Combined surface myoelectricity and near infrared spectrum acquiring device

Abstract

The invention provides a combined surface myoelectricity and near infrared spectrum acquiring device. The combined surface myoelectricity and near infrared spectrum acquiring device comprises a signal acquiring module, a signal processing module, a power module and a wearing module, wherein the signal acquiring module is used for acquiring signals; the signal processing module is used for processing the signals; by using the wearing module, the combined surface myoelectricity and near infrared spectrum acquiring device is worn on a portion for acquiring the signals; and the signals comprise surface myoelectricity signals and near infrared spectrum signals. By using the combined surface myoelectricity and near infrared spectrum acquiring device, s EMG (electromyography) signals and near infrared spectrum signals which are generated during muscle movement can be simultaneously acquired; and by integrating characteristics of the s EMG signals and characteristics of the near infrared spectrum signals, influences of interference and noise on a man-machine interface can be reduced, so that the robustness of the man-machine interface is improved.

Description

technical field [0001] The invention relates to the technical field of biomedical engineering and man-machine interface, in particular to a combined collection device of surface electromyography and near-infrared spectrum. Background technique [0002] Surface electromyography signal sEMG (surface ElectroMyoGraphy) is the electrical signal accompanying muscle contraction. It can be understood as the synthesis of electrical signals generated by multiple muscles on the surface of the human body in time and space. It is an important information for non-invasive detection of muscle activity on the body surface. sEMG signals are widely used in human-machine interface technologies, such as myoelectric prosthetics. [0003] However, the sEMG signal itself is very sensitive to interference and noise, and the crosstalk to deep muscles is more serious; the characteristics of the sEMG signal will change over time, for example, when the muscle is fatigued, the median frequency of the s...

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Problems solved by technology

[0003] However, the sEMG signal itself is very sensitive to interference and noise, and the crosstalk to deep muscles is more serious; the characteristics of the sEMG signal will change over time, for example, when the muscle is fatigued, the median frequency of the sEMG signal will decrease, and the root mean square (RMS) will increase, resulting in changes in the characteristics of the collected sEMG signal, which seriously affects the robustness of the sEMG-based human-machine interface

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