Active Machine Learning for Heterogeneity Activity Recognition Through Smartwatch Sensors
Sidra Abbas, Shtwai Alsubai, Muhammad Ibrar–ul–Haque, Gabriel Avelino Sampedro, Ahmad S. Almadhor, Abdullah Al Hejaili, Iryna Ivanochko · IEEE Access · 2024
Smartwatches with cutting-edge sensors are becoming commonplace in our daily lives. Despite their widespread use, it can be challenging to interpret accelerometer and gyroscope data efficiently for Human Activity Recognition (HAR). An effective remedy is the incorporation of active learning strategies. This study explores this junction, intending to maximize the use of smartwatch technology across a range of applications. The previous research on the dataset used in our article did not provide results with a higher accuracy, which could make it difficult to make predictions. This paper proposes a novel approach to predict human activity from the Heterogeneity human activity recognition (HHAR) dataset that joins active learning with machine learning models: Random Forest (RF),Extreme Gradient Boosting (XGBoost), K-nearest Neighbors (KNN), Decision Tree (DT), Gradient Boosting (GB) and Light Gradient Boosting Machine (LGBM) classifier to predict heterogeneous activities accurately. We evaluated our approach to these models on the HHAR dataset that was generated using an accelerometer and gyroscope that were present in smartwatches. The dataset was evaluated on 3 iterations; the evaluation measures demonstrated that we can predict human activity with the highest accuracy and F1-Score of 99.99%. The results indicate that this approach is the most accurate and effective compared to the conventional machine learning approaches.