Abstract
Sensor-Based Human Activity Recognition (HAR) is a study of recognizing the human’s activities by using the data captured from wearable sensors. Avail the temporal information from the sensors, a modified version of random forest is proposed to preserve the temporal information, and harness them in classifying the human activities. The proposed algorithm is tested on 7 public HAR datasets. Promising results are reported, with an average classification accuracy of ~ 98%.
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Ooi, S. Y., Tan, S. C., & Cheah, W. P. (2016). Classifying human activities with temporal extension of random forest. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9950 LNCS, pp. 3–10). Springer Verlag. https://doi.org/10.1007/978-3-319-46681-1_1
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