![]() ![]() For this purpose, video-based or live behavior coding is a frequently used method. In behavioural sciences, the objective and quantifiable measurement of behavior is a fundamental requirement for conducting research. In line with previous findings, our results provide a firm basis for a more precise and effective recognition system that can make human behavioral analysis faster and more objective. In summary, the LGBM is a very promising solution for HAR. The window size had no significant effect. Seventeen activities out of 40 were successfully recognized with AUC values above 0.8. ![]() We used the sliding window technique during the signal processing, and we aimed at finding the best window size for the analysis of each behavior element to achieve the most effective settings. The activity recognition was a binary classification task which was evaluated with a Light Gradient Boosted Machine (LGBM) learning algorithm, a decision tree based method with a threefold cross validation. All children were typically developing first graders from three elementary schools. We analyzed the data of 34 children (19 girls, 15 boys age range: 6.59–8.38 median age = 7.47). ![]() We defined 40 activities for ML recognition, and we collected activity motion data by means of wearable smartwatches with a special SensKid software. Our main goal was to find a reliable method that could automatically detect various playful and daily routine activities in children. Human activity recognition (HAR) using machine learning (ML) methods has been a continuously developed method for collecting and analyzing large amounts of human behavioral data using special wearable sensors in the past decade. ![]()
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