Multiparameter collection system for assisting grip strength ball rehabilitation training

Abstract

The invention provides a multiparameter collection system for assisting stress ball rehabilitation training. The system includes a control module, a Bluetooth module and a signal collection module, wherein the Bluetooth module and the signal collection module are connected with the control module; the Bluetooth module is further connected with a display module, and a power source module is connected with the signal collection module, the control module and the Bluetooth module separately; the signal collection module consists of a grip strength collection unit, a surface myoelectricity collection unit and a finger curvature collection unit. According to the system, collection gloves can be worn on the hands of a patient, correlative data of the muscles of the hands and the upper limbs canbe collected through the signal collection module, and the collected data is transmitted to the display module for display and storage under processing of the control module. By means of the system, the grip strength ball rehabilitation training effect can be accurately and objectively evaluated, and quantitative researching is conducted on training load.

Description

technical field [0001] The invention relates to the medical field, in particular to a grip strength, surface myoelectricity, and finger curvature collection system for assisting rehabilitation grip ball training. Background technique [0002] The recovery of upper limb motor function in stroke patients mainly depends on rehabilitation therapy. During the rehabilitation period, the detection of grip strength can provide the most direct feedback information for rehabilitation therapy and improve the treatment method. Lin Miaojun et al. conducted a study on boot-shaped sling suspension combined with grip ball training to treat limb swelling after distal radius fracture surgery. By allowing patients to perform active finger movements with grip balls on the basis of traditional rehabilitation methods, they found that supplementary grip training can accelerate the recovery of limb swelling. The recovery progress of the patient's side limb. However, general rehabilitation grip bal...

AI Technical Summary

Problems solved by technology

During the traditional detection of grip strength, myoelectricity, and finger curvature of patients, doctors cannot obtain time-axis-matched grip strength, myoelectricity, and finger curvature signals because the detection is often performed separately. rehabilitation of

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