Recognition of walking environments and gait period by surface electromyography

Kyeong, Seulki; Shin, Weon Ho; Yang, Minjin; Heo, Ung; Feng, Jirou; Kim, Jung

doi:10.1631/fitee.1800601

Cited by 57 publications

(45 citation statements)

References 35 publications

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“…The predefined locomotion modes were identified as follows: (1) level walking (LW), (2) ramp ascent (RA), (3) ramp descent (RD), (4) stairs ascent (SA), (5) stairs descent (SD). The above locomotion modes are consistent with those in other relevant works [16][17][18][19]. Ten healthy subjects (6 male and 4 female) were asked to walk using the five locomotion modes and ultimately produced 3,573,241 observations, with 2707 strides in total.…”

Section: A Experimental Protocolsupporting

confidence: 83%

“…Ten healthy subjects (6 male and 4 female) were asked to walk using the five locomotion modes and ultimately produced 3,573,241 observations, with 2707 strides in total. The ramp was angled at 10 degrees, and the stair height was 10 cm [17].…”

Section: A Experimental Protocolmentioning

confidence: 99%

“…A locomotion mode recognition system generally consists of two parts: a data acquisition front-end and a recognition algorithm back-end. For the former, wearable sensors, including kinematic sensors (IMUs) [14][15][16], kinetic sensors (interaction force or ground reaction force sensors) [17], and electromyography (EMG) sensors [16][17][18][19] are widely used. For the latter, machine learning is frequently used for analysis of the physical information received from the front-end.…”

Section: Introductionmentioning

confidence: 99%

“…In [16], seven EMG and two accelerometer sensors were used, and five locomotion modes were classified by SVM and LDA, with an accuracy of 95.2%. In [17], EMG, GRF, interaction force and position sensors were integrated to classify five locomotion modes with Bayesian LDA and reached an accuracy of 96.1%.…”

Section: Introductionmentioning

confidence: 99%

“…To address this, in this article, we present a smart locomotion mode recognition system with a high experimentally demonstrated classification accuracy of 96.38% by utilizing only two EMG and two GRF flexible sensors and a machine learning algorithm. Compared to the previous studies [14][15][16][17][18][19], the developed technique benefits from simplicity by smartly designing measurement locations and selecting highly correlated features for the machine learning algorithm while maintaining a high detection accuracy.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Flexible and Wearable GRF and EMG Sensors Enabled Locomotion Mode Recognition for IoHT Based In-home Rehabilitation

Fang¹,

Wang²,

Gao³

2020

Preprint

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In order to quantify the manipulation process of acupuncture, in this article, a piezoelectric glove based wearable stress sensing system is presented. Served as the sensitive element with small volume and high tensile resistance, PVDF greatly meet the need of quantitative analysis. Through piezoelectric force sensing glove, the system is capable of detecting both perpendicular stress as well as shear stress. Besides, key parameters including peak stress at needle are detected and extracted, potentially allowing for a higher learning efficiency hence advancing the development of acupuncture.

show abstract

Section: A Experimental Protocolsupporting

confidence: 83%

Section: A Experimental Protocolmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Flexible and Wearable GRF and EMG Sensors Enabled Locomotion Mode Recognition for IoHT Based In-home Rehabilitation

Fang¹,

Wang²,

Gao³

2020

Preprint

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Multimodal Sensing in Stroke Motor Rehabilitation

Zhao

Wang

et al. 2023

Advanced Sensor Research

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Applying sensors in biomedical institutions and home‐based stroke rehabilitation is now a global research focus. In this review paper, the relationship between stroke diseases’ physiology mechanism and diverse sensors’ functionalities is detailed explained. The review starts by interpreting how stroke influences motion abilities and then introduces broadly adopted physical training methods. After, the working principles of sensors and their use to objectively provide patients’ body information for stroke rehabilitation status are discussed. The content of the paper aims to not only review the state‐of‐the‐art works of developing sensors for assisting in evaluating stroke patients’ status but also bridging the gap between medical staff and engineers.

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Gesture recognition based on surface electromyography‐featureimage

Cheng

et al. 2020

Concurrency and Computation

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For the problem of surface electromyography (sEMG) gesture recognition, considering the fact that the traditional machine learning model is susceptible to the sEMG feature extraction method, it is difficult to distinguish the subtle differences between similar gestures. The NinaPro DB1 dataset is used as the research object, and the sEMG feature image and the Convolutional Neural Network (CNN) are combined to recognize 52 gesture movements. The CNN model effectively solves the limitations of traditional machine learning in sEMG gesture recognition, and combines 1-dim convolution kernel to extract deep abstract features to improve the recognition effect. Finally, the simulation experiment shows that compared with the accuracy of the raw-sEMG images based on the CNN and the sEMG-feature-images based on the CNN and sEMG based on the traditional machine learning, the multi-sEMG-features image based on the CNN is the highest, which coming up to 82.54%.

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Recognition of walking environments and gait period by surface electromyography

Cited by 57 publications

References 35 publications

Flexible and Wearable GRF and EMG Sensors Enabled Locomotion Mode Recognition for IoHT Based In-home Rehabilitation

Flexible and Wearable GRF and EMG Sensors Enabled Locomotion Mode Recognition for IoHT Based In-home Rehabilitation

Multimodal Sensing in Stroke Motor Rehabilitation

Gesture recognition based on surface electromyography‐featureimage

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