Depression screening remains challenging due to reliance on subjective assessments and limited accessibility of mental health services. This paper presents MindCare, an integrated mobile platform combining standardized questionnaires, AI-powered therapeutic interactions, emotional journaling, and EEG-based neurophysiological assessment. The system implements a novel visual stimulation protocol using 60 emotionally evocative images from the Open Affective Standardized Image Set (OASIS) to elicit measurable neural responses. User evaluation with 7 participants demonstrated high satisfaction across all features. EEG analysis of 4 participants during emotional stimulation revealed strong correlations between frontal channel neural features and PHQ-9 depression scores. Machine learning classification achieved 97.9 % accuracy in distinguishing depression status using segment-based analysis of 240 stimulus-response pairs. The integration of objective neurophysiological markers with subjective assessment tools demonstrates significant potential for enhancing digital mental health screening capabilities.

Speech recognition for individuals with impairments remains a significant challenge due to atypical speech patterns thatconfound traditional acoustic-only models. This study introduces NeuroSpeech, a novel multimodal framework that integrateselectroencephalography (EEG) with acoustic features to improve recognition accuracy, robustness, and efficiency. A large-scale random search identified optimal EEG encoder configurations and feature extraction parameters, with window size and overlap ($p < 0.001$) emerging as critical factors. Explainable AI (XAI) methods, specifically SHAP, provided insights into model decision-making, supporting interpretability and clinical translation. Evaluations were conducted on two publicly available datasets: Spanish commands and vowels (UNLP-CONICET) and English phonemes and words (KaraOne). Under clean conditions, NeuroSpeech achieved near-perfect accuracy ($F1 = 0.986$ on Spanish; 0.837 on English), while in noisy conditions (SNR = 0.5) it maintained strong performance ($F1 = 0.92$ and 0.70), demonstrating EEG's role as a noise-robust complementary signal. In contrast, Whisper, a state-of-the-art ASR model, showed severe degradation under noise (e.g., $F1$ dropping from 0.81 to 0.46). Finally, complexity analysis showed that NeuroSpeech is lightweight (1-30M parameters) with inference latency of 10-18ms/sample (RTF $< 1$ on CPU and GPU), enabling near-real-time deployment. These results demonstrate NeuroSpeech's significant potential to leverage neural information to augment speech that is compromised, offering a promising advancement for assistive technologies and improved communication for individuals with speech disorders.

IMPORTANCE: Due to insufficient smoking cessation apps for persons living with HIV, our study focused on designing and testing the Sense2Quit app, a patient-facing mHealth tool which integrated visualizations of patient information, specifically smoking use. OBJECTIVES: The purpose of this paper is to detail rigorous human-centered design methods to develop and refine visualizations of smoking data and the contents and user interface of the Sense2Quit app. The Sense2Quit app was created to support tobacco cessation and relapse prevention for people living with HIV. MATERIALS AND METHODS: Twenty people living with HIV who are current or former smokers and 5 informaticians trained in human-computer interaction participated in 5 rounds of usability testing. Participants tested the Sense2Quit app with use cases and provided feedback and then completed a survey. RESULTS: Visualization of smoking behaviors was refined through each round of usability testing. Further, additional features such as daily tips, games, and a homescreen were added to improve the usability of the app. A total of 66 changes were made to the Sense2Quit app based on end-user and expert recommendations. DISCUSSION: While many themes overlapped between usability testing with end-users and heuristic evaluations, there were also discrepancies. End-users and experts approached the app evaluation from different perspectives which ultimately allowed us to fill knowledge gaps and make improvements to the app. CONCLUSION: Findings from our study illustrate the best practices for usability testing for development and refinement of an mHealth-delivered consumer informatics tool for improving tobacco cessation yet further research is needed to fully evaluate how tools informed by target user needs improve health outcomes.

As automatic speech recognition evolves, deployment of the voice user interface (VUI) has boomingly expanded. Especially since the COVID-19 pandemic, the VUI has gained more attention in online communication owing to its non-contact property. However, the VUI struggles to be applied in public scenes due to the degradation of received audio signals caused by various ambient noises. In this article, we propose Wavoice , the first noise-resistant multi-modal speech recognition system that fuses two distinct voices sensing modalities (i.e., millimeter-wave signals and audio signals from a microphone) together. One key contribution is to model the inherent correlation between millimeter-wave and audio signals. Based on it, Wavoice facilitates the real-time noise-resistant voice activity detection and user targeting from multiple speakers. Additionally, we elaborate on two novel modules for multi-modal fusion embedded into the neural network, leading to accurate speech recognition. Extensive experiments prove the effectiveness of Wavoice under adverse conditions—that is, the character recognition error rate below 1% in a range of 7 m. In terms of robustness and accuracy, Wavoice considerably outperforms existing audio-only speech recognition methods with lower character error and word error rates.

As the demand for precision medicine rapidly grows, companion diagnostics is proposed to monitor and evaluate therapeutic effects for adjusting medicine plans in time. Although a set of clinical companion diagnostics tools (e.g., polymerase chain reaction) have been investigated, they are expensive and only accessible in a lab environment, which hinders the promotion to broader patients. In light of this situation, we take the first steps towards developing a real-world companion diagnostic tool by leveraging mobile technology. In this paper, we present TherapyPal, a privacy-preserving medicine effectiveness computational framework by harnessing semantic hashing-based digital symptomatic phenotyping. Specifically, sensor data captured from daily-life activities is first transformed into spectrograms. Then, we develop a hashing learning network to extract privacy-masked symptomatic phenotypes on smartphones. Afterward, symptomatic hashes at different medicine states are fed to a contrastive learning network in the cloud for treatment effectiveness detection. To evaluate the performance, we conduct a clinical study among 65 Parkinson's disease (PD) patients under dopaminergic drug treatment. The results show that TherapyPal can achieve around 84.1% medicine effectiveness detection accuracy among patients and above 0.925 privacy-masked scores for protecting each private attribute, which validates the reliability and security of TherapyPal to be used as a real-world companion diagnostics tool.

With the increasing deployment of voice-controlled devices in homes and enterprises, there is an urgent demand for voice identification to prevent unauthorized access to sensitive information and property loss. However, due to the broadcast nature of sound wave, a voice-only system is vulnerable to adverse conditions and malicious attacks. We observe that the cooperation of millimeter waves (mmWave) and voice signals can significantly improve the effectiveness and security of user identification. Based on the properties, we propose a multi-modal user identification system (named WavoID) by fusing the uniqueness of mmWave-sensed vocal vibration and mic-recorded voice of users. To estimate fine-grained waveforms, WavoID splits signals and adaptively combines useful decomposed signals according to correlative contents in both mmWave and voice. An elaborated anti-spoofing module in WavoID comprising biometric bimodal information defend against attacks. WavoID produces and fuses the response maps of mmWave and voice to improve the representation power of fused features, benefiting accurate identification, even facing adverse circumstances. We evaluate WavoID using commercial sensors on extensive experiments. WavoID has significant performance on user identification with over 98% accuracy on 100 user datasets.

Earpiece mode of smartphones is often used for confidential communication. In this paper, we proposed a remote(>2m) and motion-resilient attack on smartphone earpiece. We developed an end-to-end eavesdropping system mmEve based on a commercial mmWave sensor to recover speech emitted from smartphone earpiece. The rationale of the attack is based on our observation that, soundwaves emitted from the smartphone's earpiece have a strong correlation with reflected mmWaves from the smartphone's rear. However, we find the recovered speech suffers from the sensor's self-noise and smartphone user's motion which limit attack distance to less than 2m, causing limited threats in real world. We modeled the motion interference under mmWave sensing and proposed a motion-resilient solution by optimizing the fitting function on I/Q plane. To achieve a practical attack with reasonable attack distance, we developed a GAN-based denoising scheme to eliminate the noise pattern of the sensor, which boosted the attack range to 6--8m. We evaluated mmEve with extensive experiments and find 23 different models of smartphones manufactured by Samsung, Huawei, etc. can be compromised by the proposed attack.

Monitoring the changes in gait patterns is important to individuals’ health. Gait analysis should be taken as early as possible to prevent gait impairments and improve gait quality. Accurate stride-length estimation and gait rehabilitation activity recognition are fundamental components in gait monitoring, gait analysis, and long-term gait care. This article proposes a novel multimodality deep learning architecture to investigate the applications of stride length (SL) estimation and rehabilitation activity recognition. In order to verify this architecture, we have conducted the data collection and data labeling with our customized wearable sensing system. The sensing system can provide sensor readings from 96 sensors-based pressure array and 3-channels accelerometer and gyroscope. Many experiments with multiple perspective analysis are implemented to evaluate the models’ precision, robustness, and reliability. The multimodality deep learning architecture can map multiple sensor readings to the resulting SL with a mean absolute error of 3.89 cm and accurately detect the gait activity with an accuracy of 97.08%. It correlates the step length estimation and gait activity recognition to fulfill comprehensive long-term gait information statistic. The proposed applications’ implementation enriched our previous gait study and brought insights for clinically relevant wearable gait monitoring and gait analysis.

Smoking cessation is a significant challenge for many people addicted to cigarettes and tobacco. Mobile health-related research into smoking cessation is primarily focused on mobile phone data collection either using self-reporting or sensor monitoring techniques. In the past 5 years with the increased popularity of smartwatch devices, research has been conducted to predict smoking movements associated with smoking behaviors based on accelerometer data analyzed from the internal sensors in a user's smartwatch. Previous smoking detection methods focused on classifying current user smoking behavior. For many users who are trying to quit smoking, this form of detection may be insufficient as the user has already relapsed. In this paper, we present a smoking cessation system utilizing a smartwatch and finger sensor that is capable of detecting pre-smoking activities to discourage users from future smoking behavior. Pre-smoking activities include grabbing a pack of cigarettes or lighting a cigarette and these activities are often immediately succeeded by smoking. Therefore, through accurate detection of pre-smoking activities, we can alert the user before they have relapsed. Our smoking cessation system combines data from a smartwatch for gross accelerometer and gyroscope information and a wearable finger sensor for detailed finger bend-angle information. We compare the results of a smartwatch-only system with a combined smartwatch and finger sensor system to illustrate the accuracy of each system. The combined smartwatch and finger sensor system performed at an 80.6% accuracy for the classification of pre-smoking activities compared to 47.0% accuracy of the smartwatch-only system.

The growing availability of the data collected from smart manufacturing is changing the paradigms of production monitoring and control. The increasing complexity and content of the wafer manufacturing process in addition to the time-varying unexpected disturbances and uncertainties, make it infeasible to do the control process with model-based approaches. As a result, data-driven soft-sensing modeling has become more prevalent in wafer process diagnostics. Recently, deep learning has been utilized in soft sensing system with promising performance on highly nonlinear and dynamic time-series data. Despite its successes in soft-sensing systems, however, the underlying logic of the deep learning framework is hard to understand. In this paper, we propose a deep learning-based model for defective wafer detection using a highly imbalanced dataset. To understand how the proposed model works, the deep visualization approach is applied. Additionally, the model is then fine-tuned guided by the deep visualization. Extensive experiments are performed to validate the effectiveness of the proposed system. The results provide an interpretation of how the model works and an instructive fine-tuning method based on the interpretation.

The campus environments have traditionally revolved around the use of sustainable and practical mobility vehicles such as bicycles, but similar to pedestrians and bicyclists, the students riding smart-scooter are also vulnerable road users and to severe injuries during road accidents. In this paper, we created a “smart android system”. STEADi, for monitoring the Smart scooter riders. The system uses a Wearable Gait Lab for, a wearable underfoot force-sensing intelligent unit, as one of the main components. The purpose of this system is to help students who are new to using smart scooters on campus to avoid injuries and accidents by alerting the rider about unforeseen conditions. The system provides adequate data for path tracking, Potholes Detection system, and human balancing ability for the Smart Scooter riders. After careful selection of training data, we have been able to integrate a pothole detector system that identifies worse road segments as having potholes. The proposed system is evaluated based on four balance tests on different terrain and with different diverse riding experiences related to the Smart Scooters. The system testing showed that it can successfully detect several real potholes in and around the Cleveland area and is successfully able to alert the riders, including the lesser experienced ones while riding on different terrains for the potential road-related threats.

Falls are leading causes of nonfatal injuries in workplaces which lead to substantial injury and economic consequences. To help avoid fall injuries, safety managers usually need to inspect working areas routinely. However, it is difficult for a limited number of safety managers to inspect fall hazards instantly especially in large workplaces. To address this problem, a novel fall hazard identification method is proposed in this paper which makes it possible for all workers to report the potential hazards automatically. This method is based on the fact that people use different gaits to get across different floor surfaces. Through analyzing gait patterns, potential fall hazards could be identified automatically. In this research, Smart Insole, an insole shaped wearable system for gait analysis, was applied to measure gait patterns for fall hazard identification. Slips and trips are the focus of this study since they are two main causes of falls in workplaces. Five effective gait features were extracted to train a Support Vector Machine (SVM) model for recognizing slip hazard, trip hazard, and safe floor surfaces. Experiment results showed that fall hazards could be recognized with high accuracy (98.1%).

Deep learning has been becoming a promising focus in data mining research. With deep learning techniques, researchers can discover deep properties and features of events from quantitative mobile sensor data. However, many data sources are geographically separated and have strict privacy, security, and regulatory constraints. Upon releasing the privacy-sensitive data, these data sources generally no longer physically possess their data and cannot interfere with the way their personal data being used. Therefore, it is necessary to explore distributed data mining architecture which is able to conduct consensus learning based on needs. Accordingly, we propose a distributed deep learning optimized system which contains a cloud server and multiple smartphone devices with computation capabilities and each device is served as a personal mobile data hub for enabling mobile computing while preserving data privacy. The proposed system keeps the private data locally in smartphones, shares trained parameters, and builds a global consensus model. The feasibility and usability of the proposed system are evaluated by three experiments and related discussion. The experimental results show that the proposed distributed deep learning system can reconstruct the behavior of centralized training. We also measure the cumulative network traffic in different scenarios and show that the partial parameter sharing strategy does not only preserve the performance of the trained model but also can reduce network traffic. User data privacy is protected on two levels. First, local private training data do not need to be shared with other people and the user has full control of their personal training data all the time. Second, only a small fraction of trained gradients of the local model are selected for sharing, which further reduces the risk of information leaking.

With the development of the Internet of Things (IoT), wearable technologies have been proposed to measure gait parameters in everyday life. However, since both diseases and activities could influence gait patterns, clinicians cannot use the measured gait parameters for clinical applications without knowing the corresponding activities. To address this problem, a novel gait analysis method—“gait analysis in terms of activities of daily living (ADLs)”—was proposed based on a wearable Smart Insole system. Twenty six gait parameters were extracted to realize a systematic gait analysis. Novel activity recognition algorithms based on characteristics of human gait were proposed to recognize ADLs, including “sitting,” “standing,” “walking,” “running,” “ascend stairs,” and “descend stairs” with high accuracy and low computation load. To evaluate the performance of “gait analysis in terms of ADLs,” an experiment consisting of a sequence of different ADLs was designed to simulate the scenario of everyday life. In the result, gait parameters measured during different activities were automatically highlighted with different colors, which made it easy to see whether the gait pattern change was caused by activities or diseases. Besides, a refined gait analysis could be realized by individually extracting and analyzing the gait parameters of a specific activity. The results indicate that “gait analysis in terms of ADLs” is a feasible method to reach the aim of bringing gait lab to everyday life.

Body balance and perception have been shown to be linked in a number of studies, ranging from plasma membrane calcium ATPase isoform PMCA2 deficiency causing both balance and hearing problems in mice to MS (multiple sclerosis) patients' loss of foot sole sensitivity correlating with compromised balance. This project involved developing a system that tests balance as well as hearing using Augmented Reality (AR) and mobile health (mHealth) technology. Four balance tests are included in the AR application; three are from Smart Balance Master (Limits of Stability, Rhythmic Weight Shift, Sit to Stand), and the fourth is a modified Single Leg Stand with 32 sensory conditions. Hearing is tested within the same application through beeps played sequentially at frequencies of 500 Hertz, 1000 Hertz, 2000 Hertz, 4000 Hertz, and 8000 Hertz for each ear. The first volume level they are played at is less than 10 Hertz and this can be sequentially increases in increments of 10 decibels up to 100 decibels until the player can hear the beep; this volume level is recorded. The application is meant to be used with Wearable Gait Lab (WGL), an underfoot plantar pressure unit. The user wears Microsoft HoloLens (which runs the AR application) and WGL (which records balance data); experimental data is analyzed for a correlation between perception and balance in ten young adults as a proof of model.

With the development of Internet of Things (IoT), indoor localization has been a research focus in recent years. For inertial measurement unit (IMU)-based indoor localization method, zero velocity update (ZUPT) uses the known velocity at stationary epoch as a benchmark to calibrate the velocity drift. However, stationary epoch only takes up 24% of a whole gait cycle time, and the velocity drift at the remaining 76% time is usually estimated according to an assumption that velocity has a linear drift over time, which would introduce errors. In this paper, a two-step velocity calibration method was proposed based on human gait characteristics with Smart Insole: known velocity update (KUPT) and double-foot position calibration (DFPC). KUPT could measure the velocity from heel-strike to toe-off based on the recorded real-time foot angle and the shoe dimensions, which increases the time period when the velocity could be measured from 24% to 62% of a whole gait cycle time. DFPC method could fuse the position information of both feet based on the symmetrical characteristic of human gait to further increase the reliability of the localization results. The statistical result of a 20 times 20-m walking experiment showed that KUPT method was more accurate and reliable than ZUPT method for both feet, and DFPC method could further improve the result of KUPT method. Another experiment about walking in an indoor environment for 91 m showed that the proposed KUPT+DFPC method had an error of about 0.78 m which is acceptable for most IoT applications.

Fine-grained smile analysis is a complicated and challenging process. Understanding other party's smiles is one of the key tasks associated with realizing the implicit messages transmitted by the human. Considering this kind of message transmission is a major feature of human communication, understanding smiling has great potential value to promote the development of humanoid robots and animated software agents. Therefore, a fine-grained smile analysis system is proposed to uncover the hidden smile correlation across subjects. The system incorporates head pose as prior knowledge and employs conditional random forest to detect fiducial points on face. After that, a steady-state probability defined by a succession of Markov random steps is used to indicate the relevance score between smiles across subjects. We demonstrate performance of the proposed system on both constrained and unconstrained face datasets. The experimental results show that the proposed system is able to classify 4 smile levels and uncover hidden smile correlations across subjects successfully.

In this paper, we implement a behavioral biometric-based smartphone authentication technique with online training methods. The most obvious difference between online and traditional classification is that online methods allow model training and data collection to be in progress simultaneously. Therefore, our proposed authentication system is superior when dealing with time series data and its model can be updated to adapt the change of user's habit. To verify the feasibility of online training methods used in behavioral biometric authentication, three online algorithms and four traditional classification algorithms are tested with collected dataset. They all achieved an accuracy over 96%. Two additional experiments are designed to test the stability of this authentication system. The results show that the system has the ability to prevent targeted attack, such as shoulder surfing. Furthermore, it keeps high performance that accuracy greater than 95% when user holds smartphone in different scenarios, sitting and walking. Finally, we conclude that online training methods based behavioral biometric smartphone authentication system is very stable with targeted attack and different unlock scenarios.

In order to prevent the construction injuries effectively, it is essential to fully understand the accident causation in construction. Worker action detection and recognition can be treated as the initial step of further productivity and risk factor analysis. With the development of computer vision and machine learning techniques, monitoring worker activity automatically and continuously using camera becomes feasible and promising. In this paper, we focus on worker activity recognition problem and propose an automate recognition system based on an unconstrained worker activity video dataset, in which both coarse-grained and fine-grained actions coexist. Videos are segmented by graph cuts energy minimization. Neural network technique is integrated with principle component analysis for recognizing workers' activities automatically. Discussion on different scenario settings and comparison to the state-of-the-art method are provided. Experimental results show that the average accuracy outperforms the state-of-the-art results.

The use of secure HTTP calls is a first and critical step toward securing the Android application data when the app interacts with the Internet. However, one of the major causes for the unencrypted communication is app developer's errors or ignorance. Could the paradigm of literally repetitive and ineffective emphasis shift towards emphasis as a mechanism? This paper introduces emphaSSL, a simple, practical and readily-deployable way to harden networking security in Android applications. Our emphaSSL could guide app developer's security development decisions via real-time feedback, informative warnings and suggestions. At its core of emphaSSL, we use a set of rigorous security rules, which are obtained through an in-depth SSL/TLS security analysis based on security requirements engineering techniques. We implement emphaSSL via the PMD and evaluate it against 75 open- source Android applications. Our results show that emphaSSL is effective at detecting security violations in HTTPS calls with a very low false positive rate, around 2%. Furthermore, we identified 164 substantial SSL mistakes in these testing apps, 40% of which are potentially vulnerable to man-in-the-middle attacks. In each of these instances, the vulnerabilities could be quickly resolved with the assistance of our highlighting messages in emphaSSL. Upon notifying developers of our findings in their applications, we received positive responses and interest in this approach.

The ability to continuously monitor respiration rates of patients in homecare or in clinics is an important goal. Past research showed that monitoring patient breathing can lower the associated mortality rates for long-term bedridden patients. Nowadays, in-bed sensors consisting of pressure sensitive arrays are unobtrusive and are suitable for deployment in a wide range of settings. Such systems aim to extract respiratory signals from time-series pressure sequences. However, variance of movements, such as unpredictable extremities activities, affect the quality of the extracted respiratory signals. BreathSens, a high-density pressure sensing system made of e-Textile, profiles the underbody pressure distribution and localizes torso area based on the high-resolution pressure images. With a robust bodyparts localization algorithm, respiratory signals extracted from the localized torso area are insensitive to arbitrary extremities movements. In a study of 12 subjects, BreathSens demonstrated its respiratory monitoring capability with variations of sleep postures, locations, and commonly tilted clinical bed conditions.

Detecting human activity independent of intensity is essential in many applications, primarily in calculating metabolic equivalent rates and extracting human context awareness. Many classifiers that train on an activity at a subset of intensity levels fail to recognize the same activity at other intensity levels. This demonstrates weakness in the underlying classification method. Training a classifier for an activity at every intensity level is also not practical. In this paper, we tackle a novel intensity-independent activity recognition problem where the class labels exhibit large variability, the data are of high dimensionality, and clustering algorithms are necessary. We propose a new robust stochastic approximation framework for enhanced classification of such data. Experiments are reported using two clustering techniques, K-Means and Gaussian Mixture Models. The stochastic approximation algorithm consistently outperforms other well-known classification schemes which validate the use of our proposed clustered data representation. We verify the motivation of our framework in two applications that benefit from intensity-independent activity recognition. The first application shows how our framework can be used to enhance energy expenditure calculations. The second application is a novel exergaming environment aimed at using games to reward physical activity performed throughout the day, to encourage a healthy lifestyle.

Physical rehabilitation is an important process for patients recovering after surgery. In this paper, we propose and develop a framework to monitor on-bed range of motion exercises that allows physical therapists to evaluate patient adherence to set exercise programs. Using a dense pressure sensitive bedsheet, a sequence of pressure maps are produced and analyzed using manifold learning techniques. We compare two methods, Local Linear Embedding and Isomap, to reduce the dimensionality of the pressure map data. Once the image sequences are converted into a low dimensional manifold, the manifolds can be compared to expected prior data for the rehabilitation exercises. Furthermore, a measure to compare the similarity of manifolds is presented along with experimental results for five on-bed rehabilitation exercises. The evaluation of this framework shows that exercise compliance can be tracked accurately according to prescribed treatment programs.

Detecting human activity independent of intensity is essential in many applications, primarily in calculating metabolic equivalent rates (MET) and extracting human context awareness from on-body inertial sensors. Many classifiers that train on an activity at a subset of intensity levels fail to classify the same activity at other intensity levels. This demonstrates weakness in the underlying activity model. Training a classifier for an activity at every intensity level is also not practical. In this paper we tackle a novel intensity-independent activity recognition application where the class labels exhibit large variability, the data is of high dimensionality, and clustering algorithms are necessary. We propose a new robust Stochastic Approximation framework for enhanced classification of such data. Experiments are reported for each dataset using two clustering techniques, K-Means and Gaussian Mixture Models. The Stochastic Approximation algorithm consistently outperforms other well-known classification schemes which validates the use of our proposed clustered data representation.

This paper presents the design of an on-bed rehabilitation exercise monitoring system that utilizes a high density sensor bedsheet to evaluate active range of motion exercises. We propose and develop a novel framework to analyze the progression of pressure image sequences using manifold learning. The image sequences are reduced to a low dimensional subspace that can be measured against expected prior data for each of the rehabilitation exercises. We also present a metric to compare manifold similarities. Our experimental results on five on-bed exercises show that this system can accurately track compliance of patients to prescribed treatment programs. It allows physical therapists to evaluate how well patients adhere to the rehabilitation exercises. The system is convenient to setup, unobtrusive, and can be used for reliable, long term monitoring.

This paper presents the design of an on-bed rehabilitation exercise monitoring system that utilizes a high density sensor bedsheet to evaluate active range of motion exercises. We propose and develop a novel framework to analyze the progression of pressure image sequences using manifold learning. The image sequences are reduced to a low dimensional subspace that can be measured against expected prior data for each of the rehabilitation exercises. We also present a metric to compare manifold similarities. Our experimental results on five on-bed exercises show that this system can accurately track compliance of patients to prescribed treatment programs. It allows physical therapists to evaluate how well patients adhere to the rehabilitation exercises. The system is convenient to setup, unobtrusive, and can be used for reliable, long term monitoring.

Sleep plays a pivotal role in the quality of life, and sleep posture is related to many medical conditions such as sleep apnea. In this paper, we design a dense pressure-sensitive bedsheet for sleep posture monitoring. In contrast to existing techniques, our bedsheet system offers a completely unobtrusive method using comfortable textile sensors. Based on high-resolution pressure distributions from the bedsheet, we develop a novel framework for pressure image analysis to monitor sleep postures, including a set of geometrical features for sleep posture characterization and three sparse classifiers for posture recognition. We run a pilot study and evaluate the performance of our methods with 14 subjects to analyze 6 common postures. The experimental results show that our proposed method enables reliable sleep posture recognition and offers better overall performance than state-of-the-art methods, achieving up to 83.0% precision and 83.2% recall on average.

Sitting posture analysis is critical for daily applications in biomedical, education and healthcare fields. However, it remains unclear how to monitor sitting posture economically and comfortably. To this end, we presented an eTextile device, called eCushion, in this paper, which can analyze the sitting posture of human being accurately and non-invasively. First, we discussed the implementation of eCushion and design challenges of sensing data, such as scale, offset, rotation and crosstalk. Then, several effective techniques have been proposed to improve the recognition rate of sitting posture. Our experimental results show that the recognition rate of our eCushion system could achieve 92% for object-oriented cases and 79% for general cases.

We target body-wearable sensor networks, in which sensor nodes are strategically placed on the human body and the wireless communications are conducted on/off the surface of the body. The results, obtained by performing multiple experiments in outdoor environments, are presented. A single on body transmitter communicates with a single receiver node, which is located on the body or off the body at various distances. Sensor nodes utilized in our experiments are equipped with XBee and XBee Pro wireless modules for on body and off body communications, respectively. The focus of our work is to observe how the Received Signal Strength (RSS) and the Packet Reception Rate (PRR) vary as we change the communication distance and transmission power level. Our experimental results can be used to perform transmission power control with high precision in order not to exceed a certain packet error rate.