Exoskeleton-oriented gait phase classification method based on TCN-HMM

A classification method and exoskeleton technology, applied in the direction of appliances that help people walk, biological neural network models, sensors, etc., can solve the problems of being easily disturbed, easy to wear, etc., and achieve high stability

Pending Publication Date: 2021-03-12
HANGZHOU DIANZI UNIV
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At the same time, in order to solve the problems of easy wear and interference in many current human body motion signal acquisition devices, the present invention adopts a

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[0046] In order to make the purpose, technical solution and main points of the present invention clearer, the following will further describe in detail the embodiments of the present invention in conjunction with the accompanying drawings.

[0047] Such as figure 1 As shown, a TCN-HMM-based gait phase classification method for exoskeleton, including the following steps:

[0048] Step 1. IMU Data Acquisition

[0049] Fix five IMU sensors on the subject's waist, left thigh, left calf, right thigh, and right calf; the IMU sensor on the thigh is fixed about 6 cm above the knee joint on the side of the thigh, and the IMU sensor on the lower leg is Fix it at about 10cm above the ankle joint on the side of the calf. In order to facilitate labeling of IMU signals with different gait phases, the subjects were also asked to wear pressure shoes with plantar pressure sensors. The pressure shoes can detect the sole pressure and heel pressure; the subjects walk in a straight line at a sp...

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Abstract

The invention discloses an exoskeleton-oriented gait phase classification method based on a TCN-HMM. The method comprises the following specific steps: 1, acquiring IMU data; 2, preprocessing the gaitdata acquired in the step 1; 3, constructing a training set and a test set; 4, constructing a hybrid TCN-HMM model, 5, training the hybrid TCNHMM model by using the training set, and 6, classifying the new walking IMU data by using the trained hybrid TCNHMM model. According to the method, the posterior probability of the state is obtained by innovatively utilizing the TCN network, and the emission probability required by the HMM model is obtained by utilizing the posterior probability, so that the hybrid TCNHMM model is formed, and the model organically combines the time characteristics and the space characteristics of the motion data together and discriminates the gait phase information. According to the method, a gait phase classification result with very high accuracy is obtained, andmeanwhile, wrong classification is inhibited.

Description

technical field [0001] The invention belongs to the field of lower limb exoskeleton human-machine collaborative motion control, and relates to a human walking gait phase classification method based on a hybrid TCN (Temporal Convolutional Networks) and HMM (Hidden Markov Model) model. Background technique [0002] In recent years, exoskeleton robots have become emerging technologies in medical, life, industrial and military applications. Among them, the lower extremity exoskeleton has shown great value. It perfectly combines human intelligence and the "physical strength" of robots, and has broad application prospects in the fields of assisting, assisting the elderly, assisting the disabled, and the military. [0003] Gait phase classification is a general method for analyzing walking motion, and accurate classification of different gait phases is crucial for controlling lower extremity exoskeletons and detecting user intent. Current gait phase recognition methods can general...

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Application Information

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IPC IPC(8): G06K9/00G06K9/62G06N3/04A61B5/103A61B5/11A61H3/00
CPCA61B5/112A61B5/7267A61B5/1038A61B5/6807A61B5/725A61H3/00A61H2201/5071A61H2201/1659G06V40/25G06N3/045G06F18/214
Inventor 孔万增王雪岩王伟富
Owner HANGZHOU DIANZI UNIV
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