Human motion intention recognition based on EMG signal and angle signal

Human motion intention recognition is a key to achieve perfect human-machine coordination and wearing comfort of wearable robots. Surface electromyography (sEMG), as a bioelectrical signal, generates prior to the corresponding motion and reflects the human motion intention directly. Thus, a better human-machine interaction can be achieved by using sEMG based motion intention recognition. In this paper, we review and discuss the state of the art of the sEMG based motion intention recognition that is mainly used in detail. According to the method adopted, motion intention recognition is divided into two groups: sEMG-driven musculoskeletal (MS) model based motion intention recognition and machine learning (ML) model based motion intention recognition. The specific models and recognition effects of each study are analyzed and systematically compared. Finally, a discussion of the existing problems in the current studies, major advances, and future challenges is presented.

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A Novel Motion Intention Recognition Approach for Soft Exoskeleton via IMU

Electronics ◽

10.3390/electronics9122176 ◽

2020 ◽

Vol 9 (12) ◽

pp. 2176

Author(s):

Lu Zhu ◽

Zhuo Wang ◽

Zhigang Ning ◽

Yu Zhang ◽

Yida Liu ◽

...

Keyword(s):

Inertial Sensor ◽

Human Motion ◽

Measurement Unit ◽

Recognition Method ◽

Metabolic System ◽

Intention Recognition ◽

Motion Modes ◽

Deep Convolution Neural Network ◽

Multi Mode ◽

Motion Intention

To solve the complexity of the traditional motion intention recognition method using a multi-mode sensor signal and the lag of the recognition process, in this paper, an inertial sensor-based motion intention recognition method for a soft exoskeleton is proposed. Compared with traditional motion recognition, in addition to the classic five kinds of terrain, the recognition of transformed terrain is also added. In the mode acquisition, the sensors’ data in the thigh and calf in different motion modes are collected. After a series of data preprocessing, such as data filtering and normalization, the sliding window is used to enhance the data, so that each frame of inertial measurement unit (IMU) data keeps the last half of the previous frame’s historical information. Finally, we designed a deep convolution neural network which can learn to extract discriminant features from temporal gait period to classify different terrain. The experimental results show that the proposed method can recognize the pose of the soft exoskeleton in different terrain, including walking on flat ground, going up and downstairs, and up and down slopes. The recognition accuracy rate can reach 97.64%. In addition, the recognition delay of the conversion pattern, which is converted between the five modes, only accounts for 23.97% of a gait cycle. Finally, the oxygen consumption was measured by the wearable metabolic system (COSMED K5, The Metabolic Company, Rome, Italy), and compared with that without an identification method; the net metabolism was reduced by 5.79%. The method in this paper can greatly improve the control performance of the flexible lower extremity exoskeleton system and realize the natural and seamless state switching of the exoskeleton between multiple motion modes according to the human motion intention.

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Multimodal Sensor Motion Intention Recognition Based on Three-Dimensional Convolutional Neural Network Algorithm

Computational Intelligence and Neuroscience ◽

10.1155/2021/5690868 ◽

2021 ◽

Vol 2021 ◽

pp. 1-11

Author(s):

Mofei Wen ◽

Yuwei Wang

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Three Dimensional ◽

Short Term ◽

Spatiotemporal Features ◽

Emg Signal ◽

Intention Recognition ◽

Wide Range ◽

Motion Intention

With the development of microelectronic technology and computer systems, the research of motion intention recognition based on multimodal sensors has attracted the attention of the academic community. Deep learning and other nonlinear neural network models have a wide range of applications in big data sets. We propose a motion intention recognition algorithm based on multimodal long-term and short-term spatiotemporal feature fusion. We divide the target data into multiple segments and use a three-dimensional convolutional neural network to extract the short-term spatiotemporal features. The three types of features of the same segment are fused together and input into the LSTM network for time-series modeling to further fuse the features to obtain multimodal long-term spatiotemporal features with higher discrimination. According to the lower limb movement pattern recognition model, the minimum number of muscles and EMG signal characteristics required to accurately recognize the movement state of the lower limbs are determined. This minimizes the redundant calculation cost of the model and ensures the real-time output of the system results.

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Hierarchical Shared Control of Cane-Type Walking-Aid Robot

Journal of Healthcare Engineering ◽

10.1155/2017/8932938 ◽

2017 ◽

Vol 2017 ◽

pp. 1-11 ◽

Cited By ~ 1

Author(s):

Chunjing Tao ◽

Qingyang Yan ◽

Yitong Li

Keyword(s):

Potential Field ◽

Control Method ◽

Sensory System ◽

Interaction Force ◽

Human Motion ◽

Shared Control ◽

Artificial Potential Field ◽

Intention Recognition ◽

Walking Aid ◽

Motion Intention

A hierarchical shared-control method of the walking-aid robot for both human motion intention recognition and the obstacle emergency-avoidance method based on artificial potential field (APF) is proposed in this paper. The human motion intention is obtained from the interaction force measurements of the sensory system composed of 4 force-sensing registers (FSR) and a torque sensor. Meanwhile, a laser-range finder (LRF) forward is applied to detect the obstacles and try to guide the operator based on the repulsion force calculated by artificial potential field. An obstacle emergency-avoidance method which comprises different control strategies is also assumed according to the different states of obstacles or emergency cases. To ensure the user’s safety, the hierarchical shared-control method combines the intention recognition method with the obstacle emergency-avoidance method based on the distance between the walking-aid robot and the obstacles. At last, experiments validate the effectiveness of the proposed hierarchical shared-control method.

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Multi-sensor based human motion intention recognition algorithm for walking-aid robot

2015 IEEE International Conference on Robotics and Biomimetics (ROBIO) ◽

10.1109/robio.2015.7419074 ◽

2015 ◽

Cited By ~ 4

Author(s):

Wenxia Xu ◽

Jian Huang ◽

Qingyang Yan

Keyword(s):

Recognition Algorithm ◽

Human Motion ◽

Intention Recognition ◽

Walking Aid ◽

Motion Intention

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Design and Voluntary Motion Intention Estimation of a Novel Wearable Full-Body Flexible Exoskeleton Robot

Mobile Information Systems ◽

10.1155/2017/8682168 ◽

2017 ◽

Vol 2017 ◽

pp. 1-11 ◽

Cited By ~ 3

Author(s):

Chunjie Chen ◽

Xinyu Wu ◽

Du-xin Liu ◽

Wei Feng ◽

Can Wang

Keyword(s):

Gait Cycle ◽

Human Motion ◽

Experimental Result ◽

Motion Patterns ◽

Exoskeleton Robot ◽

Gait Phase ◽

Phase Recognition ◽

Motion Intention ◽

Full Body ◽

Testing Set

The wearable full-body exoskeleton robot developed in this study is one application of mobile cyberphysical system (CPS), which is a complex mobile system integrating mechanics, electronics, computer science, and artificial intelligence. Steel wire was used as the flexible transmission medium and a group of special wire-locking structures was designed. Additionally, we designed passive joints for partial joints of the exoskeleton. Finally, we proposed a novel gait phase recognition method for full-body exoskeletons using only joint angular sensors, plantar pressure sensors, and inclination sensors. The method consists of four procedures. Firstly, we classified the three types of main motion patterns: normal walking on the ground, stair-climbing and stair-descending, and sit-to-stand movement. Secondly, we segregated the experimental data into one gait cycle. Thirdly, we divided one gait cycle into eight gait phases. Finally, we built a gait phase recognition model based on k-Nearest Neighbor perception and trained it with the phase-labeled gait data. The experimental result shows that the model has a 98.52% average correct rate of classification of the main motion patterns on the testing set and a 95.32% average correct rate of phase recognition on the testing set. So the exoskeleton robot can achieve human motion intention in real time and coordinate its movement with the wearer.

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Control of Upper-Limb Power-Assist Exoskeleton Using a Human-Robot Interface Based on Motion Intention Recognition

IEEE Transactions on Automation Science and Engineering ◽

10.1109/tase.2015.2466634 ◽

2015 ◽

Vol 12 (4) ◽

pp. 1257-1270 ◽

Cited By ~ 74

Author(s):

Jian Huang ◽

Weiguang Huo ◽

Wenxia Xu ◽

Samer Mohammed ◽

Yacine Amirat

Keyword(s):

Upper Limb ◽

Intention Recognition ◽

Motion Intention

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HYBRID CLASSIFICATION STRATEGY OF EMG SIGNALS FOR ROBOTIC HAND CONTROL

Biomedical Engineering Applications Basis and Communications ◽

10.4015/s1016237221500150 ◽

2021 ◽

pp. 2150015

Author(s):

Fazia sbargoud ◽

Mohamed Djeha ◽

Mohamed Guiatni ◽

Noureddine Ababou

Keyword(s):

Degrees Of Freedom ◽

Wavelet Packet ◽

Hybrid Approach ◽

Human Motion ◽

Robotic Hand ◽

User Intention ◽

Emg Signal ◽

Hybrid Classification ◽

Electromyographic Signal ◽

Artificial Neural Network Ann

Among the different bio-signals modalities, Electromyographic signal (EMG) has been one of the frequently used signals in the bio-robotics applications field. This is due to the fact that the EMG reflects directly the muscle activity of the user following the human motion intention. Consequently, the decoding of this intention is an essential task for controlling devices such as prosthetic hands and exoskeletons, based on EMG signals. This paper deals with the processing of EMG signals of the forearm muscles, in order to control two degrees of freedom (2 DoFs) robotic hand. The main contribution of this paper is the proposal of a hybrid approach that combines a pattern and a non-pattern recognition-based strategy. The proposed approach aims to take advantage of both strategies and overcome their shortcomings leading to a better analysis of the user movement intention. The EMG recorded signals are processed for feature extraction based on a Wavelet Packet Decomposition (WPD) method and classification using an Artificial Neural Network (ANN). Furthermore, we investigate the effect of the various parameters such as the applied force level, the number of the EMG channels and the window length of the EMG signal. The proposed approach is validated experimentally under realistic conditions. Very interesting results have been obtained for user intention decoding.

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