Identifying Free-Living Physical Activities Using Lab-Based Models with Wearable Accelerometers.

ABSTRACT: The purpose of this study was to classify, and model various physical activities performed by a diverse group of participants in a supervised lab-based protocol and utilize the model to identify physical activity in a free-living setting. Wrist-worn accelerometer data were collected from ( N = 152 ) adult participants; age 18⁻64 years, and processed the data to identify and model unique physical activities performed by the participants in controlled settings. The Gaussian mixture model (GMM) and the hidden Markov model (HMM) algorithms were used to model the physical activities with time and frequency-based accelerometer features. An overall model accuracy of 92.7% and 94.7% were achieved to classify 24 physical activities using GMM and HMM, respectively. The most accurate model was then u

SUBMITTER: Dutta A 

PROVIDER: S-EPMC6263387 | biostudies-literature | 2018 Nov

REPOSITORIES: biostudies-literature