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A typical fall detection system (FDS) is shown in Figure, which consists of three main components: data capturing, fall detection, and communication components. The fall detection component is the most important in the system that requires high accuracy and reliability and real-time processing ability.
The daily activities including abnormal behaviors such as falls are commonly recognized in a framework, namely Human Activity Recognition (HAR),
In this study, we focus on data preprocessing and Apply PSO to improve pedestrian fall detection rate using wearable sensor data.
We propose a data extraction method based on the time domain, frequency domain, and Hjorth parameter to build a dataset of 44 features of accelerometer data.
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IEEE/ICACT20220120 Slide.07
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