Sensor-enabled Activity Class Recognition in Preschoolers: Hip versus Wrist Data.

Purpose: Pattern recognition approaches to accelerometer data processing have emerged as viable alternatives to cut-point methods. However, few studies have explored the validity of pattern recognition approaches in pre-schoolers; and none have compared supervised learning algorithms trained on hip and wrist data. To develop, test, and compare activity class recognition algorithms trained on hip, wrist, and combined hip and wrist accelerometer data in pre-schoolers. Methods: 11 children aged 3 - 6 y (mean age 4.8 +/- 0.9 y) completed 12 developmentally appropriate PA trials while wearing an ActiGraph GT3X+ accelerometer on the right hip and non-dominant wrist. PA trials were categorised as sedentary (SED), light activity games (LG), moderate-to-vigorous games (MVG), walking (WA), and running (RU). Random forest (RF) and support vector machine (SVM) classifiers were trained using time and frequency domain features from the vector magnitude of the raw signal. Features were extracted from 15 s non-overlapping windows. Classifier performance was evaluated using leave-one-out-cross-validation. Results: Cross-validation accuracy for the hip, wrist, and combine hip and wrist RF models was 0.80 (95% CI:0.79 - 0.82), 0.78 (95% CI:0.77-0.80), 0.82 (95% CI:0.80 - 0.83), respectively. Accuracy for Hact, Wact, and HWact SVM models was 0.81 (95% CI:0.80 - 0.83), 0.80 (95% CI:0.79-0.80), 0.85 (95% CI:0.84 - 0.86), respectively. Recognition accuracy was consistently excellent for SED (> 90%), moderate for LG, MVG, and RU (69-79%), and modest for WA (61-71%). Conclusions: Machine learning algorithms such as RF and SVM are useful for predicting PA class from accelerometer data collected in preschool children. While classifiers trained on hip or wrist data provided acceptable recognition accuracy, the combination of hip and wrist accelerometer delivered better performance. (C) 2017 American College of Sports Medicine

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