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Treatment for multiple sclerosis (MS) focuses on managing its symptoms (e.g., depression, fatigue, poor sleep quality), varying with specific symptoms experienced. Thus, for optimal treatment, there arises the need to track these symptoms. Towards this goal, there is great interest in finding their relevant phenotypes. Prior research suggests links between activities of daily living (ADLs) and MS symptoms; therefore, we hypothesize that the behavioral phenotype (revealed through ADLs) is closely related to MS symptoms. Traditional approaches to finding behavioral phenotypes which rely on human observation or controlled clinical settings are burdensome and cannot account for all genuine ADLs. Here, we present MSLife, an end-to-end, burden-free approach to digital behavioral phenotyping of MS symptoms in the wild using wearables and graph-based statistical analysis. MSLife is built upon (1) low-cost, unobtrusive wearables (i.e., smartwatches) that can track and quantify ADLs among MS patients in the wild; (2) graph-based statistical analysis that can model the relationships between quantified ADLs (i.e., digital behavioral phenotype) and MS symptoms. We design, implement, and deploy MSLife with 30 MS patients across a one-week home-based IRB-approved clinical pilot study. We use the GENEActiv smartwatch to monitor ADLs and clinical behavioral instruments to collect MS symptoms. Then we develop a graph-based statistical analysis framework to model phenotyping relationships between ADLs and MS symptoms, incorporating confounding demographic factors. We discover 102 significant phenotyping relationships (e.g., later rise times are related to increased levels of depression, history of caffeine consumption is associated with lower fatigue levels, higher relative levels of moderate physical activity are linked with decreased sleep quality). We validate their healthcare implications, using them to track MS symptoms in retrospective analysis. To our best knowledge, this is one of the first practices to digital behavioral phenotyping of MS symptoms in the wild. 

Using wireless signals to monitor human vital signs, especially heartbeat information, has been intensively studied in the past decade. This non-contact sensing modality can drive various applications from cardiac health, sleep, and emotion management. Under the circumstance of the COVID-19 pandemic, non-contact heart monitoring receives increasingly market demands. However, existing wireless heart monitoring schemes can only detect limited heart activities, such as heart rate, fiducial points, and Seismocardiography (SCG)-like information. In this paper, we present CardiacWave to enable a non-contact high-definition heart monitoring. CardiacWave can provide a full spectrum of Electrocardiogram (ECG)-like heart activities, including the details of P-wave, T-wave, and QRS complex. Specifically, CardiacWave is built upon the Cardiac-mmWave scattering effect (CaSE), which is a variable frequency response of the cardiac electromagnetic field under the mmWave interrogation. The CardiacWave design consists of a noise-resistant sensing scheme to interrogate CaSE and a cardiac activity profiling module for extracting cardiac electrical activities from the interrogation response. Our experiments show that the CardiacWave-induced ECG measures have a high positive correlation with the heart activity ground truth (i.e., measurements from a medical-grade instrument). The timing difference of P-waves, T-waves, and QRS complex is 0.67%, 0.71%, and 0.49%, respectively, and a mean cardiac event difference is within a delay of 5.3 milliseconds. These results indicate that CaridacWave offers high-fidelity and integral heart clinical characteristics. Furthermore, we evaluate the CardiacWave system with participants under various conditions, including heart and breath rates, ages, and heart habits (e.g., tobacco use).