Abstract
Abstract Human behavior recognition remains a prevalent subject of inquiry in contemporary scientific studies. Behavior recognition technology has penetrated into every aspect of our lives, mainly in video surveillance, health monitoring, and smart homes. Aiming at some people who need behavioral monitoring to prevent danger, a human behavior recognition method based on wearable devices is proposed, which first acquires the three-axis acceleration data of behavioral activities of such people through wearable devices and then performs sliding window segmentation and feature extraction on the acquired data and finally inputs the obtained features into the dual-directional long and short term memory framework (BiLSTM) model to complete the identification of human behavior. To affirm the method’s reliability, we performed activity detection tests on a dataset from UCI featuring a non-powered wearable sensor used by the senior population. The outcomes indicate that the method is proficient in identifying the routine daily activities of this demographic, demonstrating its practical utility.
Published Version
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