{"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-assessment-from-a-wrist","title":"Parkinson's Disease Assessment from a Wrist-Worn Wearable Sensor in Free-Living Conditions: Deep Ensemble Learning and Visualization","arxiv_id":"1808.02870","date":"2018-08-08","proceeding":null,"authors":["Terry Taewoong Um","Franz Michael Josef Pfister","Daniel Christian Pichler","Satoshi Endo","Muriel Lang","Sandra Hirche","Urban Fietzek","Dana Kulić"],"abstract":"Parkinson's Disease (PD) is characterized by disorders in motor function such\nas freezing of gait, rest tremor, rigidity, and slowed and hyposcaled\nmovements. Medication with dopaminergic medication may alleviate those motor\nsymptoms, however, side-effects may include uncontrolled movements, known as\ndyskinesia. In this paper, an automatic PD motor-state assessment in\nfree-living conditions is proposed using an accelerometer in a wrist-worn\nwearable sensor. In particular, an ensemble of convolutional neural networks\n(CNNs) is applied to capture the large variability of daily-living activities\nand overcome the dissimilarity between training and test patients due to the\ninter-patient variability. In addition, class activation map (CAM), a\nvisualization technique for CNNs, is applied for providing an interpretation of\nthe results.","url_abs":"http://arxiv.org/abs/1808.02870v1","url_pdf":"http://arxiv.org/pdf/1808.02870v1.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-assessment-from-a-wrist","repo_url":"https://github.com/terryum/Deep_Ensemble_CNN_for_Imbalance_Labels","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"ensemble-learning","task_name":"Ensemble Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}