{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/parkinsons-disease-digital-biomarker","title":"Parkinson's Disease Digital Biomarker Discovery with Optimized Transitions and Inferred Markov Emissions","arxiv_id":"1711.04078","date":"2017-11-11","proceeding":null,"authors":["Avinash Bukkittu","Baihan Lin","Trung Vu","Itsik Pe'er"],"abstract":"We search for digital biomarkers from Parkinson's Disease by observing\napproximate repetitive patterns matching hypothesized step and stride periodic\ncycles. These observations were modeled as a cycle of hidden states with\nrandomness allowing deviation from a canonical pattern of transitions and\nemissions, under the hypothesis that the averaged features of hidden states\nwould serve to informatively characterize classes of patients/controls. We\npropose a Hidden Semi-Markov Model (HSMM), a latent-state model, emitting\n3D-acceleration vectors. Transitions and emissions are inferred from data. We\nfit separate models per unique device and training label. Hidden Markov Models\n(HMM) force geometric distributions of the duration spent at each state before\ntransition to a new state. Instead, our HSMM allows us to specify the\ndistribution of state duration. This modified version is more effective because\nwe are interested more in each state's duration than the sequence of distinct\nstates, allowing inclusion of these durations the feature vector.","url_abs":"http://arxiv.org/abs/1711.04078v1","url_pdf":"http://arxiv.org/pdf/1711.04078v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"parkinsons-disease-digital-biomarker","repo_url":"https://github.com/ab4377/dream-project","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}