Evaluation of the SENS Motion Activity Algorithm to Classify Postures and Movement Among Children Aged 3–14 Years

Aim : To evaluate the criterion validity of the SENS Motion Activity algorithm to classify postures and movements among children aged between 3 and 14 years in a laboratory setting by comparing with human-coded video. Method : Data were collected on 48 Australian children who attended a structured ∼1 hr data collection session at a laboratory with their caregivers. The session was video recorded, and thigh acceleration was measured using a SENS accelerometer. Data from the accelerometer were processed and classified into four postures and movements using the SENS algorithm. Human-coded video provided the reference standard to calculate the performance metrics sensitivity, specificity, precision, F1-score, and balanced accuracy. Results : Overall, the SENS Motion Activity algorithm classified postures and movements with performance metrics F1-score of 73% and balanced accuracy of 87%. Lying/sitting had the highest F1-score and balanced accuracy (99%). Standing, walking, and running had somewhat lower performance metrics (F1-score and balanced accuracy of 69%–77%, 57%–92%, and 68%–82%, respectively). Difficulties with human coding likely contributed to lower performance. A higher balanced accuracy was found for boys compared with girls for running. The algorithm had better performance for children older than 11 years compared with younger children overall and for walking and running. Conclusion : Our results suggest that the SENS Motion Activity algorithm can provide accurate measurements for detecting lying/sitting, standing, walking, and running among children. Further research could usefully explore algorithm development for classifying movement in sitting and standing postures and brief sporadic movements, especially among children younger than 11 years.