Control of Walking Assist Exoskeleton With Time-delay Based on the Prediction of Plantar Force

Ding, Ming; Nagashima, Mikihisa; Cho, Sung-Gwi; Takamatsu, Jun; Ogasawara, Tsukasa

doi:10.1109/access.2020.3010644

Cited by 16 publications

(21 citation statements)

References 19 publications

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“…When controlling exoskeleton, the time-delay or phase-delay caused by the computation time, sensing time, communication delay and filter will make exoskeleton lag behind the human intention. To reduce the time-delay, the user's future motion should be predicted and the control command of exoskeleton should be send before user's motion (Ding et al, 2020 ). In order to online predict the future trajectory, two sets of Gaussian-like kernel functions are needed.…”

Section: Methodsmentioning

confidence: 99%

Exoskeleton Active Walking Assistance Control Framework Based on Frequency Adaptive Dynamics Movement Primitives

et al. 2021

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This paper introduces a novel exoskeleton active walking assistance control framework based on frequency adaptive dynamics movement primitives (FADMPs). The FADMPs proposed in this paper is an online learning and prediction algorithm which is able to online estimate the fundamental frequency of human joint trajectory, learn the shape of joint trajectory and predict the future joint trajectory during walking. The proposed active walking assistance control framework based on FADMPs is a model-based controller which relies on the human joint torque estimation. The assistance torque provided by exoskeleton is estimated by human lower limb inverse dynamics model which is sensitive to the noise in the joint motion trajectory. To estimate a smooth joint torque profile, the joint motion trajectory must be filtered first by a lowpass filter. However, lowpass filter will introduce an inevitable phase delay in the filtered trajectory. Both simulations and experiments in this paper show that the phase delay has a significant effect on the performance of exoskeleton active assistance. The active assistant control framework based on FADMPs aims at improving the performance of active assistance control by compensating the phase delay. Both simulations and experiments on active walking assistance control show that the performance of active assistance control can be further improved when the phase delay in the filtered trajectory is compensated by FADMPs.

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Section: Methodsmentioning

confidence: 99%

Exoskeleton Active Walking Assistance Control Framework Based on Frequency Adaptive Dynamics Movement Primitives

et al. 2021

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“…In Su et al's study [26], the motion trajectory after 200 ms was predicted using only kinematics data collected through an IMU sensor. Moreover, Ding et al's study [27] utilized kinematics data obtained through IMU sensors to predict motion intentions at various levels in advance, demonstrating optimal performance when predicting motion intentions after 200 ms. These related studies achieved predictions as fast as 200 ms using only kinematics data that corresponds to the time of motion execution.…”

Section: Related Workmentioning

confidence: 99%

“…The data gathered from human motion undergoes communication and processing through an embedded system before being applied to the actuator. However, this process inherently introduces control delay time [5], resulting in a time gap between human motion and the actual movement of the robot, leading to user discomfort. A potential and promising solution to address the control delay issue involves proactively predicting the wearer's motion intentions at a faster rate than conventional methods, thereby facilitating smooth motion of the exoskeleton robot.…”

Section: Introductionmentioning

confidence: 99%

Continuous Intention Prediction of Lifting Motions Using EMG-Based CNN-LSTM

Gwon,

Woo,

Sahithi

et al. 2024

IEEE Access

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Industrial exoskeletons is a field of ongoing research for improving human safety and conveniences. However, the adoption of industrial exoskeleton robots still remains challenging. One problem that needs to be solved is the control delay that inevitably occurs due to data transmission and processing issues. Recently, there has been active research employing deep learning to address control delay by leveraging diverse information extracted from human motion. One crucial source of information is electromyography (EMG) signals, known for their quicker activation compared to actual motion. This study specifically focused on predicting changing motion intentions within the squat, a representative lifting motion in industrial contexts. In an experimental setup involving 24 participants and utilizing 7 EMG electrodes, we categorized motion intentions during the squat into four types. We developed a CNN-LSTM model capable of predicting motion intentions 300 milliseconds ahead using EMG signals. The model's prediction performance was assessed by comparing them with existing models. The findings propose a methodology for utilizing EMG signals in predicting changing motion intentions for lower extremity movements, facilitating feedforward control of industrial exoskeleton robots. This research is anticipated to contribute to the advancement of acceptance in the realm of exoskeleton robots.

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“…In many cases, biosignals such as the ECG (electrocardiogram) or pulse rate, skin temperature or breathing frequency and many other parameters are measured for medical reasons [ 1 , 2 , 3 ]. Other possible applications are met in many sports disciplines [ 4 , 5 , 6 ], or even in human-machine interfaces (HMIs), e.g., to control a prosthesis, an exoskeleton, or a robot [ 7 , 8 , 9 , 10 ].…”

Section: Introductionmentioning

confidence: 99%

Measuring Biosignals with Single Circuit Boards

Ehrmann¹,

Błachowicz

Homburg

et al. 2022

Bioengineering

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To measure biosignals constantly, using textile-integrated or even textile-based electrodes and miniaturized electronics, is ideal to provide maximum comfort for patients or athletes during monitoring. While in former times, this was usually solved by integrating specialized electronics into garments, either connected to a handheld computer or including a wireless data transfer option, nowadays increasingly smaller single circuit boards are available, e.g., single-board computers such as Raspberry Pi or microcontrollers such as Arduino, in various shapes and dimensions. This review gives an overview of studies found in the recent scientific literature, reporting measurements of biosignals such as ECG, EMG, sweat and other health-related parameters by single circuit boards, showing new possibilities offered by Arduino, Raspberry Pi etc. in the mobile long-term acquisition of biosignals. The review concentrates on the electronics, not on textile electrodes about which several review papers are available.

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Control of Walking Assist Exoskeleton With Time-delay Based on the Prediction of Plantar Force

Cited by 16 publications

References 19 publications

Exoskeleton Active Walking Assistance Control Framework Based on Frequency Adaptive Dynamics Movement Primitives

Exoskeleton Active Walking Assistance Control Framework Based on Frequency Adaptive Dynamics Movement Primitives

Continuous Intention Prediction of Lifting Motions Using EMG-Based CNN-LSTM

Measuring Biosignals with Single Circuit Boards

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