Deep neural networks for wearable sensor-based activity recognition in Parkinson's disease: investigating generalizability and model complexity.

ABSTRACT:

Background

The research gap addressed in this study is the applicability of deep neural network (NN) models on wearable sensor data to recognize different activities performed by patients with Parkinson's Disease (PwPD) and the generalizability of these models to PwPD using labeled healthy data.

Methods

The experiments were carried out utilizing three datasets containing wearable motion sensor readings on common activities of daily living. The collected readings were from two accelerometer sensors. PAMAP2 and MHEALTH are publicly available datasets collected from 10 and 9 healthy, young subjects, respectively. A private dataset of a similar nature collected from 14 PwPD patients was utilized as well. Deep NN models were implemented with varying levels of complexity to invest

SUBMITTER: Davidashvilly S 

PROVIDER: S-EPMC10858599 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature