Human-in-the-Loop Control of a Hip Assistive Exoskeleton Based on Cross-Limb Virtual Force Transfer

IF 4.3 2区 综合性期刊 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC
Qiang Li;Qingcong Wu;Haitao Zou;Zihan Xu;Yanghui Zhu;Hongtao Wu
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Abstract

In industries where various unavoidable heavy lifting tasks are prevalent, workers are at high risk of developing musculoskeletal disorders. It is of great research value to relieve the physical stress of workers at work, reduce the risk of musculoskeletal diseases, and improve the work efficiency of workers. This article introduces a hip joint assistive exoskeleton robot that can provide a maximum assist torque of up to 80 Nm. We propose a human-in-loop control scheme for cross-limb virtual force transfer, with the human as the control leader. The exoskeleton is controlled based on surface electromyography (EMG) signals. The signal is filtered, normalized, and fed into a neural network model to estimate human joint torque. The estimated virtual force is used as the inner loop force control trajectory to assist the user. The assist torque can be adaptively adjusted according to the size of the grabbed heavy object. This solution collects the signals from the upper limbs, controls the exoskeleton to assist the lower limbs, and adjusts the assistance level in real time according to the preferences of user. The experimental results demonstrate that when using exoskeleton assistance, the muscle activity of the thighs of the subjects can be reduced by up to 43.82%.
基于跨肢虚拟力传递的髋关节辅助外骨骼人在环控制
在各种不可避免的繁重工作普遍存在的行业中,工人患肌肉骨骼疾病的风险很高。减轻工人在工作中的身体压力,降低肌肉骨骼疾病的风险,提高工人的工作效率,具有重要的研究价值。本文介绍了一种髋关节辅助外骨骼机器人,它可以提供高达80 Nm的最大辅助扭矩。提出了一种以人为控制主体的跨肢虚拟力传递人在环控制方案。外骨骼是基于表面肌电图(EMG)信号控制的。对信号进行滤波、归一化,并将其送入神经网络模型,以估计人体关节扭矩。将估计的虚拟力作为内环力控制轨迹来辅助用户。辅助力矩可根据抓取重物的大小自适应调节。该方案采集上肢信号,控制外骨骼辅助下肢,并根据用户喜好实时调整辅助水平。实验结果表明,使用外骨骼辅助时,受试者大腿肌肉活动可减少43.82%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
IEEE Sensors Journal
IEEE Sensors Journal 工程技术-工程:电子与电气
CiteScore
7.70
自引率
14.00%
发文量
2058
审稿时长
5.2 months
期刊介绍: The fields of interest of the IEEE Sensors Journal are the theory, design , fabrication, manufacturing and applications of devices for sensing and transducing physical, chemical and biological phenomena, with emphasis on the electronics and physics aspect of sensors and integrated sensors-actuators. IEEE Sensors Journal deals with the following: -Sensor Phenomenology, Modelling, and Evaluation -Sensor Materials, Processing, and Fabrication -Chemical and Gas Sensors -Microfluidics and Biosensors -Optical Sensors -Physical Sensors: Temperature, Mechanical, Magnetic, and others -Acoustic and Ultrasonic Sensors -Sensor Packaging -Sensor Networks -Sensor Applications -Sensor Systems: Signals, Processing, and Interfaces -Actuators and Sensor Power Systems -Sensor Signal Processing for high precision and stability (amplification, filtering, linearization, modulation/demodulation) and under harsh conditions (EMC, radiation, humidity, temperature); energy consumption/harvesting -Sensor Data Processing (soft computing with sensor data, e.g., pattern recognition, machine learning, evolutionary computation; sensor data fusion, processing of wave e.g., electromagnetic and acoustic; and non-wave, e.g., chemical, gravity, particle, thermal, radiative and non-radiative sensor data, detection, estimation and classification based on sensor data) -Sensors in Industrial Practice
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