Joint Bayesian Hidden Markov Model With Subject‐Specific Transitions for Wearable Sensor Data

With the rapid advancements in wearable device technologies, there is a growing interest in learning useful digital biomarkers from wearable device data as objective, low-cost, real-time alternatives to use in healthcare settings. They have the potential to facilitate disease progression monitoring, medication tailoring, and supplementing clinical trial endpoints. For example, triaxial accelerometer sensor data is promising for monitoring symptoms of movement-related diseases, such as tremors in Parkinson's disease (PD). However, existing methods for accelerometer studies based on hidden Markov models (HMM) often analyze each individual's activity data separately, leading to inefficiency and limited generalizability. This paper proposes a joint nonparametric Bayesian method that extends the hierarchical Dirichlet process autoregressive HMM (HDP-AR-HMM) to incorporate subject-specific transition parameters. This approach allows for simultaneous estimation across multiple subjects and repeated measurements, accounts for between-subject variability, and provides consistent hidden state estimation without pre-specifying the number of states. We validate our method on simulated data and show that it can achieve higher accuracy in detecting the true hidden states compared to alternative methods. We apply the method to a free-living study, the Biomarker & Endpoint Assessment to Track Parkinson's disease (BEAT-PD) DREAM Challenge CIS-PD study, to demonstrate its utility in monitoring disease symptoms in PD patients.