Gait phase prediction for lower limb exoskeleton robots

Wu, Guizhong; Wang, Can; Wang, Xinyu; Wang, Zhouyang; Ma, Yue; Zhang, Ting

doi:10.1109/icinfa.2016.7831791

Cited by 18 publications

(10 citation statements)

References 11 publications

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“…Revised brain symmetry (rBSI) is correlated with motor imagery tasks and used as a measure of stroke rehabilitation. Gait phase identification is done using only joint angular sensor [31]. This method helped to simplify the sensor system of lower limb exoskeleton.…”

Section: Related Workmentioning

confidence: 99%

Brain-Controlled Adaptive Lower Limb Exoskeleton for Rehabilitation of Post-Stroke Paralyzed

Vinoj

Jacob²,

Menon³

et al. 2019

IEEE Access

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Stroke is a standout amongst the most imperative reasons of incapacity on the planet. Due to partial or full paralysis, the majority of patients are compelled to rely upon parental figures and caregivers in residual life. With post-stroke rehabilitation, different types of assistive technologies have been proposed to offer developments to the influenced body parts of the incapacitated. In a large portion of these devices, the clients neither have control over the tasks nor can get feedback concerning the status of the exoskeleton. Additionally, there is no arrangement to detect user movements or accidental fall. The proposed framework tackles these issues utilizing a brain-controlled lower limb exoskeleton (BCLLE) in which the exoskeleton movements are controlled based on user intentions. An adaptive mechanism based on sensory feedback is integrated to reduce the system false rate. The BCLLE uses a flexible design which can be customized according to the degree of disability. The exoskeleton is modeled according to the human body anatomy, which makes it a perfect fit for the affected body part. The BCLLE system also automatically identifies the status of the paralyzed person and transmits information securely using Novel-T Symmetric Encryption Algorithm (NTSA) to caregivers in case of emergencies. The exoskeleton is fitted with motors which are controlled by the brain waves of the user with an electroencephalogram (EEG) headset. The EEG headset captures the human intentions based on the signals acquired from the brain. The brain-computer interface converts these signals into digital data and is interfaced with the motors via a microcontroller. The microcontroller controls the high torque motors connected to the exoskeleton's joints based on user intentions. Classification accuracy of more than 80% is obtained with our proposed method which is much higher compared with all existing solutions. INDEX TERMS Artificial skin, assistive technologies, brain-computer interface (BCI), electroencephalogram (EEG), brain-controlled exoskeleton, paralyzed, stroke.

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Section: Related Workmentioning

confidence: 99%

Brain-Controlled Adaptive Lower Limb Exoskeleton for Rehabilitation of Post-Stroke Paralyzed

Vinoj

Jacob²,

Menon³

et al. 2019

IEEE Access

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“…There are two kinds of sensors in this exoskeleton robot, the goniometers and the force sensing resistors. The lower limb exoskeleton designed by the Shenzhen Institutes of Advanced Technology (SIAT), Chinese Academy of Science called the SIAT exoskeleton was used in our experiments [37]. This exoskeleton was designed with the aim of helping the paraplegic patients to walk again normally or in the rehabilitation training for people suffering from lower limb injuries, such as accidents or strokes.…”

Section: Data Acquisition Platformmentioning

confidence: 99%

“…If the voltage of the FSR data is larger than the corresponding threshold, then the state of this FSR is high, otherwise it is low. After obtaining the threshold of each FSR in each series, a truth table is created to divide the gait phase, as shown in Table 4 [37]. The hip joint assist torque is a set of continuous smooth curves and it is defined based on the gait phase.…”

Section: Gait Phase Divisionmentioning

confidence: 99%

Gait Phase Classification and Assist Torque Prediction for a Lower Limb Exoskeleton System Using Kernel Recursive Least-Squares Method

Wang

et al. 2019

Sensors

Self Cite

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The gait phase classification method is a key technique to control an exoskeleton robot. Different people have different gait features while wearing an exoskeleton robot due to the gap between the exoskeleton and the wearer and their operation habits, such as the correspondence between the joint angle and the moment at which the foot contacts the ground, the amplitude of the joint angle and others. In order to enhance the performance of the gait phase classification in an exoskeleton robot using only the angle of hip and knee joints, a kernel recursive least-squares (KRLS) algorithm is introduced to build a gait phase classification model. We also build an assist torque predictor based on the KRLS algorithm in this work considering the adaptation of unique gait features. In this paper, we evaluate the classification performance of the KRLS model by comparing with two other commonly used gait recognition methods—the multi-layer perceptron neural network (MLPNN) method and the support vector machine (SVM) algorithm. In this experiment, the training and testing datasets for the models built by KRLS, MLPNN and SVM were collected from 10 healthy volunteers. The gait data are collected from the exoskeleton robot that we designed rather than collected from the human body. These data depict the human-robot coupling gait that includes unique gait features. The KRLS classification results are in average 3% higher than MLPNN and SVM. The testing average accuracy of KRLS is about 86%. The prediction results of KRLS are twice as good as MLPNN in assist torque prediction experiments. The KRLS performs in a good, stable, and robust way and shows model generalization abilities.

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“…They chose the FlexiForce sensor to design the pressure insole and detect the gait phase based on the threshold segmentation method of the pressure center. Wu et al [ 18 ] used an insole made of three FSR sensors to detect four gait sub-phases. Chen et al [ 19 ] used FlexiForce sensors to design a pair of insoles with eight sensors to identify walking patterns.…”

Section: Introductionmentioning

confidence: 99%

Design of a Plantar Pressure Insole Measuring System Based on Modular Photoelectric Pressure Sensor Unit

Ren

Liu

2021

Sensors

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Accurately perceiving and predicting the parameters related to human walking is very important for man–machine coupled cooperative control systems such as exoskeletons and power prostheses. Plantar pressure data is rich in human gait and posture information and is an essential source of reference information as the input of the exoskeleton control system. Therefore, the proper design of the pressure sensing insole and validation is a big challenge considering the requirements such as convenience, reliability, no interference and so on. In this research, we developed a low-cost modular sensing unit based on the principle of photoelectric sensing and designed a plantar pressure sensing insole to achieve the purpose of sensing human walking gait and posture information. On the one hand, the sensor unit is made of economy-friendly commercial flexible circuits and elastic silicone, and the mechanical and electrical characteristics of the modular sensor unit are evaluated by a self-developed pressure-related calibration system. The calibration results show that the modular sensor based on the photoelectric sensing principle has fast response and negligible hysteresis. On the other hand, we analyzed the area where the plantar pressure is densely distributed. One benefit of the modular sensing unit design is that it is rather convenient to fabricate different insole solutions, so we fabricated and compared several pressure-sensitive insole solutions in this preliminary study. During the dynamic locomotion experiments of wearing the pressure-sensing insole, the time series signal of each sensor unit was collected and analyzed. The results show that the pressure sensing insole based on the photoelectric effect can sense the distribution of the plantar pressure by capturing the deformation of the insole caused by the foot contact during locomotion, and provide reliable gait information for wearable applications.

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Gait phase prediction for lower limb exoskeleton robots

Cited by 18 publications

References 11 publications

Brain-Controlled Adaptive Lower Limb Exoskeleton for Rehabilitation of Post-Stroke Paralyzed

Brain-Controlled Adaptive Lower Limb Exoskeleton for Rehabilitation of Post-Stroke Paralyzed

Gait Phase Classification and Assist Torque Prediction for a Lower Limb Exoskeleton System Using Kernel Recursive Least-Squares Method

Design of a Plantar Pressure Insole Measuring System Based on Modular Photoelectric Pressure Sensor Unit

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