Hidden Markov model-based activity recognition for toddlers

Abstract

Abstract Objective: Physical activity has been shown to impact future health outcomes in adults, but little is known about the long-term impact of physical activity in toddlers. Accurately measuring the specific types and amounts of physical activity in toddlers will help us to understand, predict, and better affect their future health outcomes. Although activity recognition has been extensively developed for adults as well as older children, toddlers move in ways that are significantly different from older children, indicating the need for a more tailored approach. Approach: In this study, 22 toddlers wore Actigraph waist-worn accelerometers which recorded their movements during guided play. The toddlers were videotaped and their activities were later annotated for the following eight distinct activity classes: lying down, being carried, riding in a stroller, sitting, standing, running/walking, crawling, and climbing up/down. Accelerometer data were extracted in 2 s signal windows and paired with the activities the toddlers were performing during that time interval. Main results: A variety of classifiers were tuned to a validation set. A random forest classifier was found to achieve the highest accuracy of 63.8% in a test set. To improve the accuracy, a hidden Markov model (HMM) was applied by providing the predictions of the static classifiers as observations. The HMM was able to improve the accuracy to 64.8% with all five classifiers increasing the accuracy an average of 1.3% points (95% confidence interval  =  0.7–1.9, p   <  0.01). When the three most misclassified activities (sitting, standing, and riding in a stroller) were collapsed together, the accuracy increased to 79.3%. Significance: Further refinement of the toddler activity recognition classifier will enable more accurate measurements of toddler activity and improve future health outcomes of toddlers.