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Gait velocity and stride length are critical health indicators for older adults. A decade of medical research shows that they provide a predictor of future falls, hospitalization, and functional decline among seniors. However, currently these metrics are measured only occasionally during medical visits. Such infrequent measurements hamper the opportunity to detect changes and intervene early in the impairment process.

Insomnia is the most prevalent sleep disorder in the US. In-home insomnia monitoring is important for both diagnosis and treatment. Existing solutions, however, require the user to either maintain a sleep diary or wear a sensor while sleeping. Both can be quite cumbersome. This paper introduces EZ-Sleep, a new approach for monitoring insomnia and sleep. EZ-Sleep has three properties. First, it is zero effort, i.e., it neither requires the user to wear a sensor nor to record any data. It monitors the user remotely by analyzing the radio signals that bounce off her body. Second, it delivers new features unavailable with other devices such as automatically detecting where the user sleeps and her exact bed schedule, while simultaneously monitoring multiple users in different beds. Third, it is highly accurate. Its average error in measuring sleep latency and total sleep time is 4.9 min and 10.3 min, respectively.

No abstract

We conducted a pilot study using a passive radio-wave-based home monitor in individuals with Parkinson disease (PD) with a focus on gait, home activity, and time in bed. We enrolled 7 ambulatory individuals to have the device installed in the bedroom of their homes over 8 weeks and performed standard PD assessments at baseline. We evaluated the ability of the device to objectively measure gait and time in bed and to generate novel visualizations of home activity. We captured 353 days of monitoring. Mean gait speed (0.39–0.78 m/s), time in bed per day (4.4–12.1 h), and number (1.4–5.9) and duration (15.0–49.8 min) of nightly awakenings varied substantially across and within individuals. Derived gait speed correlated well with the Movement Disorder Society-Unified Parkinson’s Disease Rating Scale total (r = –0.88, p = 0.009) and motor sub-score (r = –0.95, p = 0.001). Six of the seven participants agreed that their activity was typical and indicated a willingness to continue monitoring. This technology provided promising new insights into the home activities of those with PD and may be broadly applicable to other chronic conditions.