Jeffrey Kaye

Rajib Rana, Daniel Austin, Peter G. Jacob et al.

Gait velocity has been consistently shown to be an important indicator and predictor of health status, especially in older adults. Gait velocity is often assessed clinically, but the assessments occur infrequently and thus do not allow optimal detection of key health changes when they occur. In this paper, we show the time it takes a person to move between rooms in their home denoted 'transition times' can predict gait velocity when estimated from passive infrared motion detectors installed in a patient's own home. Using a support vector regression approach to model the relationship between transition times and gait velocities, we show that velocity can be predicted with an average error less than 2.5 cm/sec. This is demonstrated with data collected over a 5 year period from 74 older adults monitored in their own homes. This method is simple and cost effective, and has advantages over competing approaches such as: obtaining 20 to100x more gait velocity measurements per day, and offering the fusion of location specific information with time stamped gait estimates. These advantages allow stable estimates of gait parameters (maximum or average speed, variability) at shorter time scales than current approaches. This also provides a pervasive in home method for context aware gait velocity sensing that allows for monitoring of gait trajectories in space and time.

Rameen Mahmood, Omar El Shahawy, Souptik Barua et al.

Human behavior is difficult to observe continuously at scale, yet it leaves measurable traces in everyday device use. We test whether encrypted smartphone network traffic -- a ubiquitous, always-on, passive sensing modality -- can passively capture behavioral patterns related to sleep, stress, and loneliness. We model shared behavioral structure using a transformer backbone with per-user adapters, allowing the model to represent both typical individual behavior and deviations from it. To make these representations interpretable, we apply a sparse autoencoder to extract behavioral features corresponding to distinct patterns of activity. We relate these features to sleep disturbance, stress, and loneliness using generalized estimating equations with Mundlak decomposition, separating between-person differences from within-person changes over time. We find that the three outcomes reflect distinct temporal structures: stress is primarily associated with stable between-person differences, loneliness with within-person variation, and sleep disturbance with a combination of both. Notably, these within-person dynamics are not captured by predefined network-traffic features, demonstrating the value of learned representations for longitudinal behavioral sensing. These results establish encrypted network traffic as a viable passive sensing modality, revealing interpretable behavioral dynamics -- particularly deviations from an individual's baseline -- that are not visible in raw traffic features.