Real-Time Human Activity Recognition using Smartphone Inertial Sensors: A Review
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Abstract
Human Activity Recognition (HAR) has become an essential field of study for today’s scenarios. Smartphone-enabled HAR is especially significant because of its practicality and affordability. In the modern world, the smartphones feature inertial sensors, including the gyroscope and accelerometer, which act as primary data inputs for HAR techniques. Smartphone-enabled HAR becomes common in applications related to health care, monitoring of physical activities, ambient assisted living, and context-aware mobile computing. This review will present some developments in HAR that utilize smartphone technologies, paying special attention to the accelerometer and gyroscope capable of sensing the body movements without hindering the user. This analysis will be focused on all stages of processing data collected by smartphones, including segmentation and motion pattern recognition. In this regard, we analyze two techniques: classical machine learning algorithms and advanced deep learning models. According to our findings, deep learning structures such as CNNs and LSTM networks provide greater recognition, accuracy and robustness than other approaches. However, some issues remain unresolved, including individual variations in human behavior and sensor noise. Several key factors such as computational complexity, latency, system robustness, and energy consumption are considered for their effect on the performance of mobile devices. Apart from discussing technical details of HAR using smartphones, this work considers practical applications of the discussed technique in unconstrained settings. Finally, the prospects in this field are discussed
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