A fall detection method and device based on multiple sequential probability ratio test

Abstract

The invention discloses a fall detection method and device based on a multi-sequential probability ratio test, and relates to the technical field of medical devices. Including the upper limb force sensor and the lower limb ultrasonic sensor array, the upper limb force signal obtains the velocity response signal through the identified admittance control model, the lower limb ultrasonic sensor array solves the real-time coordinates of the user's movement, and then obtains the displacement, which is carried out by the Kalman filter algorithm. Multi-sensor data fusion, and finally the fused data is tested based on multiple sequential probability ratios to determine whether the user falls. The invention solves the fall detection problem of the walker, reduces the data processing amount of the processor, and the multi-sequential probability ratio test algorithm solves the singleness and delay of the sequential probability ratio test (SPRT). It has a faster detection speed and improves the safety of the use of the walker.

Description

technical field [0001] The invention relates to the technical field of medical devices, in particular to a fall detection method and device based on multiple sequential probability ratio tests. Background technique [0002] my country is the country with the largest population of elderly people, and the number of elderly people will continue to grow in the future. The elderly usually suffer from muscle atrophy, osteoporosis, vision and hearing loss, and related diseases caused by organ degeneration, making it difficult for the elderly to perform basic daily activities alone, causing a lot of inconvenience. A smart walker is a medical device that can help the elderly to carry out daily walking and recovery exercise. Currently, there are few walkers with fall detection function, and most of them use visual sensors and wearable sensors to detect falls. [0003] The visual sensor-based fall detection method uses a depth camera to obtain real-time images of the user while walkin...

AI Technical Summary

Problems solved by technology

[0003] The fall detection method based on the visual sensor is to use the depth camera to obtain real-time images of the user while walking, and to recognize the posture of the human body through neural network and other related image algorithms to determine whether a fall has occurred. The disadvantage of this method is that the related algorithms are relatively complicated. The processor requirements are high, and the camera is easily interfered by factors such as light; the fall detection method based on wearable sensors needs to install a posture sensor on the user to collect motion signals to identify the user's posture. The disadvantage of this method is that the user must wear a specific The sensor increases the difficulty of using the device, and the user's comfort is poor

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