Smartphone-Based Activity Recognition Using Multistream Movelets Combining Accelerometer and Gyroscope Data.

ABSTRACT: Physical activity patterns can reveal information about one's health status. Built-in sensors in a smartphone, in comparison to a patient's self-report, can collect activity recognition data more objectively, unobtrusively, and continuously. A variety of data analysis approaches have been proposed in the literature. In this study, we applied the movelet method to classify the activities performed using smartphone accelerometer and gyroscope data, which measure a phone's acceleration and angular velocity, respectively. The movelet method constructs a personalized dictionary for each participant using training data and classifies activities in new data with the dictionary. Our results show that this method has the advantages of being interpretable and transparent. A unique aspect of our move

SUBMITTER: Huang EJ 

PROVIDER: S-EPMC9002497 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature