Development of a multi-wear-site, deep learning-based physical activity intensity classification algorithm using raw acceleration data.

BACKGROUND: Accelerometers are widely adopted in research and consumer devices as a tool to measure physical activity. However, existing algorithms used to estimate activity intensity are wear-site-specific. Non-compliance to wear instructions may lead to misspecifications. In this study, we developed deep neural network models to classify device placement and activity intensity based on raw acceleration data. Performances of these models were evaluated by making comparisons to the ground truth and results derived from existing count-based algorithms. METHODS: 54 participants (26 adults 26.9±8.7 years; 28 children, 12.1±2.3 years) completed a series of activity tasks in a laboratory with accelerometers attached to each of their hip, wrist, and chest. Their metabolic rates at rest and during activity periods were measured using the portable COSMED K5; data were then converted to metabolic equivalents, and used as the ground truth for activity intensity. Deep neutral networks using the Long Short-Term Memory approach were trained and evaluated based on raw acceleration data collected from accelerometers. Models to classify wear-site and activity intensity, respectively, were evaluated. RESULTS: The trained models correctly classified wear-sites and activity intensities over 90% of the time, which outperformed count-based algorithms (wear-site correctly specified: 83% to 85%; wear-site misspecified: 64% to 75%). When additional parameters of age, height and weight of participants were specified, the accuracy of some prediction models surpassed 95%. CONCLUSIONS: Results of the study suggest that accelerometer placement could be determined prospectively, and non-wear-site-specific algorithms had satisfactory accuracies. The performances, in terms of intensity classification, of these models also exceeded typical count-based algorithms. Without being restricted to one specific wear-site, research protocols for accelerometers wear could allow more autonomy to participants, which may in turn improve their acceptance and compliance to wear protocols, and in turn more accurate results.

Feasibility of a Chest-worn accelerometer for physical activity measurement.

OBJECTIVES: A "proof-of-concept" study to examine the feasibility of wearing an Actigraph GT3X+ at the Chest (resembling a neck pendant) for physical activity measurement. DESIGN: A convenience sample of 45 healthy adults (23 male, mean age 20.0±4.5 years) was included in data analysis. METHODS: Participants simultaneously wore three GT3X+ accelerometers, on the Waist, Wrist, and Chest and completed 8 bouts of slow (.67, .89, 1.11m/s), average (1.33, 1.56, 1.78m/s) and fast (2.00, 2.22m/s) walking on a treadmill. Paired t-test, correlations and absolute percentage errors (APE) of accelerometer output (vector magnitude, VM) were computed for the key pairs: Waist-Wrist; and Waist-Chest. RESULTS: The Wrist-site overestimated VM to a greater extent at all speeds in comparison to the Chest. Pearson's r correlations were weaker for Waist-Wrist (<.80) in comparison to the Waist-Chest (>.85). The APE's were much lower (i.e. higher agreement) for the Chest (9.23-15.5%) compared to the Wrist (19.7-54.9%). Participants also felt the Chest-site was more acceptable than the Waist-site. CONCLUSIONS: PA measurements recorded by a Chest worn GT3X+ more closely resembled PA measurements recorded at the traditional Waist site than when compared to the Wrist site. When combined with high Chest site preference, the findings of our study suggest that the Chest is a feasible site for accelerometer wear.