ActivityAware: an App for Real-Time Daily Activity Level Monitoring on the Amulet Wrist-Worn Device

Physical activity helps reduce the risk of cardiovascular disease, hypertension and obesity. The ability to monitor a person\u27s daily activity level can inform self-management of physical activity and related interventions. For older adults with obesity, the importance of regular, physical activity is critical to reduce the risk of long-term disability. In this work, we present ActivityAware, an application on the Amulet wrist-worn device that measures daily activity levels (sedentary, moderate and vigorous) of individuals, continuously and in real-time. The app implements an activity-level detection model, continuously collects acceleration data on the Amulet, classifies the current activity level, updates the day\u27s accumulated time spent at that activity level, logs the data for later analysis, and displays the results on the screen. We developed an activity-level detection model using a Support Vector Machine (SVM). We trained our classifiers using data from a user study, where subjects performed the following physical activities: sit, stand, lay down, walk and run. With 10-fold cross validation and leave-one-subject-out (LOSO) cross validation, we obtained preliminary results that suggest accuracies up to 98%, for n=14 subjects. Testing the ActivityAware app revealed a projected battery life of up to 4 weeks before needing to recharge. The results are promising, indicating that the app may be used for activity-level monitoring, and eventually for the development of interventions that could improve the health of individuals

A Wearable System that Knows Who Wears It

Body-area networks of pervasive wearable devices are increasingly used for health monitoring, personal assistance, entertainment, and home automation. In an ideal world, a user would simply wear their desired set of devices with no configuration necessary: the devices would discover each other, recognize that they are on the same person, construct a secure communications channel, and recognize the user to which they are attached. In this paper we address a portion of this vision by offering a wearable system that unobtrusively recognizes the person wearing it. Because it can recognize the user, our system can properly label sensor data or personalize interactions. \par Our recognition method uses bioimpedance, a measurement of how tissue responds when exposed to an electrical current. By collecting bioimpedance samples using a small wearable device we designed, our system can determine that (a)the wearer is indeed the expected person and (b) the device is physically on the wearer\u27s body. Our recognition method works with 98% balanced-accuracy under a cross-validation of a day\u27s worth of bioimpedance samples from a cohort of 8 volunteer subjects. We also demonstrate that our system continues to recognize a subset of these subjects even several months later. Finally, we measure the energy requirements of our system as implemented on a Nexus S smart phone and custom-designed module for the Shimmer sensing platform

Development of a ‘Smart’ Resistance Exercise Band to Assess Strength

https://digitalcommons.dartmouth.edu/wetterhahnsymposium-2018/1004/thumbnail.jp

Who Wears Me? Bioimpedance as a Passive Biometric

Mobile and wearable systems for monitoring health are becoming common. If such an mHealth system knows the identity of its wearer, the system can properly label and store data collected by the system. Existing recognition schemes for such mobile applications and pervasive devices are not particularly usable – they require ıt active engagement with the person (e.g., the input of passwords), or they are too easy to fool (e.g., they depend on the presence of a device that is easily stolen or lost). \par We present a wearable sensor to passively recognize people. Our sensor uses the unique electrical properties of a person\u27s body to recognize their identity. More specifically, the sensor uses ıt bioimpedance – a measure of how the body\u27s tissues oppose a tiny applied alternating current – and learns how a person\u27s body uniquely responds to alternating current of different frequencies. In this paper we demonstrate the feasibility of our system by showing its effectiveness at accurately recognizing people in a household 90% of the time

A bioimpedance-based monitor for real-time detection and identification of secondary brain injury

Secondary brain injury impacts patient prognosis and can lead to long-term morbidity and mortality in cases of trauma. Continuous monitoring of secondary injury in acute clinical settings is primarily limited to intracranial pressure (ICP); however, ICP is unable to identify essential underlying etiologies of injury needed to guide treatment (e.g. immediate surgical intervention vs medical management). Here we show that a novel intracranial bioimpedance monitor (BIM) can detect onset of secondary injury, differentiate focal (e.g. hemorrhage) from global (e.g. edema) events, identify underlying etiology and provide localization of an intracranial mass effect. We found in an in vivo porcine model that the BIM detected changes in intracranial volume down to 0.38 mL, differentiated high impedance (e.g. ischemic) from low impedance (e.g. hemorrhagic) injuries (p \u3c 0.001), separated focal from global events (p \u3c 0.001) and provided coarse ‘imaging’ through localization of the mass effect. This work presents for the first time the full design, development, characterization and successful implementation of an intracranial bioimpedance monitor. This BIM technology could be further translated to clinical pathologies including but not limited to traumatic brain injury, intracerebral hemorrhage, stroke, hydrocephalus and post-surgical monitoring

Use of Amulet in behavioral change for geriatric obesity management

Background: Obesity in older adults is a significant public health concern. Weight-loss interventions are known to improve physical function but risk the development of sarcopenia. Mobile health devices have the potential to augment existing interventions and, if designed accordingly, could improve one’s physical activity and strength in routine physical activity interventions. Methods and results: We present Amulet, a mobile health device that has the capability of engaging patients in physical activity. The purpose of this article is to discuss the development of applications that are tailored to older adults with obesity, with the intention to engage and improve their health. Conclusions: Using a team-science approach, Amulet has the potential, as an open-source mobile health device, to tailor activity interventions to older adults

Using Natural Language Processing and Sentiment Analysis to Augment Traditional User-Centered Design: Development and Usability Study

Background: Sarcopenia, defined as the age-associated loss of muscle mass and strength, can be effectively mitigated through resistance-based physical activity. With compliance at approximately 40% for home-based exercise prescriptions, implementing a remote sensing system would help patients and clinicians to better understand treatment progress and increase compliance. The inclusion of end users in the development of mobile apps for remote-sensing systems can ensure that they are both user friendly and facilitate compliance. With advancements in natural language processing (NLP), there is potential for these methods to be used with data collected through the user-centered design process. Objective: This study aims to develop a mobile app for a novel device through a user-centered design process with both older adults and clinicians while exploring whether data collected through this process can be used in NLP and sentiment analysis. Methods: Through a user-centered design process, we conducted semistructured interviews during the development of a geriatric-friendly Bluetooth-connected resistance exercise band app. We interviewed patients and clinicians at weeks 0, 5, and 10 of the app development. Each semistructured interview consisted of heuristic evaluations, cognitive walkthroughs, and observations. We used the Bing sentiment library for a sentiment analysis of interview transcripts and then applied NLP-based latent Dirichlet allocation (LDA) topic modeling to identify differences and similarities in patient and clinician participant interviews. Sentiment was defined as the sum of positive and negative words (each word with a +1 or −1 value). To assess utility, we used quantitative assessment questionnaires—System Usability Scale (SUS) and Usefulness, Satisfaction, and Ease of use (USE). Finally, we used multivariate linear models—adjusting for age, sex, subject group (clinician vs patient), and development—to explore the association between sentiment analysis and SUS and USE outcomes. Results: The mean age of the 22 participants was 68 (SD 14) years, and 17 (77%) were female. The overall mean SUS and USE scores were 66.4 (SD 13.6) and 41.3 (SD 15.2), respectively. Both patients and clinicians provided valuable insights into the needs of older adults when designing and building an app. The mean positive-negative sentiment per sentence was 0.19 (SD 0.21) and 0.47 (SD 0.21) for patient and clinician interviews, respectively. We found a positive association with positive sentiment in an interview and SUS score (ß=1.38; 95% CI 0.37 to 2.39; P=.01). There was no significant association between sentiment and the USE score. The LDA analysis found no overlap between patients and clinicians in the 8 identified topics. Conclusions: Involving patients and clinicians allowed us to design and build an app that is user friendly for older adults while supporting compliance. This is the first analysis using NLP and usability questionnaires in the quantification of user-centered design of technology for older adults

Amulet: a Secure Architecture for Mhealth Applications for Low-Power Wearable Devices

Interest in using mobile technologies for health-related applications (mHealth) has increased. However, none of the available mobile platforms provide the essential properties that are needed by these applications. An mHealth platform must be (i) secure; (ii) provide high availability; and (iii) allow for the deployment of multiple third-party mHealth applications that share access to an individual\u27s devices and data. Smartphones may not be able to provide property (ii) because there are activities and situations in which an individual may not be able to carry them (e.g., while in a contact sport). A low-power wearable device can provide higher availability, remaining attached to the user during most activities. Furthermore, some mHealth applications require integrating multiple on-body or near-body devices, some owned by a single individual, but others shared with multiple individuals. In this paper, we propose a secure system architecture for a low-power bracelet that can run multiple applications and manage access to shared resources in a body-area mHealth network. The wearer can install a personalized mix of third-party applications to support the monitoring of multiple medical conditions or wellness goals, with strong security safeguards. Our preliminary implementation and evaluation supports the hypothesis that our approach allows for the implementation of a resource monitor on far less power than would be consumed by a mobile device running Linux or Android. Our preliminary experiments demonstrate that our secure architecture would enable applications to run for several weeks on a small wearable device without recharging