can be removed using an adaptive filtering scheme and characterise this approach on a 5-subject dataset. In addition to explicit eye movements for HCI, we discuss how the analysis of unconscious eye movements may eventually allow to deduce information on user activity and context not available with current sensing modalities.

Chewing is an essential part of food intake. The analysis and detection of food patterns is an important component of an automatic dietary monitoring system. However chewing is a time-variable process depending on food properties. We present an automated methodology to extract sub-sequences of similar chews from chewing sound recordings. The approach is based on a chew-accurate segmentation of the sound signal, a multi-objective evolutionary search for temporal partitions in the sequence using NSGA-II and a validation of the best solution by classification. We evaluate the method on chewing sound recordings from a four participant study, eating foods with different rheological properties. The proposed methodology allows to determine the most appropriate partitioning of the sequences and extract relevant sound features at the same time. Potato chips and chocolate showed a two-phase structure, for lasagne and apples a single-phase structure was derived. The results led to the hypothesis that a sequential structure can be found in chewing sounds from brittle or rigid foods.

In order to provide relevant information to mobile users, such as workers engaging in the manual tasks of maintenance and assembly, a wearable computer requires information about the user's specific activities. This work focuses on the recognition of activities that are characterized by a hand motion and an accompanying sound. Suitable activities can be found in assembly and maintenance work. Here, we provide an initial exploration into the problem domain of continuous activity recognition using on-body sensing. We use a mock "wood workshop" assembly task to ground our investigation. We describe a method for the continuous recognition of activities (sawing, hammering, filing, drilling, grinding, sanding, opening a drawer, tightening a vise, and turning a screwdriver) using microphones and three-axis accelerometers mounted at two positions on the user's arms. Potentially "interesting" activities are segmented from continuous streams of data using an analysis of the sound intensity detected at the two different locations. Activity classification is then performed on these detected segments using linear discriminant analysis (LDA) on the sound channel and hidden Markov models (HMMs) on the acceleration data. Four different methods at classifier fusion are compared for improving these classifications. Using user-dependent training, we obtain continuous average recall and precision rates (for positive activities) of 78 percent and 74 percent, respectively. Using user-independent training (leave-one-out across five users), we obtain recall rates of 66 percent and precision rates of 63 percent. In isolation, these activities were recognized with accuracies of 98 percent, 87 percent, and 95 percent for the user-dependent, user-independent, and user-adapted cases, respectively.

We describe our initial efforts to learn high-level human behaviors from low-level gestures observed using on-body sensors. Such an activity discovery system could be used to index captured journals of a person's life automatically. In a medical context, an annotated journal could assist therapists in helping to describe and treat symptoms characteristic to behavioral syndromes such as autism. We review our current work on user-independent activity recognition from continuous data where we identify "interesting" user gestures through a combination of acceleration and audio sensors placed on the user's wrists and elbows. We examine an algorithm that can take advantage of such a sensor framework to automatically discover and label recurring behaviors, and we suggest future work where correlations of these low-level gestures may indicate higher-level activities

Abstract This paper presents an extensive investigations to characterize antennas for small UWB mobile terminals. Two small planar UWB antennas (slot antenna and printed monopole antenna) were designed, using a full‐wave electromagnetic finite difference time domain (FDTD) method. The UWB slot antenna has a size of 32 × 29 mm, the monopole antenna size is 20 × 36.75 mm. Both antennas were characterized usingtrueUWB parameters, such as antenna transfer functions, fidelity, and pulse energy spread. We have investigated angular‐dependence of antenna characteristics over the entire radiation sphere. Spatially averaged transfer functions were introduced to disclose antenna characteristics for different solid angles in the radiation sphere. We have also shown the difference in transient characteristics of antenna in transmitting (Tx) and receiving (Rx) mode. Finally, we have investigated the Tx/Rx antenna system, where any relative orientation between Tx and Rx antenna was assumed to be equally probable. Copyright © 2005 John Wiley & Sons, Ltd.

Wearable computing has recently gained much popularity as an ambitious vision for future personalized mobile systems. Its aim is intelligent, environment aware systems unobtrusively embedded into the mobile environments of their users. With the combination of complex processing requirements, the necessity of placing sensors and input/output modules at different locations on the user's body, and stringent limits on size, weight, and battery capacity, the design of such systems is an inherently challenging problem. We demonstrate how systematic design and quantitative analysis can be applied to wearable architectures. We first present a model that allows various factors influencing the design of a wearable system to be incorporated into formal cost metrics. In particular, we show how to consistently incorporate specific wearable factors such as device placement requirements, ergonomics, and dynamic workload profiles into the model. We then discuss how efficient estimation algorithms can be extended and applied to the evaluation of different architectures with respect to our cost metrics. Finally, we discuss quantitative results from a proof-of-concept case study showing the trade offs between different architectures for a given wearable scenario. Summarized, we demonstrate how the description and the design of wearable systems can be put on a systematic, formal basis allowing us to treat them similar as conventional embedded systems.

This paper describes an advanced care and alert portable telemedical monitor (AMON), a wearable medical monitoring and alert system targeting high-risk cardiac/respiratory patients. The system includes continuous collection and evaluation of multiple vital signs, intelligent multiparameter medical emergency detection, and a cellular connection to a medical center. By integrating the whole system in an unobtrusive, wrist-worn enclosure and applying aggressive low-power design techniques, continuous long-term monitoring can be performed without interfering with the patients' everyday activities and without restricting their mobility. In the first two and a half years of this EU IST sponsored project, the AMON consortium has designed, implemented, and tested the described wrist-worn device, a communication link, and a comprehensive medical center software package. The performance of the system has been validated by a medical study with a set of 33 subjects. The paper describes the main concepts behind the AMON system and presents details of the individual subsystems and solutions as well as the results of the medical validation.

The invention of the Jacquard weaving machine led to the concept of a stored "program" and "mechanized" binary information processing. This development served as the inspiration for C. Babbage's analytical engine-the precursor to the modern-day computer. Today, more than 200 years later, the link between textiles and computing is more realistic than ever. In this paper, we look at the synergistic relationship between textiles and computing and identify the need for their "integration" using tools provided by an emerging new field of research that combines the strengths and capabilities of electronics and textiles into one: electronic textiles, or e-textiles. E-textiles, also called smart fabrics, have not only "wearable" capabilities like any other garment, but also have local monitoring and computation, as well as wireless communication capabilities. Sensors and simple computational elements are embedded in e-textiles, as well as built into yarns, with the goal of gathering sensitive information, monitoring vital statistics, and sending them remotely (possibly over a wireless channel) for further processing. The paper provides an overview of existing efforts and associated challenges in this area, while describing possible venues and opportunities for future research.

In this work, the correlation-based image localization problem is targeted toward implementation for real-time operation on high-resolution images. The main contribution resides in a global approach to the two-dimensional correlation problem which concurrently considers the algorithm, the system architecture, and its implementation. Analyzing and studying the alternatives concerning algorithm specification, VLSI architecture, and system architecture at different levels of the design phase, a scalable multiprocessor system, based on multiple instances of identical modules, is derived which achieves a real-time image correlation performance exceeding that of currently available solutions by an order of magnitude concerning frame-rate and input image resolution. As a result, the relevance of image correlation traditionally limited to the image processing domain is now expanded into applied machine vision applications. Addressing packaging and reusability aspects, an MCM implementation of the relevant system components is presented.

Passive components integrated into a high-density substrate can be a tolerable way to overcome the size and manufacturing limits of SMD passives mounted onto the system board. Still, this technology is perceived as being "too risky" and not cost effective. In this paper we propose a "passives optimized" solution combining the advantages from both SMD and integrated technology and avoiding the respective drawbacks. Exemplified by a GPS receiver front end, we present a methodology to assess the possible benefits when using the mixed technology.

The inherent potentials of Si technology are limited by the low interaction with packaging. Co-design as the symbiosis between the ICs and appropriate high-density packaging offers lower RC line delay, improved SSN and lower costs compared to single-chip approaches. The distribution of the system functionality between ICs and the packaging level opens up new vistas in future electronic design and system architecture.

In this paper a dedicated multiprocessor architecture for a real time implementation of the normalized cross correlation function (NCCF) on images up to 1024x1024 pixels is presented. The computational requirements are dramatically reduced by calculating this algorithm in the frequency domain. In contrast to a standard implementation of the NCCF which inherently imposes rectangular templates, the proposed enhanced method also allows to search for free-form templates which even may include holes. The computation in the frequency domain is based on a single program multiple data (SPMD) architecture which includes a dedicated ASIC for the computation of the 1D complex FFT. Besides this specific part of the system, the image pre- and post- processing tasks are supported by general purpose DSP's. A system consisting of 4 ASIC's and 2 Sharc DSP's is able to compute the enhanced NCCF of a free form template on images of 1024x1024 pixels within 134 ms (8 frames/s).

A compact Monte-Carlo-based universal procedure is presented that allows the estimation and qualification of geometrical arrangements and matching strategies, especially in monolithic devices. On the basis of a simple circuit example, the general structure of the main algorithm is explained, and some extensions are discussed. Experimental results on different configurations illustrate the broad application range.>