A Novel Motion Intention Recognition Approach for Soft Exoskeleton via IMU

Zhu, Lu; Wang, Zhuo; Zhi-gang, Ning; Zhang, Yu; Liu, Yida; Cao, Wujing; Wang, Xinyu; Chen, Chunjie

doi:10.3390/electronics9122176

Cited by 45 publications

(54 citation statements)

References 33 publications

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“…Despite offering promising performances, the use of EMG signals can present some practice-related drawbacks, such as (a) the difficulty to maintain the sensors in the same position during the user's locomotion, which can affect the joint torque estimation over time; (b) the need for an expert-based setup; (c) skin reactions; (d) the measures are affected by temperature variations and sweating [39][40][41][42]56]. From a comparative analysis between the estimation of the ankle joint torque trajectories with and without considering EMG signals as input, significant differences between both approaches were not found (p-value > 0.05).…”

Section: Discussionmentioning

confidence: 99%

“…Most of the algorithms for joint torque estimation fuse EMG data ( [29,30]). EMG-based approaches have been left behind since the EMG sensing is prone to fade during long-term use due to (i) movements between the skin and the electrodes; (ii) temperature variations; and (iii) sweating [39][40][41][42]. These phenomena can cause incorrect joint torque estimations, which may compromise the PADs' assistance efficacy.…”

Section: Introductionmentioning

confidence: 99%

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Kinematics, Speed, and Anthropometry-Based Ankle Joint Torque Estimation: A Deep Learning Regression Approach

et al. 2021

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Powered Assistive Devices (PADs) have been proposed to enable repetitive, user-oriented gait rehabilitation. They may include torque controllers that typically require reference joint torque trajectories to determine the most suitable level of assistance. However, a robust approach able to automatically estimate user-oriented reference joint torque trajectories, namely ankle torque, while considering the effects of varying walking speed, body mass, and height on the gait dynamics, is needed. This study evaluates the accuracy and generalization ability of two Deep Learning (DL) regressors (Long-Short Term Memory and Convolutional Neural Network (CNN)) to generate user-oriented reference ankle torque trajectories by innovatively customizing them according to the walking speed (ranging from 1.0 to 4.0 km/h) and users’ body height and mass (ranging from 1.51 to 1.83 m and 52.0 to 83.7 kg, respectively). Furthermore, this study hypothesizes that DL regressors can estimate joint torque without resourcing electromyography signals. CNN was the most robust algorithm (Normalized Root Mean Square Error: 0.70 ± 0.06; Spearman Correlation: 0.89 ± 0.03; Coefficient of Determination: 0.91 ± 0.03). No statistically significant differences were found in CNN accuracy (p-value > 0.05) whether electromyography signals are included as inputs or not, enabling a less obtrusive and accurate setup for torque estimation.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Kinematics, Speed, and Anthropometry-Based Ankle Joint Torque Estimation: A Deep Learning Regression Approach

et al. 2021

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“…For interactive information fusion, common fusion algorithms include Kalman filter [ 95 ], particle filter [ 96 ], complementary filter [ 97 ], and artificial neural network [ 98 ]. Generally, a single Kalman filter is not ideal, so the extended Kalman filter method or combined with other methods is a good choice [ 99 ]. In addition, from the perspective of sensors involved, a majority of research still choose wearable sensors to deal with gait information.…”

Section: Gait Recognition Based On Information Fusionmentioning

confidence: 99%

Gait Recognition for Lower Limb Exoskeletons Based on Interactive Information Fusion

Chen

Zhu

et al. 2022

Applied Bionics and Biomechanics

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In recent decades, although the research on gait recognition of lower limb exoskeleton robot has been widely developed, there are still limitations in rehabilitation training and clinical practice. The emergence of interactive information fusion technology provides a new research idea for the solution of this problem, and it is also the development trend in the future. In order to better explore the issue, this paper summarizes gait recognition based on interactive information fusion of lower limb exoskeleton robots. This review introduces the current research status, methods, and directions for information acquisition, interaction, fusion, and gait recognition of exoskeleton robots. The content involves the research progress of information acquisition methods, sensor placements, target groups, lower limb sports biomechanics, interactive information fusion, and gait recognition model. Finally, the current challenges, possible solutions, and promising prospects are analysed and discussed, which provides a useful reference resource for the study of interactive information fusion and gait recognition of rehabilitation exoskeleton robots.

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“…Human-exoskeleton interface (HEI) is the core to enhance the interaction between the user and the robotic exoskeleton, and its main function is to predict the movement of exoskeleton wearers. Traditionally, HEI based on the physical signal, such as inertial measurement unit signal, is certainly one of the earliest used interfaces to predict the movements of the exoskeleton wearer (Beil et al, 2018 ; Zhu et al, 2020a ). However, this kind of HEI makes low participation of patients in rehabilitation training.…”

Section: Introductionmentioning

confidence: 99%

Multimodal Human-Exoskeleton Interface for Lower Limb Movement Prediction Through a Dense Co-Attention Symmetric Mechanism

Shi

Huang

et al. 2022

Front. Neurosci.

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A challenging task for the biological neural signal-based human-exoskeleton interface is to achieve accurate lower limb movement prediction of patients with hemiplegia in rehabilitation training scenarios. The human-exoskeleton interface based on single-modal biological signals such as electroencephalogram (EEG) is currently not mature in predicting movements, due to its unreliability. The multimodal human-exoskeleton interface is a very novel solution to this problem. This kind of interface normally combines the EEG signal with surface electromyography (sEMG) signal. However, their use for the lower limb movement prediction is still limited—the connection between sEMG and EEG signals and the deep feature fusion between them are ignored. In this article, a Dense con-attention mechanism-based Multimodal Enhance Fusion Network (DMEFNet) is proposed for predicting lower limb movement of patients with hemiplegia. The DMEFNet introduces the con-attention structure to extract the common attention between sEMG and EEG signal features. To verify the effectiveness of DMEFNet, an sEMG and EEG data acquisition experiment and an incomplete asynchronous data collection paradigm are designed. The experimental results show that DMEFNet has a good movement prediction performance in both within-subject and cross-subject situations, reaching an accuracy of 82.96 and 88.44%, respectively.

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A Novel Motion Intention Recognition Approach for Soft Exoskeleton via IMU

Cited by 45 publications

References 33 publications

Kinematics, Speed, and Anthropometry-Based Ankle Joint Torque Estimation: A Deep Learning Regression Approach

Kinematics, Speed, and Anthropometry-Based Ankle Joint Torque Estimation: A Deep Learning Regression Approach

Gait Recognition for Lower Limb Exoskeletons Based on Interactive Information Fusion

Multimodal Human-Exoskeleton Interface for Lower Limb Movement Prediction Through a Dense Co-Attention Symmetric Mechanism

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