Wearable system for quantitative detection of major symptoms in Parkinson's patients

Abstract

The invention provides a wearable system and method for cardinal symptom quantitative detection of Parkinson patients. The system comprises a glove and a computer, wherein the glove and the computer are connected through wires or in a wireless mode. The glove further comprises a wrist module and a fingertip module. The wrist module and the fingertip module are worn on the fingertips and the wrist of a patient respectively. A tremor detection module, a tremor amplitude steady state detection module, a muscular rigidity detection module and a slow movement symptom detection module are arranged in the computer and are used for tremor detection, tremor amplitude steady state detection, muscular rigidity detection and slow movement symptom detection of the Parkinson patients and displaying detection results. For the situation that the symptom judgment of different neurologists for the same patient is different, the system has the uniform quantitative detection standard. Detection of three main symptoms is integrated in one system, and the system can provide complete and comprehensive illness state detection.

Description

technical field [0001] The invention relates to a technology for quantitative detection of main symptoms of Parkinson's patients, in particular to a wearable system and detection method for quantitative detection of three main symptoms of Parkinson's patients. Background technique [0002] Parkinson's disease is a chronic degenerative disorder of the central nervous system. Tremor, muscle stiffness, and slowness of movement are the three main symptoms of Parkinson's disease. There are more and more treatments, but the severity of Parkinson's symptoms and the effect of treatment are mainly diagnosed by neurologists based on experience and feelings. [0003] MEMS (Micro-Electro-Mechanical Systems) refers to a complete micro-electro-mechanical system that integrates mechanical elements, micro-sensors, signal processing, communication interfaces and power supplies. MEMS inertial sensing technology can realize the detection and measurement of acceleration, tilt, shock, vibratio...

AI Technical Summary

Problems solved by technology

However, there are some problems in this system that need to be resolved: the commercial products currently used for the quantitative detection of Parkinson's disease symptoms are only aimed at a single or certain symptoms, especially the quantitative evaluation of muscle stiffness symptoms is currently less; The embedding and integration still need to be optimized, because different patients need to wear gloves of different sizes; the measurement method of muscle stiffness and the sensor fusion method are not ideal enough, and the influence of fluctuations in tremor symptoms on the measurement results is less considered, and the practicability of the system remains to be seen. Further improve

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