Wearable human upper limb muscle fatigue detection and training system based on multi-sensor data fusion

Abstract

The invention discloses a wearable human upper limb muscle fatigue detection and training system based on multi-sensor data fusion. The system comprises a signal acquisition module, a data processingmodule, an alarm module and a human-computer interaction module. A surface electromyography sensor, a muscular sound sensor and a blood oxygen saturation sensor constitute a multi-sensor acquisition array which are worn on that measured upper limb part, and data fusion is carry out based on a weighted average method to synthetically calculate the muscle fatigue parameters of the upper limb, thereby greatly improving the detection accuracy of the muscle fatigue of the upper limb. If the parameter reaches the preset value, the system can give the fatigue alarm reminder. The system also has the function of muscle strength training, By collecting surface electromyography signals and muscle tone signals to judge human upper limb movements and muscle strength, and real-time monitoring muscle fatigue status, using wireless transmission mode to upload data to the human-computer interaction module, matching with the virtual game, make the upper limb muscle strength training process more interesting, and effectively improve its training effect.

Description

technical field [0001] The invention relates to the field of human muscle fatigue detection and training, in particular to a wearable human upper limb muscle exercise fatigue detection and training system based on multi-sensor data fusion. Background technique [0002] To a certain extent, stroke patients can achieve full recovery through moderate exercise and reasonable exercise, but stroke patients are more prone to muscle fatigue than ordinary people during exercise rehabilitation training, and most patients have impaired central nervous system function at the training site During the training, the patient's brain cannot obtain feedback information about muscle activity in time. With the aggravation of fatigue, the muscle tension will increase significantly, causing serious consequences such as spasms and strains, which may easily cause secondary damage to human muscles; in sports In terms of competition, in order to improve the performance of athletes during training, ex...

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Problems solved by technology

[0003] The current research on upper limb muscle fatigue has found that local muscle activity can be detected by collecting muscle sound signals through pressure sensors, condenser microphones, displacement sensors, etc., and then judge the different degrees of muscle fatigue. The movement of muscle fibers and the vibration of the muscle surface can be observed through the mechanical vibration wave generated by the MMG; the existing wireless surface electromyography instrument is equipped with signal processing software, which can store and display the surface electromyography signal, and simultaneously There are data processing and analysis functions such as wavelet transform, multiple filters, and fast Fourier transform, but most of them do not have the function of muscle fatigue analysis. In addition, studies have shown that when the measured skin is not clean or sweating, the surface EMG signal cannot Sufficient for muscle fatigue analysis

[0004] To sum up, quantitative analysis of human muscle exercise fatigue detection and research on wearable devices are still at a relatively early stage. Products on the domestic and foreign markets mainly have two important features: one is to collect a single original signal of muscle state, After the post-processing and analysis of the data on the PC side, the results are fed back to the experimenter, but the noise increases in extreme situations such as exercise sweating, high-speed and high-frequency limb movement, and cannot accurately reflect the fatigue state of muscle movement; the other is The original signal of the muscle state is extracted through complex equipment, without the analysis of the physiological characteristics contained, and the combination of rehabilitation medicine and control engineering methods is not well combined, and the exercise fatigue state of human muscles cannot be accurately evaluated in real time.

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