While commendable progress has been made in user-centric research on mobile assistive systems for blind and low-vision (BLV) individuals, references that directly inform robot navigation design remain rare. To bridge this gap, we conducted a comprehensive human study involving interviews with 26 guide dog handlers, four white cane users, nine guide dog trainers, and one O&M trainer, along with 15+ hours of observing guide dog-assisted walking. After de-identification, we open-sourced the dataset to promote human-centered development and informed decision-making for assistive systems for BLV people. Building on insights from this formative study, we developed GuideNav, a vision-only, teach-and-repeat navigation system. Inspired by how guide dogs are trained and assist their handlers, GuideNav autonomously repeats a path demonstrated by a sighted person using a robot. Specifically, the system constructs a topological representation of the taught route, integrates visual place recognition with temporal filtering, and employs a relative pose estimator to compute navigation actions-all without relying on costly, heavy, power-hungry sensors such as LiDAR. In field tests, GuideNav consistently achieved kilometer-scale route following across five outdoor environments, maintaining reliability despite noticeable scene variations between teach and repeat runs. A user study with 3 guide dog handlers and 1 guide dog trainer further confirmed the system's feasibility, marking, to our knowledge, the first demonstration of a quadruped mobile system guiding a route in a manner comparable to guide dogs.

Cardiovascular diseases remain the leading cause of death worldwide, and Doppler blood flow indices play a crucial role in their early detection and diagnosis. Existing ultrasound devices, though effective, are costly, bulky, and unsuitable for daily or at-home use. In this work, we introduce Phonotonos, a system that transforms commodity smartphones into portable and accessible tools for through-skin ultrasonic sensing of blood flow velocity. We demonstrate that despite smartphones' inherent limitations—including low ultrasound power, coarse spatial resolution, and lack of beamforming—blood flow velocity waveforms can still be recovered using novel phase-based signal processing, including nonlinearity cancellation and baseline drift removal. We further propose a triple-modality framework that fuses Doppler ultrasound, arterial sound, and IMU for robust artery localization and interfering motion (e.g., involuntary hand motion) rejection. Extensive simulations, phantom experiments, and IRB-approved human studies validate that our proposed system can measure four key Doppler indices (AT, S/D ratio, RI, PI) with accuracy comparable to dedicated medical devices. These results highlight the potential of smartphones to democratize vascular health monitoring and enable continuous cardiovascular screening in everyday environments.

Mild Cognitive Impairment (MCI) affects up to 20% of older adults and often progresses to dementia. While brief cognitive screening tools like the Montreal Cognitive Assessment (MoCA) can aid in early detection and monitoring, their reliance on trained clinicians and susceptibility to test anxiety limit accessibility and ecological validity. Game-based cognitive monitoring presents a promising alternative, yet its sensitivity to cognitive changes in individuals with MCI remains underexplored. This study introduces an analytic pipeline for screening cognitive decline using Neuro-World, a serious gaming platform featuring six adaptive subgames that assess cognitive abilities through metrics such as accuracy and response time. Over 12 weeks, ten participants with MCI completed 24 game sessions. Gameplay data were analyzed using correlation-based feature selection and machine learning models to estimate cognitive function and track longitudinal changes. Results showed strong correlations between game-based assessments and clinician-administered MoCA scores$(r=0.71)$, as well as with longitudinal cognitive changes ($r =0.80)$. These findings highlight the potential of game-based cognitive assessments to provide self-administered, ecologically valid screening for cognitive decline in MCI, supporting early detection in aging populations.

This paper presents a novel human-body communication technology that enables capacitive intra-body backscatter (C-IBB) communication between a batteryless ring sensor and a wrist-worn transceiver. C-IBB leverages the finite conductivity of human skin and air coupling capacitance to facilitate nearfield communication (NFC) between wearable devices. The C-IBB system features a radio frequency energy harvester connected to an impedance-matched wearable electrode, which charges a capacitor. This energy storage capacitor powers an ultra-lowpower microcontroller, enabling backscatter communication by modulating the electrode's load impedance. In this work, we developed a modular heterodyne transceiver system and intrabody channel gain emulator. These tools optimize transceiver and tag systems for realistic channel gains tailored to specific electrode configurations. We validated the system's performance on the human body, optimizing it for sensing applications in a wearable ring format. Our preliminary study reveals that the system supports a bit rate of 20.83 kbps with a bit error rate of 10−3to 10−2and operates effectively within a range of 23 cm.

Continuous health monitoring is crucial to ensuring better health and taking preventive measures just-in-time. Existing battery-powered health wearables pose a significant limitation to continuous monitoring as batteries wear out after fixed energy cycles and need replacement. Ambient energy harvesting unlocks battery-free sensing but it suffers from spatio-temporal variability, making it unfit for health sensing. Intra-body power transfer (IBPT) provides an alternative energy source for battery-free operation, however, it can only provide limited energy in order to ensure wearer's safety. Existing system support is designed to maximize computational progress in a single energy cycle, thus wasting energy on computations that become stale in the next energy cycle. We instantiate an IBPT-powered health wearable capable of supporting multiple health sensors. To cope with lower incoming energy, we introduce BodyOS; a system support that exposes programming constructs for domain experts to express health applications in terms of the inherent dependencies of bio-signals being monitored by the application. By avoiding unnecessary sensing operations, BodyOS allows energy-efficient application execution and faster capacitor recharge while ensuring that the data sensed by the application is always useful. We evaluate BodyOS to show that it significantly improves energy efficiency, thus increasing the on-time and number of data points collected by the device.

EEG signals contain highly sensitive information about an individual's mental state, cognitive processes, and health conditions, making privacy preservation crucial. With the rise of commercial headwear capable of capturing EEG signals, developing robust mechanisms for ensuring privacy of such data is imperative. This work aims to protect EEG data privacy in cloud-based processing systems by sending intermediate output after neural network layer splitting to the cloud. We propose a novel holistic Combined Privacy Metric (CPM) that quantifies privacy leakage between raw EEG signals and intermediate outputs. Our study focuses on EEG-based seizure detection using a 1D CNN architecture, achieving accuracy of 96.25%. We evaluate various splitting configurations to optimize the trade-off between privacy preservation and computational efficiency. We find that splitting after the second convolutional layer achieves a CPM of 0.82 with a modest client-side model size of 509kB. This approach significantly enhances EEG data privacy while enabling effective cloud-based analysis, potentially facilitating wider adoption of secure EEG technologies in healthcare and research applications.

Stroke causes motor impairments in the upper limbs, significantly affecting stroke survivors' ability to perform daily activities. Rehabilitation is critical for motor recovery, and frequent assessments are crucial for monitoring improvements in motor impairment throughout the rehabilitation process. Wearable-based motor assessments offer the potential for frequent and objective monitoring of recovery trajectories. Particularly promising is the movement segmentation technique, which decomposes continuous wrist inertial data into lower-level units of upper-limb movements, grounded in theories of motor control and behavior. However, the technique's responsiveness to changes in motor function in stroke survivors has not yet been extensively studied. In this study, we investigate how inertial sensor data obtained during patients' continuous and task-free upper-limb movements can be processed to monitor the recovery trajectory in subacute stroke survivors as they undergo the recovery process. Our results showed that the variability of morphological shapes of the velocity profile of movement segments significantly correlated with changes in clinical scores on the Fugl-Meyer Assessment for the upper extremity (FMA-UE). Moreover, Receiver Operating Characteristic analysis demonstrated that this feature could distinguish participants who showed improvement based on minimal detectable changes, with an Area Under the Curve of 0.86 for FMA-UE. Our comprehensive analysis demonstrates that the movement segmentation approach offers the potential to support objective and frequent assessment of rehabilitation outcomes.

Dog guides are favored by blind and low-vision (BLV) individuals for their ability to enhance independence and confidence by reducing safety concerns and increasing navigation efficiency compared to traditional mobility aids. However, only a relatively small proportion of BLV individuals work with dog guides due to their limited availability and associated maintenance responsibilities. There is considerable recent interest in addressing this challenge by developing legged guide dog robots. This study was designed to determine critical aspects of the handler-guide dog interaction and better understand handler needs to inform guide dog robot development. We conducted semi-structured interviews and observation sessions with 23 dog guide handlers and 5 trainers. Thematic analysis revealed critical limitations in guide dog work, desired personalization in handler-guide dog interaction, and important perspectives on future guide dog robots. Grounded on these findings, we discuss pivotal design insights for guide dog robots aimed for adoption within the BLV community.

Stoke is a leading cause of long-term disability, including upper-limb hemiparesis. Frequent, unobtrusive assessment of naturalistic motor performance could enable clinicians to better assess rehabilitation effectiveness and monitor patients' recovery trajectories. We therefore propose and validate a two-phase data analytic pipeline to estimate upper-limb impairment based on the naturalistic performance of activities of daily living (ADLs). Eighteen stroke survivors were equipped with an inertial sensor on the stroke-affected wrist and performed up to four ADLs in a naturalistic manner. Continuous inertial time series were segmented into sliding windows, and a machine-learned model identified windows containing instances of point-to-point (P2P) movements. Using kinematic features extracted from the detected windows, a subsequent model was used to estimate upper-limb motor impairment, as measured by the Fugl-Meyer Assessment (FMA). Both models were evaluated using leave-one-subject-out cross-validation. The P2P movement detection model had an area under the precision-recall curve of 0.72. FMA estimates had a normalized root mean square error of 18.8% with$R^{2}=0.72$. These promising results support the potential to develop seamless, ecologically valid measures of real-world motor performance.

In the era of deep learning, accessibility to a large amount of wearable Inertial Measurement Unit (IMU) data plays a crucial role in various biomedical and health applications. However, collection of ‘big’ IMU data is extremely challenging due to its cost and time requirements. To address this, researchers have explored using publicly available videos, such as those on YouTube, to extract human skeletal models and synthesize IMU data. However, existing methods for converting skeletal models to virtual IMU data are oversimplified and lack systematic data augmentation capabilities. In this study, we propose a systematic approach to synthesize realistic and diverse IMU data, including three-axis accelerometer and gyroscope measurements, from video-based skeleton representations. Through experiments involving seven healthy individuals, we demonstrate that our method can accurately synthesize accelerometer and gyroscope data with a normalized root mean square error of 14.4 % and 16.0 %, respectively. Furthermore, we qualitatively evaluate the algorithm’s ability to generate a large volume of diverse IMU data. Our findings affirm the potential of obtaining diverse synthetic IMU data from videos, offering a promising solution to reduce the costs associated with collecting IMU data in deep learning-based applications.

Gait modification has been shown to have positive effects on patients with knee osteoarthritis. However, it is challenging to detect whether the patients achieved sufficient modification during gait. To address this, we present a data-driven approach to differentiate between various gaits performed by 20 healthy controls. We calculated features that captured Ground Reaction Force and Center of Position and applied Random Forest to differentiate walking pattern. We analyzed important features that may relate to gait modification. Our experimental results show that our model achieved good performance with an average F1score of 0.74 and an average AUC of 0.90. With further development and testing, we believe that our method can be deployed on wearable platforms to improve the rehabilitation progress of patients with osteoarthritis in clinical settings.

A robot guide dog has compelling advantages over animal guide dogs for its cost-effectiveness, the potential for mass production, and low maintenance burden. However, despite the long history of guide dog robot research, previous studies were conducted with little or no consideration of how the guide dog handler and the guide dog work as a team for navigation. To develop a robotic guiding system that genuinely benefits blind or visually impaired individuals, we performed qualitative research, including interviews with guide dog handlers, trainers, and first-hand blindfold walking experiences with various guide dogs. We build a collaborative indoor navigation scheme for a guide dog robot that includes preferred features such as speed and directional control. For collaborative navigation, we propose a semantic-aware local path planner that enables safe and efficient guiding work by utilizing semantic information about the environment and considering the handler's position and directional cues to determine the collision-free path. We evaluate our integrated robotic system by testing blindfolded walking in indoor settings and demonstrate guide dog-like navigation behavior by avoiding obstacles at typical gait speed (0.7m/s). The following demonstration video link includes an audio description: https://youtu.be/YxlcMeaL7GA

Acoustic sensing on smartphones has gained extensive attention from both industry and research communities. Prior studies suffer from one fundamental limit, i.e., audio sampling rates on smartphones are constrained at 48 kHz. In this work, we present PowerPhone, a software reconfiguration to support higher sampling rates on both microphones and speakers of smartphones. We reverse-engineered more than 100 smartphones and found that their sampling rates can be reconfigured to 192 kHz. We conducted benchmark experiments and showcased field studies to demonstrate the unleashed sensing capability using our reconfigured smart-phones. First, we improve the sensing resolution from 7 cm to 1cm and enable multi-finger gesture recognition on smart-phones. Second, we push the sensing granularity of subtle movements to 2 μm and show the feasibility of turning the smartphone into a micrometer-level machine vibration meter. Third, we increase the sensing range to 6 m and showcase room-scale human presence detection using a smartphone. Finally, we demonstrate that PowerPhone can enable new applications that were previously infeasible. Specifically, we can detect the home appliance status by analyzing ultrasonic leakages above 24 kHz from the wireless charger while charging a smartphone. Our open-source artifacts can be found at: https://powerphone.github.io.

Contactless wireless sensing without attaching a device to the target has achieved promising progress in recent years. However, one severe limitation is the small sensing range. This paper presents Widesee to realize wide-area sensing with only one transceiver pair. Widesee utilizes the LoRa signal to achieve a larger range of sensing and further incorporates drone’s mobility to broaden the sensing area. Widesee presents solutions across software and hardware to overcome two aspects of challenges for wide-range contactless sensing: (i) the interference brought by device mobility and LoRa’s high sensitivity; and (ii) the ambiguous target information such as location when employing just a single pair of transceivers for sensing. We have developed a working prototype of Widesee for human target detection and localization that are especially useful in emergency scenarios such as rescue search, and evaluated Widesee with both controlled experiments and the field study in a high-rise building. Extensive experiments demonstrate the great potential of Widesee for wide-area contactless sensing with a single LoRa transceiver pair hosted on a drone.

Over the past couple of years, the Capacitive Intra-Body Power Transfer (C-IBPT) technology, which uses the human body as a wireless power transfer medium via capacitive links, has received tremendous attention in the field as a potential solution to support a network of battery-free body sensors. However, circuit modeling of C-IBPT systems, despite its importance in supporting the reliable operation of battery-free body sensors, has been significantly understudied in the field. This paper proposes a finite element model (FEM) and equivalent linear circuit models to estimate path loss and inter-electrode capacitance of a C-IBPT system. As a demonstrative example, the model approximates a typical human forearm (from wrist to elbow) and allows for investigation of the transmission loss between a skin-coupled power transmitter and a receiver in the electro-quasistatic domain. The computed transmission loss from the proposed model is further validated against experimental measurements obtained from five healthy human subjects using a wearable 40 MHz radio frequency (RF) transmitter and an isolated power receiver system in a laboratory environment. The preliminary experimental data show an approximate 40 dB transmission loss within 10 cm body channel length for the parallel plate electrode configuration with dimensions of 30 mm ×40 mm. The simulation finding shows a lower transmission loss of 35 dB and 13.5 fF coupling capacitance across a 10 cm body channel.

Acoustic sensing shows great potential to transform billions of consumer-grade electronic devices that people interact with on a daily basis into ubiquitous sensing platforms. In this paper, we share our experience and findings during the process of developing and deploying acoustic sensing systems for real-world usage. We identify multiple practical problems that were not paid attention to in the research community, and propose the corresponding solutions. The challenges include: (i) there exists annoying audible sound leakage caused by acoustic sensing; (ii) acoustic sensing actually affects music play and voice call; (iii) acoustic sensing consumes a significant amount of power, degrading the battery life; (iv) real-world device mobility can fail acoustic sensing. We hope the shared experience can benefit not only the future development of sensing algorithms but also the hardware design, pushing acoustic sensing one step further towards real-life adoption.

Acoustic signal has been recently adopted for contact-free hand gesture recognition due to its fine-grained sensing granularity and wide availability of microphone and speaker in consumer-grade electronic devices such as smartphones. However, a very limited sensing range constrains acoustic sensing to application scenarios where users interact with devices in close proximity. In this paper, we improve the range of acoustic sensing and demonstrate the feasibility of enabling room-scale hand gesture recognition using commodity smart speakers. We develop a series of novel signal processing techniques and implement our system on two commodity smart speaker prototypes with different numbers of microphones. Extensive evaluations are performed in three different environments with 1440 gestures collected from 16 participants. Experiment results show that our system can significantly increase the sensing range from 1 m to 4--5 m. In the challenging scenario where the user is 4 m away from the smart speaker and there is strong interference, the achieved gesture recognition accuracy is still higher than 90%.

Respiration rate is an essential vital indicator for health monitoring. While traditional sensor-based methods support acceptable sensing performance, the recent advance in wireless sensing could enable sensor-free and contact-free respiration sensing, which is particularly important during the practice of social distancing against a pandemic like COVID-19. Among a variety of wireless technologies employed for respiration sensing, Wi-Fi-based solutions are most popular due to the pervasive development of infrastructure. However, the existing Wi-Fi-based approaches need to retrieve Wi-Fi readings from access points, which are not often accessible for the end users. In this article, we propose a novel system, MoBreath, in which we utilize the Wi-Fi channel state information (CSI) readings extracted from the end-user device, a smartphone, to monitor the respiration rate for the first time. We introduce and address unique technical challenges, such as selecting the optimum CSI subcarriers from many noisy candidates and providing smartphone placement strategies for both single and multiple human target scenarios based on the Fresnel zone model to support highly accurate respiration sensing. Our evaluation of MoBreath using commodity smartphones in different environments shows that it can accurately estimate the respiration rate at a low error rate of 0.34 breaths per minute and support the sensing range of up to 3–4 m. Even for challenging scenarios such as the target is covered by a quilt and multiple targets are in the sensing area, MoBreath can still support highly accurate results.

Contactless acoustic motion tracking enables new opportunities to interact with smart devices, such as smartphones and voice-controlled smart assistants. The speakers and microphones integrated in these devices provide unique opportunities to simultaneously track multiple targets in a fine-grained manner. To this end, we propose a system, namely FM-Track, that enables contactless multi-target tracking using acoustic signals. We first introduce a signal model to characterize the location and motion status of targets by fusing the information from multiple dimensions (i.e., range, velocity, and angle of targets). Then we develop a series of techniques to separate signals reflected from multiple targets and accurately track each individual target. We implement and evaluate FM-Track on both research-purpose hardware platform (i.e., Bela) and commercial devices (i.e., smartphones and smart speakers). Extensive experiments show that FM-Track can successfully differentiate two targets with a spacing as small as 1 cm, and achieve a median tracking accuracy of 0.86 cm and 0.11 cm for absolute range and displacement estimates respectively. For multi-target tracking, FM-Track can accurately track four targets and the tracking range can be up to 3 m.

Upper-limb impairments due to stroke can severely affect the quality of life in patients. Scientific evidence supports that repetitive rehabilitation exercises can improve motor ability in stroke patients. Rehabilitation games gained tremendous interest among researchers and clinicians because of their potential to make the seemingly mundane, enduring rehabilitation therapies more engaging. However, routine and longitudinal use of rehabilitation games in real-world clinical settings has not been investigated in depth. Particularly, we know little about current practices, challenges, and their potential impacts on therapeutic outcomes. To address this gap, we established a partnership with a rehabilitation hospital where game-assisted rehabilitation was routinely employed over a 2-year period. We then conducted an observational study, in which we observed 11 game-assisted therapy sessions and interviewed 15 therapists who moderated the therapy. Significant findings include (1) different engagement patterns of stroke patients in game-assisted therapy, (2) imperative roles of therapists in moderating games and challenges that therapists face during game-assisted therapy, and (3) lack of support for therapists in delivering patient-centered, personalized therapy to individual stroke patients. Furthermore, we discuss design implications for more effective rehabilitation game therapies that take into consideration both patients and therapists and their specific needs.

Continuous monitoring of hand function in individuals with upper limb paresis, such as stroke survivors, could provide a quantitative assessment of their real-world functional performance, which has great potential to enhance the clinical guidance of rehabilitation interventions. In this paper, we explore a novel wearable approach to quantify the amount of hand use by leveraging Radio Frequency Identification (RFID) technologies. We introduce a prototype implementation of our wearable RFID system composed of a wrist-worn reader (antenna) and a small passive tag placed on a fingernail. Then, we discuss a machine learning-based data analytic pipeline that analyzes the backscattered RF signal to estimate the amount of hand use. The accuracy of the system is validated against an optoelectronic motion capture system - the gold standard for human movement analyses - using a dataset collected from five neurologically intact individuals. The proposed wearable RFID system could accurately estimate the amount of hand use with R2of 0.67 and Normalized Root Mean Square Error of 7.3%, and shows great potential for clinical applications.

Contactless wireless sensing without attaching a device to the target has achieved promising progress in recent years. However, one severe limitation is the small sensing range. This paper presents WideSee to realize wide-area sensing with only one transceiver pair. WideSee utilizes the LoRa signal to achieve a larger range of sensing and further incorporates drone's mobility to broaden the sensing area. WideSee presents solutions across software and hardware to overcome two aspects of challenges for wide-range contactless sensing: (i) the interference brought by the device mobility and LoRa's high sensitivity; and (ii) the ambiguous target information such as location when employing just a single pair of transceivers. We have developed a working prototype of WideSee for human target detection and localization that are especially useful in emergency scenarios such as rescue search, and evaluated WideSee with both controlled experiments and the field study in a high-rise building. Extensive experiments demonstrate the great potential of WideSee for wide-area contactless sensing with a single LoRa transceiver pair hosted on a drone.

Contactless wireless sensing without attaching a device to the target has achieved promising progress in recent years. However, one severe limitation in this field is the limited sensing range. This paper presents WideSee to realize wide-area sensing with only one transceiver pair. WideSee utilizes the LoRa signal to achieve a larger range of sensing and further incorporates drone's mobility to broaden the sensing area. We have developed a working prototype of WideSee for human target detection and localization that are especially useful in emergency scenarios like rescue and terrorist search. We also evaluated WideSee with field study in a high-rise building, which demonstrates the great potential of WideSee for supporting wide-area contactless sensing applications with a single LoRa transceiver pair hosted on a drone.

In this work, we present SkinnyPower, a technology for Intra-Body Power Transfer (IBPT) that wirelessly transfers power through human skin to operate batteryless wearable sensors. We envision a scenario, in which batteryless sensors placed on small body parts (e.g., on-finger, in-ear, and in-mouth) can obtain operating power from another body-worn energy sources (e.g., already existing battery-powered wearable devices such as a smartwatch or a BandAid-like battery patch attached to the neck). The key technical challenges in realizing this vision include 1) providing a robust return path in the body channel - where the forward (power signal) and return (ground) paths are not explicitly defined - using implicit capacitances formed between the devices and earth ground, and 2) achieving reliable operation despite variations in capacitive coupling between the skin and devices, devices and earth ground, and conductance of the subdermal layer. We identify and optimize critical system design parameters to maximize the power transfer between a transmitter and a receiver with the capacitively coupled return path. To demonstrate and validate the concept of IBPT, we implemented a prototype consisting of 1) a wrist-worn, battery-equipped power transmitter that sends alternating current through the human body and 2) a finger-worn, batteryless sensor device that operates solely on body-transferred power. Evaluations on five subjects show that we can reliably support the power of approximately 1 mW, which can be used to operate an embedded system, continuously collect sensor (e.g. accelerometer) data, and wirelessly transfer the collected data in real-time using Bluetooth Low Energy. Moreover, we achieve a power transfer rate of 14.5% between the transmitter and receiver, which is significantly higher than other wireless power transfer techniques such as RFID. We believe that the proposed system has great potential to transform current architectures and designs for body-area networks, promoting the development of innovative on-body sensors that would otherwise not be possible with on-device batteries.

In this work, we present SkinnyPower, a concept of Intra-Body Power Transfer (IBPT) that wirelessly transfers power through human skin to operate batteryless wearable sensors. We envision a scenario, in which batteryless sensors placed on small body parts (e.g., on-finger, in-ear, and in-mouth) can obtain operating power from another body-worn energy sources (e.g., already existing battery-powered wearable devices such as a smartphone, smartwatch, or BandAid-like battery patch attached to the neck). To demonstrate and validate the concept of IBPT, we implemented a prototype consisting of 1) a wrist-worn, battery-powered power transmitter that sends alternating current through the human body and 2) a finger-worn, batteryless sensor device that operates solely on body-transferred power. Evaluations on five subjects show that we can reliably support the power of approximately 1 mW, which can be used to operate an embedded system, continuously collect sensor (accelerometer) data, and wirelessly transfer the collected data in real-time using Bluetooth Low Energy. We believe that the proposed system has great potential to transform current architectures and designs for body-area networks, promoting the development of innovative on-body sensors that would otherwise not be possible with on-device batteries.

Individuals with cognitive impairments are evaluated using clinically validated cognitive assessment tools, which need to be administered by trained therapists. This serves as a major barrier for frequent and longitudinal monitoring of patients' cognitive impairment level. We introduce Neuro-World, a set of six mobile games designed to challenge visuospatial short-term memory and selective attention, which allows one to self-administer the assessment of his/her cognitive impairment level. Game performance is analyzed to estimate a widely accepted clinical measure, the mini mental state examination (MMSE), which highlights the translational impact of the system in real-world settings. We collected game-specific performance data from 12 post-stroke patients at baseline and a three-month follow-up, which were used to train supervised machine learning models to estimate the corresponding MMSE scores. The results presented herein show that the proposed approach can estimate the MMSE scores with a normalized root mean square error of 5.75%. We also validate the system's responsiveness to longitudinal changes in cognitive impairment level and demonstrate the system's positive usability in cognitively impaired individuals and their willingness to adhere to the longitudinal use. This study demonstrates that Neuro-world has great potential to be used to evaluate the cognitive impairment level and monitor its long-term change. This study enables new clinical and research opportunities for accurate, longitudinal assessment of cognitive function via mobile games.

Objective assessment of stroke survivors' upper limb movements in ambulatory settings can provide clinicians with important information regarding the real impact of rehabilitation outside the clinic and help to establish individually-tailored therapeutic programs. This paper explores a novel approach to monitor the amount of hand use, which is relevant to the purposeful, goal-directed use of the limbs, based on a body networked sensor system composed of miniaturized finger- and wrist-worn accelerometers. The main contributions of this paper are twofold. First, this paper introduces and validates a new benchmark measurement of the amount of hand use based on data recorded by a motion capture system, the gold standard for human movement analysis. Second, this paper introduces a machine learning-based analytic pipeline that estimates the amount of hand use using data obtained from the wearable sensors and validates its estimation performance against the aforementioned benchmark measurement. Based on data collected from 18 neurologically intact individuals performing 11 motor tasks resembling various activities of daily living, the analytic results presented herein show that our new benchmark measure is reliable and responsive, and that the proposed wearable system can yield an accurate estimation of the amount of hand use (normalized root mean square error of 0.11 and average Pearson correlation of 0.78). This study has the potential to open up new research and clinical opportunities for monitoring hand function in ambulatory settings, ultimately enabling evidence-based, patient-centered rehabilitation and healthcare.

To circumvent current limitations of wearable sensors that can be used to assess and monitor joint movements, we developed an accurate, low-cost, flexible wearable sensor comprising a retractable reel, a string, and a potentiometer. This sensor is intended to estimate joint angles in correlation with the amount of skin stretch measured by the change in the length of the string. In this study, we validated the accuracy of the sensor against an optoelectronic system in estimating knee joint angles using a dataset obtained from 9 healthy individuals while they walk and run on a treadmill. By our simple calibration procedure, we could convert the voltage output of the potentiometer to the amount of skin stretch as subjects flex or extend their knee. Then, we incorporated a simple polynomial fitting model to estimate the joint angle. Using a leave-one-subject-out cross validation, we achieved an average root mean square error of 4.51 degrees. This work demonstrates the accuracy of the proposed system in estimating knee joint angles and provides the basis to develop more complex systems to assess and monitor joints having more degrees of freedom. We believe that our novel low-cost wearable sensing technology has great potential to enable joint kinematic monitoring in ambulatory settings.

Predicting the functional outcomes of spinal cord disorder patients after medical treatments, such as a surgical operation, has always been of great interest. Accurate posttreatment prediction is especially beneficial for clinicians, patients, care givers, and therapists. This paper introduces a prediction method for postoperative functional outcomes by a novel use of Gaussian process regression. The proposed method specifically considers the restricted value range of the target variables by modeling the Gaussian process based on a truncated Normal distribution, which significantly improves the prediction results. The prediction has been made in assistance with target tracking examinations using a highly portable and inexpensive handgrip device, which greatly contributes to the prediction performance. The proposed method has been validated through a dataset collected from a clinical cohort pilot involving 15 patients with cervical spinal cord disorder. The results show that the proposed method can accurately predict postoperative functional outcomes, Oswestry disability index and target tracking scores, based on the patient's preoperative information with a mean absolute error of 0.079 and 0.014 (out of 1.0), respectively.

Motivated by a need for accurate assessment and monitoring of patients with knee osteoarthritis in an ambulatory setting, a wearable electrogoniometer composed of a knee angular sensor and a three-axis accelerometer placed on the thigh is developed. Accurate assessment of knee kinematics requires accurate detection of walking amongst dynamic, heterogeneous, and individualized activities of daily living. This paper investigates four different machine learning techniques for detecting occurrences of walking in uncontrolled environments based on a dataset collected from a total of 4 healthy subjects. Multi-class classifier (random forest) based detection method showed the best performance, which supports 90% precision and 75% recall. The in-depth analysis and interpretation of the results show that accurate decision boundaries are necessary between 1) fast walking and descending stairs, 2) slow walking and ascending stairs, as well as 3) slow walking and transitional activities. This work provides a systematic approach to detect occurrences of walking in uncontrolled living conditions, which can also be extended to other activities.

Exergaming as a tool to combat obesity yields an interesting take on the problem of design and implementation of activity recognition systems for truly mobile games that achieve moderate levels of intensity. This work presents SoccAR, a mobile, sensor-based wearable exergaming system with fine-grain activity recognition. The system in this paper presents a recognition algorithm for the appropriate classification of 26 movements by extracting a large number of features and selecting the most important, as well as developing a multiple model strategy to better classify movements. This movement strategy allows for a trade off of detailed classification versus classification speed. A metric to define the accuracy in terms of the importance of particular movements is defined. The scheme presented develops a framework for more accurately classifying movements with a smaller number of features for a large, multiclass real-time environment. This results in a more accurate classification of movements, with an F-score in cross-validation of .937 using a PUK-kernel based SVM and multiple models, to .755 using only a single RBF-based model and 20 features.

Due to the exploding costs of chronic diseasesstemming from physical inactivity, wearable sensor systems toenable remote, continuous monitoring of individuals has increasedin popularity. Many research and commercial systems exist inorder to track the activity levels of users from general dailymotion to detailed movements. This work examines this problemfrom the space of smartwatches, using the Samsung GalaxyGear, a commercial device containing an accelerometer and agyroscope, to be used in recognizing physical activity. This workalso shows the sensors and features necessary to enable suchsmartwatches to accurately count, in real-time, the repetitions offree-weight and body-weight exercises. The goal of this work isto try and select only the best single axis for each activity byextracting only the most informative activity-specific features, inorder to minimize computational load and power consumptionin repetition counting. The five activities are incorporated in aworkout routine, and knowing this information, a random forestclassifier is built with average area under the curve (AUC) of: 974, with average accuracy of 93%, in cross validation to identify eachrepetition of a given exercise using all available sensors and AUCof: 950 with accuracy of 89:9% using the single best axis foreach activity alone. Adding a gyroscope with the accelerometerincreased the average AUC from: 968 to: 974, increasing theaccuracy of specific movements as much as 2%. Results show that, while a combination of accelerometer and gyroscope provide thestrongest classification results, often times features extracted froma single, best axis are enough to accurately identify movementsfor a personal training routine, where that axis is often, but notalways, an accelerometer axis.

Exergaming is expanding as an option for sedentary behavior in childhood/adult obesity and for extra exercise for gamers. This paper presents the development process for a mobile active sports exergame with near-realistic motions through the usage of body-wearable sensors. The process begins by collecting a dataset specifically targeted to mapping real-world activities directly to the games, then, developing the recognition system in a fashion to produce an enjoyable game. The classification algorithm in this paper has precision and recall of 77% and 77% respectively, compared with 40% and 19% precision and recall on current activity monitoring algorithms intended for general daily living activities. Aside from classification, the user experience must be strong enough to be a successful system for adoption. Indeed, fast and intense activities as well as competitive, multiplayer environments make for a successful, enjoyable exergame. This enjoyment is evaluated through a 30 person user study. Multiple aspects of the exergaming user experience trials have been merged into a comprehensive survey, called ExerSurvey. All but one user thought the motions in the game were realistic and difficult to cheat. Ultimately, a game with near-realistic motions was shown to be an enjoyable, active video exergame for any environment.

Hyperexcitability in hand is a disorder characterized by exaggerated muscle movement, and is a common symptom associated with neuro-degenerative diseases and spinal cord injuries. Current assessment methods for hyperexcitability rely on subjective examination, or on methods that evaluate the overall hand grip performance without particularization in the excitation. This paper introduces a system that utilizes an inexpensive body sensor device combined with a series of signal processing units that extract information specifically related to physiological phenomena generated by hyperexcitability. A clinical cohort study has been conducted on nine patients with cervical spinal cord injuries (mean age 58.2 ± 13.5). The experimental results show that the proposed signal processing mechanism accurately detects and analyzes the body signal. The medical significance of the experimental results is also investigated. This opens up a new opportunity for patients and clinical professionals to obtain accurate feedback of patient's motor function in an economical and ubiquitous manner.

The use of accelerometers to approximate energy expenditure and serve as inputs for exergaming, have both increased in prevalence in response to the worldwide obesity epidemic. Exergames have a need to show energy expenditure values to validate their results, often using accelerometer approximations applied to general daily-living activities. This work presents a method for estimating the metabolic equivalent of task (MET) values achieved when users perform exergaming-specific movements. This shows the caloric expenditure achieved by active video games, based upon raw gravity values of accelerations. Results show that, while a fusion of sensors monitoring the entire body achieves the best results, sensors placed closest to the primary location of movement achieve the most accurate approximations to the METs achieved per activity as well as the overall MET achieved for the soccer exergame under consideration. The METs achieved approach 7, the value considered to be actual casual soccer game play.

With the growing cost associated with the diagnosis and treatment of chronic neuro-degenerative diseases, the design and development of portable monitoring systems becomes essential. Such portable systems will allow for early diagnosis of motor function ability and provide new insight into the physical characteristics of ailment condition. This paper introduces a highly mobile and inexpensive monitoring system to quantify upper-limb performance for patients with movement disorders. With respect to the data analysis, we first present an approach to quantify general motor performance using the introduced sensing hardware. Next, we propose an ailment-based analysis which employs a significant-feature identification algorithm to perform cross-patient data analysis and classification. The efficacy of the proposed framework is demonstrated using real data collected through a clinical trial. The results show that the system can be utilized as a preliminary diagnostic tool to inspect the level of hand-movement performance. The ailment-based analysis performs an intergroup comparison of physiological signals for cerebral vascular accident (CVA) patients, chronic inflammatory demyelinating polyneuropathy (CIDP) patients, and healthy individuals. The system can classify each patient group with an accuracy of up to 95.00% and 91.42% for CVA and CIDP, respectively.

In this paper, we present a mechanism for estimating data quality of BANs composed of cardiac sensors. Currently available cardiac monitoring sensors suffer from high level of noise generated from loose physical contact of the sensor node due to the highly mobile and pervasive environment of the BAN (e.g., at-home remote health care applications). Therefore, there is a need to estimate the data quality of individual cardiac sensors as well as the data quality of the overall BAN while particularly considering the resource scarceness of BAN-scale mobile systems. The proposed method successfully estimates the data quality of a BAN without employing computationally expensive machine learning techniques. It also provides a number of resource management options that enable efficient data quality estimation. We present experimental results of four participants with three off-the-shelf cardiac sensors to form a BAN. We also present simulation results to examine if the proposed mechanism can successfully detect health hazardous events such as heart arrhythmia.

We envision that diverse social exercising games, or exergames, will emerge, featuring much richer interactivity with immersive game play experiences. Further, the recent advances of mobile devices and wireless networking will make such social engagement more pervasive - people carry portable exergame devices (e.g., jump ropes) and interact with remote users anytime, anywhere. Towards this goal, we explore the potential of using heterogeneous exercise devices as game controllers for a multi-player social exergame; e.g., playing a boat paddling game with two remote exercisers (one with a jump rope, and the other with a treadmill). In this paper, we propose a novel platform called ExerLink that converts exercise intensity to game inputs and intelligently balances intensity/delay variations for fair game play experiences. We report the design considerations and guidelines obtained from the design and development processes of game controllers. We validate the efficacy of game controllers and demonstrate the feasibility of social exergames with heterogeneous exercise devices via extensive human subject studies.

We demonstrate a pervasive social exergame platform called ExerLink that converts exercise intensity to game inputs and intelligently balances intensity/delay variations for fair game play experiences. Also, we show the potential of using multiple exercise devices as game controllers and incorporating multiple heterogeneous controllers into a game. Specifically, we consider a class of exercise equipment used for repetitive, individual, and aerobic (RIA) exercises such as treadmill running, stationary cycling, hula hooping, and jump roping.