Abstract
Posture recognition has been widely applied in fields such as physical training, environmental awareness, human-computer-interaction, surveillance system and elderly health care. The traditional methods consist of two main variations: machine vision methods and acceleration sensor methods. The former has the disadvantages of privacy invasion, high cost and complex implementation processes, while the latter has low recognition rate for still postures. A new body posture recognition scheme based on indoor positioning technology is presented in this paper. A single deployed indoor positioning system is constructed by installing wearable receiving tags at key points of the human body. The distance measurement method with ultra-wide band (UWB) radio is applied to position the key points of human body. Posture recognition is implemented by positioning. In the posture recognition algorithm, least square estimation (LSE) method and the improved extended Kalman filtering (iEKF) algorithm are respectively adopted to suppress the noise of the distances measurement and to improve the accuracy of positioning and recognition. The comparison of simulation results with the two methods shows that the improved extended Kalman filtering algorithm is more effective in error performance.
Highlights
Human posture recognition is an attractive and challenging topic due to its wide range of applications, e.g., smart home environments for the monitoring of physical activity levels, assessment of recovery phases of living independently, and detection of accidental falls in elderly people [1].Among these applications, the most important one is the elderly health care due to the population aging in the 21st century
In the posture recognition algorithm, least square estimation (LSE) method and the improved extended Kalman filtering algorithm are respectively adopted to suppress the noise of the distances measurement and to improve the accuracy of positioning and recognition
The comparison of simulation results with the two methods shows that the improved extended Kalman filtering algorithm is more effective in error performance
Summary
Human posture recognition is an attractive and challenging topic due to its wide range of applications, e.g., smart home environments for the monitoring of physical activity levels, assessment of recovery phases of living independently, and detection of accidental falls in elderly people [1]. Among these applications, the most important one is the elderly health care due to the population aging in the 21st century. According to the US population report, the aged population (over 65 years old) reached more than 50 million in 2017 [2], which represented 15.41% of the US population. Health care for the elderly has become a major concern
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