Flexible and Wearable GRF and EMG Sensors Enabled Locomotion Mode Recognition for IoHT Based In-home Rehabilitation

10.36227/techrxiv.11987994 ◽

2020 ◽

Author(s):

Chaoming Fang ◽

Yixuan Wang ◽

Shuo Gao

Keyword(s):

Sensitive Element ◽

Small Volume ◽

Peak Stress ◽

Higher Learning ◽

Force Sensing ◽

Sensing System ◽

Mode Recognition ◽

Locomotion Mode ◽

Stress Sensing ◽

Manipulation Process

In order to quantify the manipulation process of acupuncture, in this article, a piezoelectric glove based wearable stress sensing system is presented. Served as the sensitive element with small volume and high tensile resistance, PVDF greatly meet the need of quantitative analysis. Through piezoelectric force sensing glove, the system is capable of detecting both perpendicular stress as well as shear stress. Besides, key parameters including peak stress at needle are detected and extracted, potentially allowing for a higher learning efficiency hence advancing the development of acupuncture.

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A Piezoelectric Force Sensing Glove For Acupuncture Quantification

10.36227/techrxiv.12000078.v1 ◽

2020 ◽

Author(s):

Kaize Lin ◽

Jin Cao ◽

Shuo Gao

Keyword(s):

Shear Stress ◽

Quantitative Analysis ◽

Sensitive Element ◽

Small Volume ◽

Peak Stress ◽

Higher Learning ◽

Force Sensing ◽

Sensing System ◽

Stress Sensing ◽

Manipulation Process

In order to quantify the manipulation process of acupuncture, in this article, a piezoelectric glove based wearable stress sensing system is presented. Served as the sensitive element with small volume and high tensile resistance, PVDF greatly meet the need of quantitative analysis. Through piezoelectric force sensing glove, the system is capable of detecting both perpendicular stress as well as shear stress. Besides, key parameters including peak stress at needle are detected and extracted, potentially allowing for a higher learning efficiency hence advancing the development of acupuncture.

Download Full-text

A Piezoelectric Force Sensing Glove For Acupuncture Quantification

10.36227/techrxiv.12000078 ◽

2020 ◽

Author(s):

Kaize Lin ◽

Jin Cao ◽

Shuo Gao

Keyword(s):

Shear Stress ◽

Quantitative Analysis ◽

Sensitive Element ◽

Small Volume ◽

Peak Stress ◽

Higher Learning ◽

Force Sensing ◽

Sensing System ◽

Stress Sensing ◽

Manipulation Process

In order to quantify the manipulation process of acupuncture, in this article, a piezoelectric glove based wearable stress sensing system is presented. Served as the sensitive element with small volume and high tensile resistance, PVDF greatly meet the need of quantitative analysis. Through piezoelectric force sensing glove, the system is capable of detecting both perpendicular stress as well as shear stress. Besides, key parameters including peak stress at needle are detected and extracted, potentially allowing for a higher learning efficiency hence advancing the development of acupuncture.

Download Full-text

A wearable capacitive sensing system with phase-dependent classifier for locomotion mode recognition

2012 4th IEEE RAS & EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob) ◽

10.1109/biorob.2012.6290721 ◽

2012 ◽

Cited By ~ 4

Author(s):

Enhao Zheng ◽

Baojun Chen ◽

Qining Wang ◽

Kunlin Wei ◽

Long Wang

Keyword(s):

Capacitive Sensing ◽

Sensing System ◽

Mode Recognition ◽

Locomotion Mode

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Utilizing Wearable GRF and EMG Sensing System and Machine Learning Algorithms to Enable Locomotion Mode Recognition for In-home Rehabilitation

2020 IEEE International Conference on Flexible and Printable Sensors and Systems (FLEPS) ◽

10.1109/fleps49123.2020.9239563 ◽

2020 ◽

Author(s):

Chaoming Fang ◽

Yixuan Wang ◽

Shuo Gao

Keyword(s):

Machine Learning ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Sensing System ◽

Mode Recognition ◽

Locomotion Mode

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Design of Decision Tree Structure with Improved BPNN Nodes for High-Accuracy Locomotion Mode Recognition Using a Single IMU

Sensors ◽

10.3390/s21020526 ◽

2021 ◽

Vol 21 (2) ◽

pp. 526

Author(s):

Yang Han ◽

Chunbao Liu ◽

Lingyun Yan ◽

Lei Ren

Keyword(s):

Decision Tree ◽

Time Window ◽

Threshold Value ◽

Recognition System ◽

Tree Structure ◽

Classification Model ◽

Measurement Unit ◽

Local Optima ◽

Mode Recognition ◽

Locomotion Mode

Smart wearable robotic system, such as exoskeleton assist device and powered lower limb prostheses can rapidly and accurately realize man–machine interaction through locomotion mode recognition system. However, previous locomotion mode recognition studies usually adopted more sensors for higher accuracy and effective intelligent algorithms to recognize multiple locomotion modes simultaneously. To reduce the burden of sensors on users and recognize more locomotion modes, we design a novel decision tree structure (DTS) based on using an improved backpropagation neural network (IBPNN) as judgment nodes named IBPNN-DTS, after analyzing the experimental locomotion mode data using the original values with a 200-ms time window for a single inertial measurement unit to hierarchically identify nine common locomotion modes (level walking at three kinds of speeds, ramp ascent/descent, stair ascent/descent, Sit, and Stand). In addition, we reduce the number of parameters in the IBPNN for structure optimization and adopted the artificial bee colony (ABC) algorithm to perform global search for initial weight and threshold value to eliminate system uncertainty because randomly generated initial values tend to result in a failure to converge or falling into local optima. Experimental results demonstrate that recognition accuracy of the IBPNN-DTS with ABC optimization (ABC-IBPNN-DTS) was up to 96.71% (97.29% for the IBPNN-DTS). Compared to IBPNN-DTS without optimization, the number of parameters in ABC-IBPNN-DTS shrank by 66% with only a 0.58% reduction in accuracy while the classification model kept high robustness.

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