Bio-impedance spectroscopy (BIS) is a sophisticated testing technique used to analyze impedance changes at different frequencies. In this study, we investigated the estimation of the Cole Model for BIS measurements without the need for high-frequency resistance and reactance measurements, where they are inaccurate due to leakage capacitences. We employed a Texas Instruments evaluation kit (AFE4300) and compared the Cole plots of two different circuit models of tissue between the proposed configuration and a commercial impedance analyzer used as a reference. To enhance the performance of the AFE4300, we incorporated an external direct digital synthesis (DDS) to generate higher frequencies. The results demonstrated the reliability of the proposed theoretical estimation technique in accurately estimating the resistances and capacitance of the Cole Model.

Adherence to medications is a complex task that requires complex biobehavioral support. To better provide tools to assist with medication adherence, digital pills provide an option to directly measure medication taking behaviors. These systems comprise a gelatin capsule with radiofrequency emitter, a wearable Reader that collects the radio signal and a smartphone app that collects ingestion data displays it for patients and clinicians. These systems are feasible in measuring adherence in the real-world, even in stigmatized diseases like HIV treatment adherence. While the current iteration of the digital pill system utilizes a wearable Reader worn like a necklace, preliminary feedback demonstrated that a miniaturized system that was worn on the wrist could be more functional in the real-world. This paper therefore describes the development and preliminary field testing of a wrist-borne wearable Reader to facilitate acquisition of oral HIV pre-exposure prophylaxis (PrEP) adherence data among individual prescribed PrEP.

Researchers have been leveraging wearable cameras to both visually confirm and automatically detect individuals' eating habits. However, energy-intensive tasks such as continuously collecting and storing RGB images in memory, or running algorithms in real-time to automate detection of eating, greatly impacts battery life. Since eating moments are spread sparsely throughout the day, battery life can be mitigated by recording and processing data only when there is a high likelihood of eating. We present a framework comprising a golf-ball sized wearable device using a low-powered thermal sensor array and real-time activation algorithm that activates high-energy tasks when a hand-to-mouth gesture is confirmed by the thermal sensor array. The high-energy tasks tested are turning on the RGB camera (Trigger RGB mode) and running inference on an on-device machine learning model (Trigger ML mode). Our experimental setup involved the design of a wearable camera, 6 participants collecting 18 hours of data with and without eating, the implementation of a feeding gesture detection algorithm on-device, and measures of power saving using our activation method. Our activation algorithm demonstrates an average of at-least 31.5% increase in battery life time, with minimal drop of recall (5%) and without impacting the accuracy of detecting eating (a slight 4.1% increase in F1-Score).

We investigate the connection between the autonomic nervous system and the voice in patients with vocal hyperfunction and healthy-control groups. We present a methodology and preliminary results of two multi-modal measurement streams that capture this relationship. Subjects were instrumented for daily, ambulatory collection of their voice and wrist-based electrodermal activity. Measures of vocal function (e.g., fundamental frequency) were computed, as well as measures of autonomic function (e.g., skin conductance response). Spearman correlation coefficients were calculated to measure the relationship between vocal and autonomic function over sliding windows throughout each observation day. We found preliminary evidence that patients with a subtype of vocal hyperfunction (non-phonotraumatic vocal hyperfunction) exhibit a coupling between the autonomic nervous system and the vocal system. Understanding how the autonomic nervous system interacts with the voice may provide new insights into the etiology/pathophysiology of vocal hyperfunction and improve prevention, diagnosis and treatment of these disorders.

The ability to monitor a person's level of daily activity can inform self-management of physical activity and assist in augmenting behavioral interventions. For older adults, the importance of regular physical activity is critical to reduce the risk of long-term disability. In this work, we present GeriActive, an application on the Amulet wrist-worn device that monitors in real time older adults' daily activity levels (low, moderate and vigorous), which we categorized using metabolic equivalents (METs). The app implements an activity-level detection model we developed using a linear Support Vector Machine (SVM). We trained our model using data from volunteer subjects (n=29) who performed common physical activities (sit, stand, lay down, walk and run) and obtained an accuracy of 94.3% with leave-one-subject-out (LOSO) cross-validation. We ran a week-long field study to evaluate the usability and battery life of the GeriActive system where 5 older adults wore the Amulet as it monitored their activity level. Their feedback showed that our system has the potential to be usable and useful. Our evaluation further revealed a battery life of at least 1 week. The results are promising, indicating that the app may be used for activity-level monitoring by individuals or researchers for health delivery interventions that could improve the health of older adults.

Transcutaneous vagus nerve stimulation (t-VNS) is a promising technology for modulating brain function and possibly treating disorders of the central nervous system. While handheld devices are available for t-VNS, stimulation efficacy can only be quantified using expensive imaging or blood biomarker analyses. Additionally, the parameters and "dosage" recommendations for t-VNS are typically fixed, as there are limited biomarkers that can assess downstream effects of the stimulation outside of clinical settings. In this proof-of-concept study, we evaluated non-invasive peripheral cardiovascular measurements as physiological biomarkers of t-VNS efficacy. Specifically, we hypothesized two physiological biomarkers: (1) the pre-ejection period (PEP) of the heart - a parameter closely linked to sympathetic tone - and (2) the amplitude of peripheral photoplethysmogram (PPG) waveforms - representing changes in vasomotor tone and thus parasympathetic / sympathetic activation. A total of six healthy human subjects participated in the multi-day study, half each undergoing active or sham t-VNS stimulus. The three subjects receiving t-VNS had no decrease in PEP and an increase in PPG amplitude following t-VNS, while the subjects receiving sham stimulus had a decrease in PEP and no change in PPG amplitude. When combined with mental stress (a traumatic script being read back to the subjects), the group with t-VNS had no decrease in PEP and only a slight decrease in PPG amplitude following stimulus, while the group receiving sham stimulus had a decrease in PEP and also a slight decrease in PPG amplitude. These studies suggest that PEP and PPG amplitude measures may provide non-invasive physiological biomarkers of t-VNS efficacy, including in the presence of mental stress.

This study reports a new technique for extracting muscle synergies using continuous wavelet transform. The method allows to quantify coincident activation of muscle groups caused by the physiological processes of fixed duration, thus enabling the extraction of wavelet modules of arbitrary groups of muscles. Hierarchical clustering and identification of the repeating wavelet modules across subjects and across movements, was used to identify consistent muscle synergies. Results indicate that the most frequently repeated wavelet modules comprised combinations of two muscles that are not traditional agonists and span different joints. We have also found that these wavelet modules were flexibly combined across different movement directions in a pattern resembling directional tuning. This method is extendable to multiple frequency domains and signal modalities.

Asthma is the most prevalent chronic disease among pediatrics, as it is the leading cause of student absenteeism and hospitalization for those under the age of 15. To address the significant need to manage this disease in children, the authors present a mobile health (mHealth) system that determines the risk of an asthma attack through physiological and environmental wireless sensors and representational state transfer application program interfaces (RESTful APIs). The data is sent from wireless sensors to a smartwatch application (app) via a Health Insurance Portability and Accountability Act (HIPAA) compliant cryptography framework, which then sends data to a cloud for real-time analytics. The asthma risk is then sent to the smartwatch and provided to the user via simple graphics for easy interpretation by children. After testing the safety and feasibility of the system in an adult with moderate asthma prior to testing in children, it was found that the analytics model is able to determine the overall asthma risk (high, medium, or low risk) with an accuracy of 80.10±14.13%. Furthermore, the features most important for assessing the risk of an asthma attack were multifaceted, highlighting the importance of continuously monitoring different wireless sensors and RESTful APIs. Future testing this asthma attack risk prediction system in pediatric asthma individuals may lead to an effective self-management asthma program.

Limb tracking is an important aspect of human-machine interfaces (HMI). These systems, however, can often be limited by complex algorithms requiring significant processing power, obtrusive and immobile sensing techniques, and high costs. In this work, we utilize a sensor fusion algorithm implemented in commercial inertial measurement units (IMU) to combine accelerometer and gyroscope measurements in an effort to minimize computational requirements of the limb tracking system. In addition, previously developed methods were implemented to eliminate sensor drift by including information from a magnetometer. We tested the accuracy of our system by computing the root mean squared error (RMSE) of the true angle between the headings of two sensors and the estimate of that angle through quaternion-vector manipulations. An average RMSE of approximately 2.9° was achieved. Our limb tracking system is wearable, minimally complex, low-cost, and simple to use which has proven useful in multiple HMI applications discussed herein.

What can wearable sensors and usage of smart phones tell us about academic performance, self-reported sleep quality, stress and mental health condition? To answer this question, we collected extensive subjective and objective data using mobile phones, surveys, and wearable sensors worn day and night from 66 participants, for 30 days each, totaling 1,980 days of data. We analyzed daily and monthly behavioral and physiological patterns and identified factors that affect academic performance (GPA), Pittsburg Sleep Quality Index (PSQI) score, perceived stress scale (PSS), and mental health composite score (MCS) from SF-12, using these month-long data. We also examined how accurately the collected data classified the participants into groups of high/low GPA, good/poor sleep quality, high/low self-reported stress, high/low MCS using feature selection and machine learning techniques. We found associations among PSQI, PSS, MCS, and GPA and personality types. Classification accuracies using the objective data from wearable sensors and mobile phones ranged from 67-92%.