scholarly journals Locomotion Mode Recognition for Walking on Three Terrains Based on sEMG of Lower Limb and Back Muscles

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 2933
Author(s):  
Hui Zhou ◽  
Dandan Yang ◽  
Zhengyi Li ◽  
Dao Zhou ◽  
Junfeng Gao ◽  
...  
Keyword(s):  
Lower Limb ◽  
Classifier Fusion ◽  
Assistive Device ◽  
Gradient Boosting ◽  
Walking Ability ◽  
Phase Detection ◽  
Back Muscles ◽  
Time Frequency ◽  
Light Gradient ◽  
Gait Phase

Gait phase detection on different terrains is an essential procedure for amputees with a lower limb assistive device to restore walking ability. In the present study, the intent recognition of gait events on three terrains based on sEMG was presented. The class separability and robustness of time, frequency, and time-frequency domain features of sEMG signals from five leg and back muscles were quantitatively evaluated by statistical analysis to select the best features set. Then, ensemble learning method that combines the outputs of multiple classifiers into a single fusion-produced output was implemented. The results obtained from data collected from four human participants revealed that the light gradient boosting machine (LightGBM) algorithm has an average accuracy of 93.1%, a macro-F1 score of 0.929, and a calculation time of prediction of 15 ms in discriminating 12 different gait phases on three terrains. This was better than traditional voting-based multiple classifier fusion methods. LightGBM is a perfect choice for gait phase detection on different terrains in daily life.

Gait Phase Detection for Lower Limb Prosthetic Devices

2018 ◽  
pp. 201-205 ◽  
Author(s):  
Pablo E. Caicedo ◽  
Carlos F. Rengifo ◽  
Luís E. Rodríguez ◽  
Wilson A. Sierra
Keyword(s):  
Lower Limb ◽  
Phase Detection ◽  
Prosthetic Devices ◽  
Gait Phase

scholarly journals A Review of Gait Phase Detection Algorithms for Lower Limb Prostheses

Sensors ◽  
2020 ◽  
Vol 20 (14) ◽  
pp. 3972
Author(s):  
Huong Thi Thu Vu ◽  
Dianbiao Dong ◽  
Hoang-Long Cao ◽  
Tom Verstraten ◽  
Dirk Lefeber ◽  
...  
Keyword(s):  
Lower Limb ◽  
Detection Algorithm ◽  
Detection Methods ◽  
Phase Detection ◽  
Prosthetic Devices ◽  
Detection Algorithms ◽  
Gait Phase ◽  
Robotic Devices ◽  
Functional Gait ◽  
Lower Limb Prostheses

Fast and accurate gait phase detection is essential to achieve effective powered lower-limb prostheses and exoskeletons. As the versatility but also the complexity of these robotic devices increases, the research on how to make gait detection algorithms more performant and their sensing devices smaller and more wearable gains interest. A functional gait detection algorithm will improve the precision, stability, and safety of prostheses, and other rehabilitation devices. In the past years the state-of-the-art has advanced significantly in terms of sensors, signal processing, and gait detection algorithms. In this review, we investigate studies and developments in the field of gait event detection methods, more precisely applied to prosthetic devices. We compared advantages and limitations between all the proposed methods and extracted the relevant questions and recommendations about gait detection methods for future developments.

scholarly journals Gait Phase Detection for Lower-Limb Exoskeletons using Foot Motion Data from a Single Inertial Measurement Unit in Hemiparetic Individuals

Sensors ◽  
2019 ◽  
Vol 19 (13) ◽  
pp. 2988 ◽  
Author(s):  
Miguel D. Sánchez Sánchez Manchola ◽  
María J. Pinto Pinto Bernal ◽  
Marcela Munera ◽  
Carlos A. Cifuentes
Keyword(s):  
Lower Limb ◽  
Inertial Measurement Unit ◽  
Low Cost ◽  
Linear Acceleration ◽  
Measurement Unit ◽  
Phase Detection ◽  
Inertial Measurement ◽  
Phase Partitioning ◽  
Motion Data ◽  
Gait Phase

Due to the recent rise in the use of lower-limb exoskeletons as an alternative for gait rehabilitation, gait phase detection has become an increasingly important feature in the control of these devices. In addition, highly functional, low-cost recovery devices are needed in developing countries, since limited budgets are allocated specifically for biomedical advances. To achieve this goal, this paper presents two gait phase partitioning algorithms that use motion data from a single inertial measurement unit (IMU) placed on the foot instep. For these data, sagittal angular velocity and linear acceleration signals were extracted from nine healthy subjects and nine pathological subjects. Pressure patterns from force sensitive resistors (FSR) instrumented on a custom insole were used as reference values. The performance of a threshold-based (TB) algorithm and a hidden Markov model (HMM) based algorithm, trained by means of subject-specific and standardized parameters approaches, were compared during treadmill walking tasks in terms of timing errors and the goodness index. The findings indicate that HMM outperforms TB for this hardware configuration. In addition, the HMM-based classifier trained by an intra-subject approach showed excellent reliability for the evaluation of mean time, i.e., its intra-class correlation coefficient (ICC) was greater than 0 . 75 . In conclusion, the HMM-based method proposed here can be implemented for gait phase recognition, such as to evaluate gait variability in patients and to control robotic orthoses for lower-limb rehabilitation.

A Knowledge Based System for the Selection of Muscles for Gait Phase Detection using EMGs

2017 ◽  
Vol 12 (2) ◽  
pp. 18-45
Author(s):  
Vasileios Syrimpeis ◽  
Vassilis Moulianitis ◽  
Nikos A. Aspragathos ◽  
Elias Panagiotopoulos
Keyword(s):  
Conceptual Design ◽  
Lower Limb ◽  
Lower Limbs ◽  
Phase Detection ◽  
Knowledge Based System ◽  
Knowledge Based ◽  
Rehabilitation System ◽  
Gait Phase ◽  
Correction System ◽  
Selection Of

Purpose: This paper presents the development of a knowledge based system for the detection of gait phases based on EMGs from muscles of the lower limb. Methods: An empirical analysis of the EMG characteristics for the most representative muscle of every muscle group concerning their suitability for the gait phase detection is presented. The same approach is applied to every lower limb muscle where an EMG could be received. The entities and the decision-making mechanism of the knowledge based system is presented in a formal way. Results: A knowledge based system is built upon the knowledge acquired from this analysis. Finally, an example is presented where the developed knowledge based system is used to support the conceptual design of a drop foot correction system. Conclusions: The knowledge based system can be used in the conceptual design of any rehabilitation system for lower limb disabilities using EMG signals from the lower limbs.

scholarly journals Surface Electromyography and Electroencephalogram-Based Gait Phase Recognition and Correlations Between Cortical and Locomotor Muscle in the Seven Gait Phases

2021 ◽  
Vol 15 ◽  
Author(s):  
Pengna Wei ◽  
Jinhua Zhang ◽  
Baozeng Wang ◽  
Jun Hong
Keyword(s):  
Lower Limb ◽  
Surface Electromyography ◽  
Theoretical Basis ◽  
Interaction Analysis ◽  
Beta Band ◽  
Mean Power ◽  
Mean Power Frequency ◽  
Time Frequency ◽  
Gait Phase ◽  
Phase Recognition

The classification of gait phases based on surface electromyography (sEMG) and electroencephalogram (EEG) can be used to the control systems of lower limb exoskeletons for the rehabilitation of patients with lower limb disorders. In this study, the slope sign change (SSC) and mean power frequency (MPF) features of EEG and sEMG were used to recognize the seven gait phases [loading response (LR), mid-stance (MST), terminal stance (TST), pre-swing (PSW), initial swing (ISW), mid-swing (MSW), and terminal swing (TSW)]. Previous researchers have found that the cortex is involved in the regulation of treadmill walking. However, corticomuscular interaction analysis in a high level of gait phase granularity remains lacking in the time–frequency domain, and the feasibility of gait phase recognition based on EEG combined with sEMG is unknown. Therefore, the time–frequency cross mutual information (TFCMI) method was applied to research the theoretical basis of gait control in seven gait phases using beta-band EEG and sEMG data. We firstly found that the feature set comprising SSC of EEG as well as SSC and MPF of sEMG was robust for the recognition of seven gait phases under three different walking speeds. Secondly, the distribution of TFCMI values in eight topographies (eight muscles) was different at PSW and TSW phases. Thirdly, the differences of corticomuscular interaction between LR and MST and between TST and PSW of eight muscles were not significant. These insights enrich previous findings of the authors who have carried out gait phase recognition and provide a theoretical basis for gait recognition based on EEG and sEMG.

Using a saddle-assistive device equipped with mechanical orthosis for walking of the person with incomplete spinal cord injury

2021 ◽  
pp. 095441192110201
Author(s):  
Akbar Hojjati Najafabadi ◽  
Saeid Amini ◽  
Farzam Farahmand
Keyword(s):  
Spinal Cord ◽  
Spinal Cord Injury ◽  
Reaction Force ◽  
Assistive Devices ◽  
Assistive Device ◽  
Walking Ability ◽  
Vertical Force ◽  
Incomplete Spinal Cord Injury ◽  
Combined Application ◽  
Cord Injury

The majority of the people with incomplete spinal cord injury lose their walking ability, due to the weakness of their muscle motors in providing torque. As a result, developing assistive devices to improve their conditionis of great importance. In this study, a combined application of the saddle-assistive device (S-AD) and mechanical medial linkage or thosis was evaluated to improve the walking ability in patients with spinal cord injury in the gait laboratory. This mobile assistive device is called the saddle-assistive device equipped with medial linkage or thosis (S-ADEM). In this device, a mechanical orthosis was used in a wheeled walker as previously done in the literature. Initially, for evaluation of the proposed assistive device, the experimental results related to the forces and torques exerted on the feet and upper limbs of a person with the incomplete Spinal Cord Injury (SCI) during walking usingthe standard walker were compared with an those obtained from using the S-ADEM on an able-bodied subject. It was found that using this combination of assistive devices decreases the vertical force and torque on the foot at the time of walking by 53% and 48%, respectively compared to a standard walker. Moreover, the hand-reaction force on the upper limb was negligible instanding and walking positions usingthe introduced device. The findings of this study revealed that the walking ability of the patients with incomplete SCI was improved using the proposed device, which is due to the bodyweight support and the motion technology used in it.

scholarly journals A Multi-Class Automatic Sleep Staging Method Based on Photoplethysmography Signals

Entropy ◽  
2021 ◽  
Vol 23 (1) ◽  
pp. 116
Author(s):  
Xiangfa Zhao ◽  
Guobing Sun
Keyword(s):  
Time Domain ◽  
Single Channel ◽  
Kappa Statistic ◽  
Gradient Boosting ◽  
Sleep Staging ◽  
Challenging Problem ◽  
Sleep State ◽  
Light Gradient ◽  
Gradient Boosting Machine ◽  
The Time Domain

Automatic sleep staging with only one channel is a challenging problem in sleep-related research. In this paper, a simple and efficient method named PPG-based multi-class automatic sleep staging (PMSS) is proposed using only a photoplethysmography (PPG) signal. Single-channel PPG data were obtained from four categories of subjects in the CAP sleep database. After the preprocessing of PPG data, feature extraction was performed from the time domain, frequency domain, and nonlinear domain, and a total of 21 features were extracted. Finally, the Light Gradient Boosting Machine (LightGBM) classifier was used for multi-class sleep staging. The accuracy of the multi-class automatic sleep staging was over 70%, and the Cohen’s kappa statistic k was over 0.6. This also showed that the PMSS method can also be applied to stage the sleep state for patients with sleep disorders.

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