Human-Following Control of Cane-Type Walking-Aid Robot Within Fixed Relative Posture

Yan, Qingyang; Huang, Jian; Yang, Zhaohui; Hasegawa, Yasuhisa; Fukuda, Toshio

doi:10.1109/tmech.2021.3068138

Cited by 25 publications

(12 citation statements)

References 25 publications

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“…Several methods have been put forward to minimize the influence of arm position changes in past studies, such as the multi-position classifier (Geng et al, 2012 ), cascade classifier (Geng et al, 2017 ), dynamic training (Shahzad et al, 2019 ), position-invariant features (Asogbon et al, 2020 ), and other classification algorithms. Multi-modal signals are usually needed for many methods, such as the accelerometry (ACC) signal, the near-infrared spectroscopy (NIRS) (Guo et al, 2015 , 2017 ), electroencephalography (EEG) (Leeb et al, 2011 ), FMG (Ferigo et al, 2017 ; Prakash et al, 2020a , b ; Huang et al, 2021 ), and some industry sensors used for human-centered robotic systems (Huang et al, 2020 ; Yan et al, 2021 ). Among these signals, the ACC is the most commonly used signal for the complementary of EMG (Geng et al, 2012 ; Huang et al, 2015 ; Shahzad et al, 2019 ), since the tri-axis ACC signal could provide information about arm position.…”

Section: Introductionmentioning

confidence: 99%

Improving the Robustness of Human-Machine Interactive Control for Myoelectric Prosthetic Hand During Arm Position Changing

Huang²,

Wang³

et al. 2022

Front. Neurorobot.

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Robust classification of natural hand grasp type based on electromyography (EMG) still has some shortcomings in the practical prosthetic hand control, owing to the influence of dynamic arm position changing during hand actions. This study provided a framework for robust hand grasp type classification during dynamic arm position changes, improving both the “hardware” and “algorithm” components. In the hardware aspect, co-located synchronous EMG and force myography (FMG) signals are adopted as the multi-modal strategy. In the algorithm aspect, a sequential decision algorithm is proposed by combining the RNN-based deep learning model with a knowledge-based post-processing model. Experimental results showed that the classification accuracy of multi-modal EMG-FMG signals was increased by more than 10% compared with the EMG-only signal. Moreover, the classification accuracy of the proposed sequential decision algorithm improved the accuracy by more than 4% compared with other baseline models when using both EMG and FMG signals.

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Section: Introductionmentioning

confidence: 99%

Improving the Robustness of Human-Machine Interactive Control for Myoelectric Prosthetic Hand During Arm Position Changing

Huang²,

Wang³

et al. 2022

Front. Neurorobot.

Self Cite

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“…In order to estimate the disturbance and realize the control stability of the mobile wheeled inverted pendulum systems, a novel sliding mode control based on high-order disturbance observer was developed (Huang et al, 2020 ). Further a cane type walking-aid robot is proposed (Yan et al, 2021 ), which follow a human user for the safety and supervision of independent walking during rehabilitation training. The autonomous positioning and navigational capabilities of mobile robots are critical in walking robots, and an accurate map is the basis of these capabilities in walking robots.…”

Section: Introductionmentioning

confidence: 99%

Simultaneous Localization and Mapping Algorithm Based on the Asynchronous Fusion of Laser and Vision Sensors

et al. 2022

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In this paper, a simultaneous localization and mapping algorithm based on the weighted asynchronous fusion of laser and vision sensors is proposed for an assistant robot. When compared to the synchronous fusion algorithm, this method can effectively use the redundant data in the vision sensor and improve the tracking accuracy of the algorithm. At the same time, the attitude estimation of the visual sensor is taken as a prior of the attitude estimation of the laser sensor to reduce the number of iterations and improve the efficiency of the algorithm. Further, according to the running state of the robot, a weighting coefficient based on angle is introduced to improve the confidence of the measurement. Experimental results show that the algorithm is robust and can work in a degraded environment. When compared to the synchronous fusion method, the asynchronous fusion algorithm has a more accurate prior, faster operation speed, higher pose estimation frequency, and more accurate positioning accuracy.

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“…Predicting foot placement would improve the performance of walking aid robots (e.g. exoskeleton robots, supernumerary robotic limbs [4], [5], robotic prostheses [6] and canetype walking-aid robots [7], [8]) as it would provide the robots with knowledge of the stride length and width in advance and help them adjust their pose before providing assistance. In this study, we aim to propose a probability distribution model-based approach to predict foot placement when people are walking on flat ground.…”

Section: Introductionmentioning

confidence: 99%

A Probability Distribution Model-Based Approach for Foot Placement Prediction in the Early Swing Phase With a Wearable IMU Sensor

Chen

Zhang

Liu

et al. 2021

IEEE Trans. Neural Syst. Rehabil. Eng.

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Human-Following Control of Cane-Type Walking-Aid Robot Within Fixed Relative Posture

Cited by 25 publications

References 25 publications

Improving the Robustness of Human-Machine Interactive Control for Myoelectric Prosthetic Hand During Arm Position Changing

Improving the Robustness of Human-Machine Interactive Control for Myoelectric Prosthetic Hand During Arm Position Changing

Simultaneous Localization and Mapping Algorithm Based on the Asynchronous Fusion of Laser and Vision Sensors

A Probability Distribution Model-Based Approach for Foot Placement Prediction in the Early Swing Phase With a Wearable IMU Sensor

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