HAR-sEMG: A Dataset for Human Activity Recognition on Lower-Limb sEMG

Luan, Yu; Shi, Yuhang; Wu, Wanyin; Liu, Zhiyao; Chang, Hai; Cheng, Jun

doi:10.1007/s10115-021-01598-w

Cited by 12 publications

(6 citation statements)

References 52 publications

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“…Also, our maximum hip angle in DNS is about 7 degrees smaller than J. Camargo et al . 16 and much smaller than the other datasets (the difference between these datasets about this value is also more than 20 degrees) 14 , 28 , 29 . The minimum and maximum hip angle in UPS of these datasets are different (the results of SIAT-LLMD are smallest), but the angular range in all these datasets are around 50 degrees 14 , 16 , 28 , 29 .…”

Section: Technical Validationmentioning

confidence: 77%

“… 16 and much smaller than the other datasets (the difference between these datasets about this value is also more than 20 degrees) 14 , 28 , 29 . The minimum and maximum hip angle in UPS of these datasets are different (the results of SIAT-LLMD are smallest), but the angular range in all these datasets are around 50 degrees 14 , 16 , 28 , 29 . For STDUP and SITDN, the kinematic and kinetic data can be mutually verified with the research of C. Pinheiro et al .…”

Section: Technical Validationmentioning

confidence: 77%

“…For upper limb movement intent characterization, the Ninapro repository 9,10 (a publicly accessible database) provides a considerable amount of datasets, including sEMG, joint angle (kinematics), and force (kinetics) data with corresponding labels to support the research community. Similarly, there are a few publicly available sEMG datasets for lower limb movement characterization, which have helped advance research and development to an extent in the field of rehabilitation robotics and the likes [11][12][13][14][15][16][17] . However, these lower limb sEMG datasets are limited in a number of ways that preclude their wide usage by researchers in the field.…”

Section: Background and Summarymentioning

confidence: 99%

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Surface electromyogram, kinematic, and kinetic dataset of lower limb walking for movement intent recognition

et al. 2023

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Surface electromyogram (sEMG) offers a rich set of motor information for decoding limb motion intention that serves as a control input to Intelligent human-machine synergy systems (IHMSS). Despite growing interest in IHMSS, the current publicly available datasets are limited and can hardly meet the growing demands of researchers. This study presents a novel lower limb motion dataset (designated as SIAT-LLMD), comprising sEMG, kinematic, and kinetic data with corresponding labels acquired from 40 healthy humans during 16 movements. The kinematic and kinetic data were collected using a motion capture system and six-dimensional force platforms and processed using OpenSim software. The sEMG data were recorded using nine wireless sensors placed on the subjects’ thigh and calf muscles on the left limb. Besides, SIAT-LLMD provides labels to classify the different movements and different gait phases. Analysis of the dataset verified the synchronization and reproducibility, and codes for effective data processing are provided. The proposed dataset can serve as a new resource for exploring novel algorithms and models for characterizing lower limb movements.

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Section: Technical Validationmentioning

confidence: 77%

Section: Technical Validationmentioning

confidence: 77%

Section: Background and Summarymentioning

confidence: 99%

See 1 more Smart Citation

Surface electromyogram, kinematic, and kinetic dataset of lower limb walking for movement intent recognition

et al. 2023

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“…Traditional methods of activity recognition, relying heavily on surface electromyographic signals (sEMGs), have encountered challenges that necessitate a re-evaluation of approaches [11]. The journey from sEMGs to alternative mechanical sensors, including accelerometers, gyroscopes, and pressure sensors, reflects the ongoing endeavour to enhance the reliability and accuracy of activity recognition systems [12,13]. The significance of this pursuit extends beyond the technical intricacies of signal processing and sensor fusion; it encapsulates the essence of restoring not just mobility but a holistic sense of autonomy and participation in daily life.…”

Section: Introductionmentioning

confidence: 99%

Integrated Sensor Fusion and Multi-Modal Hierarchical Neural Network for Activity Recognition in Lower Limb Prosthetics

S. Prakash

2024

jes

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Activity recognition plays pivotal role in enhancing functionality for prosthetic devices, ensuring seamless integration with users' movements. However, the complexity arises from the diverse data sources, including acceleration, angular velocity, joint angles, orientation, electromyography (EMG), and marker data, necessitating a robust approach to overcome challenges in information integration. The primary challenge lies in the effective utilization of multiple sensor modalities, each with unique characteristics and potential noise sources. The proposed solution addresses this by employing advanced sensor fusion techniques, such as Kalman filtering, during data collection. Synchronization and resampling ensure temporal consistency, while noise reduction techniques, such as low-pass filters, mitigate signal distortions. To further refine the process, a hybrid optimization-based feature selection Adaptive Step Size in Marine Predators Algorithm (ASSMPA) is introduced, focusing on marker data features. ASSMPA synergizes Marine Predators Algorithm (MPA) and Pathfinder Algorithm (PFA) for optimal feature selection in marine predator pathfinding tasks. The Feature Fusion step integrates attention mechanisms to dynamically weigh the significance of different sensor modalities during the fusion process. This strategic fusion enhances the overall performance of the Multi-Modal Hierarchical Neural Network (MMHNN). The proposed model is implemented using Python.

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“…By utilizing electromyography (EMG), predicting lower limb behavior intention can leverage the natural laws of human behavior for achieving human-machine interaction, facilitating seamless switching of robot working modes, improving robot adaptability to the human body, and enhancing the overall experience of human-machine interaction. Furthermore, predicting human motion intention and recognizing motion posture have significant applications in many fields, for example, clinical analysis [1,2], healthcare [3,4], human-machine cooperation [5,6], virtual reality [7,8], bionic prosthesis [9,10].…”

Section: Introductionmentioning

confidence: 99%

An Efficient Method for Identifying Lower Limb Behavior Intentions Based on Surface Electromyography

Ling,

Wang,

Ding

et al. 2023

CMC

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Surface electromyography (sEMG) is widely used for analyzing and controlling lower limb assisted exoskeleton robots. Behavior intention recognition based on sEMG is of great significance for achieving intelligent prosthetic and exoskeleton control. Achieving highly efficient recognition while improving performance has always been a significant challenge. To address this, we propose an sEMG-based method called Enhanced Residual Gate Network (ERGN) for lower-limb behavioral intention recognition. The proposed network combines an attention mechanism and a hard threshold function, while combining the advantages of residual structure, which maps sEMG of multiple acquisition channels to the lower limb motion states. Firstly, continuous wavelet transform (CWT) is used to extract signals features from the collected sEMG data. Then, a hard threshold function serves as the gate function to enhance signals quality, with an attention mechanism incorporated to improve the ERGN's performance further. Experimental results demonstrate that the proposed ERGN achieves extremely high accuracy and efficiency, with an average recognition accuracy of 98.41% and an average recognition time of only 20 ms-outperforming the state-ofthe-art research significantly. Our research provides support for the application of lower limb assisted exoskeleton robots.

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HAR-sEMG: A Dataset for Human Activity Recognition on Lower-Limb sEMG

Cited by 12 publications

References 52 publications

Surface electromyogram, kinematic, and kinetic dataset of lower limb walking for movement intent recognition

Surface electromyogram, kinematic, and kinetic dataset of lower limb walking for movement intent recognition

Integrated Sensor Fusion and Multi-Modal Hierarchical Neural Network for Activity Recognition in Lower Limb Prosthetics

An Efficient Method for Identifying Lower Limb Behavior Intentions Based on Surface Electromyography

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