Decomposition of complex movements into primitives for Parkinson's disease assessment
| Autor: | Peter R. Bergethon, Erhan Bilal, Marco Cavallo, Hao Zhang, Farhan Hameed, Avner Abrami, Stephen Heisig, John Rice, Daniel R. Karlin, Paul Wacnik, Eleftheria Kyriaki Pissadaki, Stephen Amato, Kelley Erb, Vesper Ramos |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
0301 basic medicine
Parkinson's disease Activities of daily living General Computer Science Computer science Movement (music) Wearable computer Disease medicine.disease 03 medical and health sciences 030104 developmental biology 0302 clinical medicine Human–computer interaction medicine Decomposition (computer science) Leverage (statistics) Motor assessment 030217 neurology & neurosurgery |
| Zdroj: | IBM Journal of Research and Development. 62:5:1-5:11 |
| ISSN: | 0018-8646 |
| Popis: | Recent advances in technology present an important opportunity in medicine to augment episodic, expert-based observations of patients’ disease signs, obtained in the clinic, with continuous and sensitive measures using wearable and ambient sensors. In Parkinson's disease (PD), such technology-based objective measures have shown exciting potential for passively monitoring disease signs, their fluctuation, and their progression. We are developing a system to passively and continuously capture data from people with PD in their daily lives, and provide a real-time estimate of their motor functions, that is analogous to scores obtained during Part III of the human-administered Movement Disorder Society's Unified Parkinson's Disease assessment (MDS-UPDRS3). Our hypothesis is that complex human movements can be decomposed into movement primitives related to the performance of the MDS-UPDRS3 motor assessment. Toward this hypothesis, we developed a system for integrating and analyzing multiple streams of sensor data collected from volunteers executing the tasks based on the MDS-UPDRS3. In this paper, we show how we can leverage the data collected from MDS-UPDRS3 tasks to develop machine learning models that can identify movement primitives in activities of daily living. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|