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Classification of Standing and Walking States Using Ground Reaction Forces. SENSORS 2021; 21:s21062145. [PMID: 33803909 PMCID: PMC8003339 DOI: 10.3390/s21062145] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/17/2021] [Revised: 03/13/2021] [Accepted: 03/16/2021] [Indexed: 11/25/2022]
Abstract
The operation of wearable robots, such as gait rehabilitation robots, requires real-time classification of the standing or walking state of the wearer. This report explains a technique that measures the ground reaction force (GRF) using an insole device equipped with force sensing resistors, and detects whether the insole wearer is standing or walking based on the measured results. The technique developed in the present study uses the waveform length that represents the sum of the changes in the center of pressure within an arbitrary time window as the determining factor, and applies this factor to a conventional threshold method and an artificial neural network (ANN) model for classification of the standing and walking states. The results showed that applying the newly developed technique could significantly reduce classification errors due to shuffling movements of the patient, typically noticed in the conventional threshold method using GRF, i.e., real-time classification of the standing and walking states is possible in the ANN model. The insole device used in the present study can be applied not only to gait analysis systems used in wearable robot operations, but also as a device for remotely monitoring the activities of daily living of the wearer.
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Yin G, Zhang X, Chen D, Li H, Chen J, Chen C, Lemos S. Processing Surface EMG Signals for Exoskeleton Motion Control. Front Neurorobot 2020; 14:40. [PMID: 32765250 PMCID: PMC7381241 DOI: 10.3389/fnbot.2020.00040] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2020] [Accepted: 05/26/2020] [Indexed: 01/30/2023] Open
Abstract
The surface electromyography (sEMG) signal has been used for volitional control of robotic assistive devices. There are still challenges in improving system performance accuracy and signal processing to remove systematic noise. This study presents procedures and a pilot validation of the EMG-driven speed-control of exoskeleton and integrated treadmill with a goal to provide better interaction between a user and the system. The gait cycle duration (GCD) was extracted from sEMG signals using the autocorrelation algorithm and Bayesian fusion algorithm. GCDs of various walking speeds were then programmed to control the motion speed of exoskeleton robotic system. The performance and efficiency of this sEMG-controlled robotic assistive ambulation system was tested and validated among 6 healthy volunteers. The results demonstrated that the autocorrelation algorithm extracted the GCD from individual muscle contraction. The GCDs of individual muscles had variability between different walking steps under a designated walking speed. Bayesian fusion algorithms processed the GCDs of multiple muscles yielding a final GCD with the least variance. The fused GCD effectively controlled the motion speeds of exoskeleton and treadmill. The higher amplitude of EMG signals with shorter GCD was found during a faster walking speed. The algorithms using fused GCDs and gait stride length yielded trajectory joint motion tracks in a shape of sine curve waveform. The joint angles of the exoskeleton measured by a decoder mounted on the hip turned out to be in sine waveforms. The hip joint motion track of the exoskeleton matched the angles projected by trajectory curve generated by computer algorithms based on the fused GCDs with high agreement. The EMG-driven speed-control provided the human-machine inter-limb coordination mechanisms for an intuitive speed control of the exoskeleton-treadmill system at the user's intents. Potentially the whole system can be used for gait rehabilitation of incomplete spinal cord hemispheric stroke patients as goal-directed and task-oriented training tool.
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Affiliation(s)
- Gui Yin
- Institute of Robotics and Intelligent Systems, School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an, China
- Shaanxi Key Laboratory of Intelligent Robots, Xi’an Jiaotong University, Xi’an, China
| | - Xiaodong Zhang
- Institute of Robotics and Intelligent Systems, School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an, China
- Shaanxi Key Laboratory of Intelligent Robots, Xi’an Jiaotong University, Xi’an, China
| | - Dawei Chen
- Robotic Rehabilitation Laboratory, Department of Biomedical Engineering, Wayne State University, Detroit, MI, United States
| | - Hanzhe Li
- Institute of Robotics and Intelligent Systems, School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an, China
- Shaanxi Key Laboratory of Intelligent Robots, Xi’an Jiaotong University, Xi’an, China
| | | | - Chaoyang Chen
- Robotic Rehabilitation Laboratory, Department of Biomedical Engineering, Wayne State University, Detroit, MI, United States
- Department of Rehabilitation Medicine, First Affiliated Hospital, Fujian Medical University, Fuzhou, China
- Department of Orthopaedic Surgery and Sport Medicine, Detroit Medical Center, Detroit, MI, United States
| | - Stephen Lemos
- Department of Orthopaedic Surgery and Sport Medicine, Detroit Medical Center, Detroit, MI, United States
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