A modular, deep learning-based holistic intent sensing system tested with Parkinson's disease patients and controls.

ABSTRACT: People living with mobility-limiting conditions such as Parkinson's disease can struggle to physically complete intended tasks. Intent-sensing technology can measure and even predict these intended tasks, such that assistive technology could help a user to safely complete them. In prior research, algorithmic systems have been proposed, developed and tested for measuring user intent through a Probabilistic Sensor Network, allowing multiple sensors to be dynamically combined in a modular fashion. A time-segmented deep-learning system has also been presented to predict intent continuously. This study combines these principles, and so proposes, develops and tests a novel algorithm for multi-modal intent sensing, combining measurements from IMU sensors with those from a microphone and interpret

SUBMITTER: Russell J 

PROVIDER: S-EPMC10646321 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature