Title: Parkinson's Disease Digital Biomarker Discovery with Optimized Transitions and Inferred Markov Emissions,
Abstract: We search for digital biomarkers from Parkinson's Disease by observing
approximate repetitive patterns matching hypothesized step and stride periodic
cycles. These observations were modeled as a cycle of hidden states with
randomness allowing deviation from a canonical pattern of transitions and
emissions, under the hypothesis that the averaged features of hidden states
would serve to informatively characterize classes of patients/controls. We
propose a Hidden Semi-Markov Model (HSMM), a latent-state model, emitting
3D-acceleration vectors. Transitions and emissions are inferred from data. We
fit separate models per unique device and training label. Hidden Markov Models
(HMM) force geometric distributions of the duration spent at each state before
transition to a new state. Instead, our HSMM allows us to specify the
distribution of state duration. This modified version is more effective because
we are interested more in each state's duration than the sequence of distinct
states, allowing inclusion of these durations the feature vector.