A method for identifying lower limb exercise resistance state based on brain hemoglobin information

Abstract

The invention discloses a recognition method for the resistance states of lower-extremity motion on the basis of cerebral hemoglobin information. The method comprises the following steps: applying a near-infrared light brain-imaging device during an experiment to collect hemoglobin information of a cerebral cortex of a subject when the subject performs riding motion by himself in the different resistance states; as for hemoglobin concentration information of the cerebral cortex recorded at the motion starting point, utilizing the difference value between oxyhemoglobin and deoxyhemoglobin and deoxyhemoglobin as analysis parameters and respectively calculating difference value and change rate of deoxyhemoglobin during each sampling period as well as calculating difference values and averagevalue of the change rate of deoxyhemoglobin in multiple sampling periods, screening out and determining a key channel and respectively considering parameter characteristics of the key channel in multiple frequency bands; utilizing difference values between oxyhemoglobin and deoxyhemoglobin of the key channel in multiple frequency bands and the average speed of the change rate of deoxyhemoglobin asfeature vectors and adopting an ELM extreme learning machine method to test the resistance state level of the subject.

Description

technical field [0001] The invention belongs to the technical field of intelligent walking aid and rehabilitation training, in particular to a method for identifying the resistance state of lower limb movement based on brain hemoglobin information. Background technique [0002] According to statistics from the Disabled Persons' Federation, the number of disabled people in my country is about 85 million, of which 29.08% are physically disabled. There are more and more walking disabilities in the lower limbs among physically disabled people caused by stroke and traumatic brain injury. Only stroke has about 2 million new patients every year. About 70-80% of patients cannot live independently due to disability, which brings a great burden to the family and society. Therefore, the prognosis and rehabilitation of these patients with physical disabilities is very important. my country's rehabilitation prognosis training and understanding are relatively late. At present, most of th...

AI Technical Summary

Problems solved by technology

In the study of brain-computer interface systems based on EEG signals, the commonly used methods based on visual evoked potentials (VEP) and event-related potentials (P300) require additional stimulation devices to provide stimulation to generate evoked potentials, and rely on human A certain feeling (such as vision) forces the experimenter to synchronize with external stimuli. Long-term operation may easily cause visual fatigue or reduce the significance of P300 potential. The corresponding brain-computer interface operation time should not be too long

The spontaneous EEG depends on the user's spontaneous mental activity. Only a special thinking process can produce detectable brain activity. It requires a lot of training by the experimenter to generate a specific pattern of EEG, which is greatly affected by subjective factors.

Therefore, most experiments are completed under specific conditions, requiring the experimenter to concentrate, the realized actions are simple and limited, lack of naturalness and flexibility, and are not very practical

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