Purpose—State-of-the-art methods for recognizing human activity using raw data from body worn accelerometers have primarily been validated with data collected from adults. This study applies a previously available method for activity classification using wrist or ankle accelerometer to work on datasets collected from both adults and youth.

Methods—An algorithm for detecting activity from wrist-worn accelerometers, originally developed using data from 33 adults, is tested on a dataset of 20 youth (age 13±1.3). The algorithm is also extended by adding new features required to improve performance on the youth dataset. Subsequent tests on both the adult and youth data were performed using crossed tests (training on one group and testing on the other) and leave-one-subject-out cross-validation.

Results—The new feature set improved overall recognition using wrist data by 2.3% for adults and 5.1% for youth. Leave-one-subject-out cross-validation accuracy performance was 87.0% (wrist) and 94.8% (ankle) for adults, and 91.0% (wrist) and 92.4% (ankle) for youth. Merging the two datasets, overall accuracy was 88.5% (wrist) and 91.6% (ankle).

Conclusions—Previously available methodological approaches for activity classification in adults can be extended to youth data. Including youth data in the training phase and using features designed to capture information on the peculiar activity fragmentation of young participants allows a better fit of the methodological framework to the characteristics of activity in youth, improving its overall performance. The proposed algorithm differentiates ambulation from sedentary activities that involve gesturing in wrist data, such as that being collected in large surveillance studies.

Activity classification; activity in children; wearable sensor; inertial sensor; leave-one-subject-out cross-validation; energy expenditure.