Accurate recognition of lower limb ambulation mode based on surface electromyography and motion data using machine learning

Zhou, Bin; Wang, Hong; Hu, Fo; Feng, Naishi; Xi, Hailong; Zhang, Zhihan; Tang, Hao

doi:10.1016/j.cmpb.2020.105486

Cited by 29 publications

(19 citation statements)

References 46 publications

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“…The main advantage of this method is that it does not require any cognitive load or direct input from the user, making the interaction more intuitive and natural. For this method, generally joint sensors and IMU data (often from the upper body in persons with paraplegia) are processed by a machine learning or fuzzy logic algorithm to recognize the situation [47][48][49][50][51][52][53][54][55][56][57][58][59][60][61][62][63][64], although simpler threshold-based methods have also been proposed [65]. Sometimes, other types of signals such as the ground reaction forces or electromyography (EMG) are also used to infer the movement or the intention of the user [66][67][68][69][70][71].…”

Section: Movements Recognition (Mov)mentioning

confidence: 99%

Review of control strategies for lower-limb exoskeletons to assist gait

Baud

Manzoori

Ijspeert

et al. 2021

J NeuroEngineering Rehabil

161

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Background Many lower-limb exoskeletons have been developed to assist gait, exhibiting a large range of control methods. The goal of this paper is to review and classify these control strategies, that determine how these devices interact with the user. Methods In addition to covering the recent publications on the control of lower-limb exoskeletons for gait assistance, an effort has been made to review the controllers independently of the hardware and implementation aspects. The common 3-level structure (high, middle, and low levels) is first used to separate the continuous behavior (mid-level) from the implementation of position/torque control (low-level) and the detection of the terrain or user’s intention (high-level). Within these levels, different approaches (functional units) have been identified and combined to describe each considered controller. Results 291 references have been considered and sorted by the proposed classification. The methods identified in the high-level are manual user input, brain interfaces, or automatic mode detection based on the terrain or user’s movements. In the mid-level, the synchronization is most often based on manual triggers by the user, discrete events (followed by state machines or time-based progression), or continuous estimations using state variables. The desired action is determined based on position/torque profiles, model-based calculations, or other custom functions of the sensory signals. In the low-level, position or torque controllers are used to carry out the desired actions. In addition to a more detailed description of these methods, the variants of implementation within each one are also compared and discussed in the paper. Conclusions By listing and comparing the features of the reviewed controllers, this work can help in understanding the numerous techniques found in the literature. The main identified trends are the use of pre-defined trajectories for full-mobilization and event-triggered (or adaptive-frequency-oscillator-synchronized) torque profiles for partial assistance. More recently, advanced methods to adapt the position/torque profiles online and automatically detect terrains or locomotion modes have become more common, but these are largely still limited to laboratory settings. An analysis of the possible underlying reasons of the identified trends is also carried out and opportunities for further studies are discussed.

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Section: Movements Recognition (Mov)mentioning

confidence: 99%

Review of control strategies for lower-limb exoskeletons to assist gait

Baud

Manzoori

Ijspeert

et al. 2021

J NeuroEngineering Rehabil

161

View full text Add to dashboard Cite

show abstract

“…The empirical results demonstrated that SVM (95.2%) produced a better classification accuracy than KNN (89.2%). The outperformance of SVM on locomotion recognition was also pointed out by Zhou et al [ 29 ] and SVM had the highest accuracy (94.29%) compared with KNN and ensemble learning algorithms. The results of our study were consistent with previous studies.…”

Section: Discussionmentioning

confidence: 58%

“…A value of 80 ms was selected for SVM, KNN, and ANN whereas the window size was set to 50 ms for LDA in order to achieve an optimal recognition performance. The window size was significantly shorter than those used in previous studies where a data length of 150–300 ms was usually selected [ 21 , 27 , 29 , 31 , 32 ]. These studies chose a long sliding window for achieving a high recognition accuracy but sacrificed the response time.…”

Section: Discussionmentioning

confidence: 99%

The Role of Surface Electromyography in Data Fusion with Inertial Sensors to Enhance Locomotion Recognition and Prediction

Meng

Pang

Wang

et al. 2021

Sensors

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Locomotion recognition and prediction is essential for real-time human–machine interactive control. The integration of electromyography (EMG) with mechanical sensors could improve the performance of locomotion recognition. However, the potential of EMG in motion prediction is rarely discussed. This paper firstly investigated the effect of surface EMG on the prediction of locomotion while integrated with inertial data. We collected EMG signals of lower limb muscle groups and linear acceleration data of lower limb segments from ten healthy participants in seven locomotion activities. Classification models were built based on four machine learning methods—support vector machine (SVM), k-nearest neighbor (KNN), artificial neural network (ANN), and linear discriminant analysis (LDA)—where a major vote strategy and a content constraint rule were utilized for improving the online performance of the classification decision. We compared four classifiers and further investigated the effect of data fusion on the online locomotion classification. The results showed that the SVM model with a sliding window size of 80 ms achieved the best recognition performance. The fusion of EMG signals does not only improve the recognition accuracy of steady-state locomotion activity from 90% (using acceleration data only) to 98% (using data fusion) but also enables the prediction of the next steady locomotion (∼370 ms). The study demonstrates that the employment of EMG in locomotion recognition could enhance online prediction performance.

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“…The nonlinear classifier has a strong fitting ability; the shortcomings are insufficient data, easy overfitting, high computational complexity and poor interpretability. Based on the advantages and disadvantages of the above classifiers, this paper selected five classifiers for model training, namely, linear discriminant analysis (LDA) and KNN among the linear classifiers and DT [ 48 ], SVM [ 49 ] and ensemble learning (EL) [ 50 ] among the nonlinear classifiers.…”

Section: Classification Frameworkmentioning

confidence: 99%

Human Behavior Recognition Model Based on Feature and Classifier Selection

Gao

Huan

et al. 2021

Sensors

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With the rapid development of the computer and sensor field, inertial sensor data have been widely used in human activity recognition. At present, most relevant studies divide human activities into basic actions and transitional actions, in which basic actions are classified by unified features, while transitional actions usually use context information to determine the category. For the existing single method that cannot well realize human activity recognition, this paper proposes a human activity classification and recognition model based on smartphone inertial sensor data. The model fully considers the feature differences of different properties of actions, uses a fixed sliding window to segment the human activity data of inertial sensors with different attributes and, finally, extracts the features and recognizes them on different classifiers. The experimental results show that dynamic and transitional actions could obtain the best recognition performance on support vector machines, while static actions could obtain better classification effects on ensemble classifiers; as for feature selection, the frequency-domain feature used in dynamic action had a high recognition rate, up to 99.35%. When time-domain features were used for static and transitional actions, higher recognition rates were obtained, 98.40% and 91.98%, respectively.

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Accurate recognition of lower limb ambulation mode based on surface electromyography and motion data using machine learning

Cited by 29 publications

References 46 publications

Review of control strategies for lower-limb exoskeletons to assist gait

Review of control strategies for lower-limb exoskeletons to assist gait

The Role of Surface Electromyography in Data Fusion with Inertial Sensors to Enhance Locomotion Recognition and Prediction

Human Behavior Recognition Model Based on Feature and Classifier Selection

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