sEMG-Based Lower Limb Motion Prediction Using CNN-LSTM with Improved PCA Optimization Algorithm

Zhu, Meng; Guan, Xiaorong; Li, Zhong; Lin, He; Wang, Zheng; Cai, Keshu

doi:10.1007/s42235-022-00280-3

Cited by 44 publications

(11 citation statements)

References 35 publications

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“…However, higher-order Butterworth filters exhibit faster amplitude decay in the stop band compared to lower-order filters. Other types of filters display different amplitude diagonal frequency curve shapes for higher orders compared to lower orders [24][25][26].…”

Section: Baseline Drift 0~20hzmentioning

confidence: 99%

Positional analysis of assisting muscles for handling-assisted exoskeleton

Wang,

Guan,

et al. 2024

Preprint

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In order to better design the Handling-assisted exoskeleton, it is necessary to analyze the biomechanics of humane handling movements. In this study, Anybody Modeling System(AMS) simulation was used to analyze the movement state of muscles during humane handling. Combined with surface electromyography(sEMG) experiments, specific analysis and verification were carried out to obtain the position of muscles that the human body needs to assist during handling. In this study, the simulation and experiment were carried out for the manual handling process. The treatment group and the experimental group were set up. This study found that the vastus medialis muscle, vastus lateralis muscle, latissimus dorsi muscle, trapezius muscle, deltoid muscle and triceps brachii muscle needs more power in the process of handling, and it is reasonable and effective to combine sEMG signals with simulation of the musculoskeletal model to analyze the muscle condition of humane movement.

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Section: Baseline Drift 0~20hzmentioning

confidence: 99%

Positional analysis of assisting muscles for handling-assisted exoskeleton

Wang,

Guan,

et al. 2024

Preprint

Self Cite

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“…The data inputs for the above combined approach are mainly divided into neurophysiological signals such as EEG and EMG [8] [9] [12], 3D motion capture information [7] [10] [11] and kinetic data information [12]. And the use of the above combined approach has a large requirement for the initial input data.…”

Section: Gait Recognition Using Deep Learning Algorithmsmentioning

confidence: 99%

“…And the use of the above combined approach has a large requirement for the initial input data. For EMG signals collected by EMG sensors such as TrignoTM requires a filter to filter the sEMG signal [8], which in turn eliminates the interference of other electromagnetic signals received during signal recording. The acceleration and angular velocity data collected using IMU from various parts of the experimenter's lower extremities also need to be filtered by low-pass filters, and the sensor data should be in the same dimension using linear interpolation, normalization, and data segmentation, so as to effectively improve the accuracy of the subsequent deep neural network algorithms.…”

Section: Gait Recognition Using Deep Learning Algorithmsmentioning

confidence: 99%

An analysis of gait capture and simulation techniques for lower limb exoskeleton robots for stroke rehabilitation

Jin

2023

ACE

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Lower extremity rehabilitation-assisted exoskeleton robots bring together multiple disciplines such as biomechanics, control engineering, robotics, and computer science. The main role of lower extremity rehabilitation exoskeleton robots is to help patients and rehabilitators to maintain or restore the mobility of lower extremities, therefore, proper research and discussion of human gait analysis is the basis for establishing and improving such exoskeleton robots. To analyse and evaluate the positive effects of modern technology on stroke patients, the development of human gait capture and simulation technologies will be mainly summarized, the latest human gait capture and simulation technologies will be classified and evaluated. And through a comprehensive review and analysis of research advances in these areas, the usefulness of lower extremity rehabilitation-assisted exoskeleton robots for stroke populations is evaluated. This paper is informative in studying the usefulness of a lower limb exoskeleton robot combined with gait capture and simulation technology for rehabilitation training of stroke patients.

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“…Liao et al [15] predicted the parameters of a mathematical model of infectious diseases by fusing deep learning models with other temporal prediction methods. Zhu et al [16] fused several deep learning models to propose a new knee angle prediction model. Polson et al [17] developed a deep learning model to predict traffic flow.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Inbound and Outbound Passenger Flow in Urban Rail Transit Based on Spatio-Temporal Attention Residual Network

Yang,

Dong,

Yang

et al. 2023

Applied Sciences

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Passenger flow prediction is a critical approach to ensure the effective functioning of urban rail transit. However, there are few studies that combine multiple influencing factors for short-term passenger flow prediction. It is also a challenge to accurately predict passenger flow at all stations in the line at the same time. To overcome the above limitations, a deep learning-based method named ST-RANet is proposed, which consists of three spatio-temporal modules and one external module. The model is capable of predicting inbound and outbound passenger flow for all stations within the network simultaneously. We model the spatio-temporal data in terms of three temporal characteristics, including closeness, period, and trend. For each characteristic, we construct a spatio-temporal module that innovatively integrates the attention mechanisms into the middle of residual units and convolutional neural networks (CNNs) to extract and learn spatio-temporal features. Subsequently, the results of the three modules are integrated using a parameter matrix method, which allows for dynamic aggregation based on data. The integration results are further combined with external factors, such as holidays and meteorological information, to obtain passenger flow prediction values for each station. The proposed model is validated using real data from Beijing Subway, and optimized parameters are applied for 30-min granularity passenger flow predictions. Comparing the performance against 5 baseline models and verifying with data from multiple lines, the results indicate that the proposed ST-RANet model shows the best results. It is demonstrated that the method proposed in this paper has high prediction accuracy and good applicability.

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sEMG-Based Lower Limb Motion Prediction Using CNN-LSTM with Improved PCA Optimization Algorithm

Cited by 44 publications

References 35 publications

Positional analysis of assisting muscles for handling-assisted exoskeleton

Positional analysis of assisting muscles for handling-assisted exoskeleton

An analysis of gait capture and simulation techniques for lower limb exoskeleton robots for stroke rehabilitation

Prediction of Inbound and Outbound Passenger Flow in Urban Rail Transit Based on Spatio-Temporal Attention Residual Network

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