scholarly journals Toward Design of an Environment-Aware Adaptive Locomotion-Mode-Recognition System

2012 ◽  
Vol 59 (10) ◽  
pp. 2716-2725 ◽  
Author(s):  
Lin Du ◽  
Fan Zhang ◽  
Ming Liu ◽  
He Huang
Keyword(s):  
Recognition System ◽  
Adaptive Locomotion ◽  
Mode Recognition ◽  
Locomotion Mode

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

Sensors ◽  
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.

scholarly journals Computer Vision and Deep Learning for Environment-Adaptive Control of Robotic Lower-Limb Exoskeletons

2021 ◽  
Author(s):  
Brokoslaw Laschowski ◽  
William McNally ◽  
Alexander Wong ◽  
John McPhee
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
Image Classification ◽  
Recognition System ◽  
Camera System ◽  
Human Control ◽  
Mode Recognition ◽  
Environment Recognition ◽  
Locomotion Mode ◽  
High Level

Robotic exoskeletons require human control and decision making to switch between different locomotion modes, which can be inconvenient and cognitively demanding. To support the development of automated locomotion mode recognition systems (i.e., high-level controllers), we designed an environment recognition system using computer vision and deep learning. We collected over 5.6 million images of indoor and outdoor real-world walking environments using a wearable camera system, of which ~923,000 images were annotated using a 12-class hierarchical labelling architecture (called the ExoNet database). We then trained and tested the EfficientNetB0 convolutional neural network, designed for efficiency using neural architecture search, to predict the different walking environments. Our environment recognition system achieved ~73% image classification accuracy. While these preliminary results benchmark EfficientNetB0 on the ExoNet database, further research is needed to compare different image classification algorithms to develop an accurate and real-time environment-adaptive locomotion mode recognition system for robotic exoskeleton control.

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

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.

scholarly journals Kinematic-based locomotion mode recognition for power augmentation exoskeleton

2017 ◽  
Vol 14 (5) ◽  
pp. 172988141773032 ◽  
Author(s):  
Hongchul Kim ◽  
Young June Shin ◽  
Jung Kim
Keyword(s):  
Joint Angle ◽  
Identification Accuracy ◽  
Sensor Data ◽  
Knee Joint Angle ◽  
Stair Ascent ◽  
Mode Recognition ◽  
Different Types ◽  
Locomotion Mode ◽  
The Difference ◽  
Foot Position

This article presents a kinematic-based method for locomotion mode recognition, for use in the control of an exoskeleton for power augmentation, to implement natural and smooth locomotion transition. The difference in vertical foot position between a foot already in contact with ground and a foot newly in contact with the ground was calculated via kinematics for the entire exoskeleton and used to identify the locomotion mode with other sensor data including data on the knee joint angle and inclination of the thigh, shank, and foot. Locomotion on five different types of terrain—level-ground walking, stair ascent, stair descent, ramp ascent, and ramp descent—were identified using two-layer decision tree classes. An updating process is proposed to improve identification of the transition and accuracy using the foot inclination at the mid-stance. An average identification accuracy of more than 99% was achieved in experiments with eight subjects for single terrains (no terrain transitions) and hybrid terrains. The experimental results show that the proposed method can achieve high accuracy without significant misrecognition and minimize the delay in locomotion mode recognition of the exoskeleton.

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