Intent pattern recognition of lower-limb motion based on mechanical sensors

Liu, Zuojun; Lin, Wei Chun; Geng, Yanli; Yang, Peng

doi:10.1109/jas.2017.7510619

Cited by 54 publications

(33 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…An obvious thing to add on the list would be the inclusion of more daily life conditions for testing the devices. Examples of these would be different angles of approaches towards stairs or slope [9,28], different staircases [18] or load bearing changes [51]. A good extension for many of the studies included in this review would be a test of the algorithms outside the laboratory.…”

Section: Recommendations For Generalization To Daily Life Conditionsmentioning

confidence: 99%

See 1 more Smart Citation

Machine Learning Approaches for Activity Recognition and/or Activity Prediction in Locomotion Assistive Devices—A Systematic Review

Labarrière

Thomas

Calistri³

et al. 2020

Sensors

View full text Add to dashboard Cite

Locomotion assistive devices equipped with a microprocessor can potentially automatically adapt their behavior when the user is transitioning from one locomotion mode to another. Many developments in the field have come from machine learning driven controllers on locomotion assistive devices that recognize/predict the current locomotion mode or the upcoming one. This review synthesizes the machine learning algorithms designed to recognize or to predict a locomotion mode in order to automatically adapt the behavior of a locomotion assistive device. A systematic review was conducted on the Web of Science and MEDLINE databases (as well as in the retrieved papers) to identify articles published between 1 January 2000 to 31 July 2020. This systematic review is reported in accordance with the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines and is registered on Prospero (CRD42020149352). Study characteristics, sensors and algorithms used, accuracy and robustness were also summarized. In total, 1343 records were identified and 58 studies were included in this review. The experimental condition which was most often investigated was level ground walking along with stair and ramp ascent/descent activities. The machine learning algorithms implemented in the included studies reached global mean accuracies of around 90%. However, the robustness of those algorithms seems to be more broadly evaluated, notably, in everyday life. We also propose some guidelines for homogenizing future reports.

show abstract

Section: Recommendations For Generalization To Daily Life Conditionsmentioning

confidence: 99%

“…Once again, very few researchers have investigated this condition. One researcher who has taken a step in this direction is Liu et al, 2017 [28].…”

Section: Recommendations For Generalization To Daily Life Conditionsmentioning

confidence: 99%

Machine Learning Approaches for Activity Recognition and/or Activity Prediction in Locomotion Assistive Devices—A Systematic Review

Labarrière

Thomas

Calistri³

et al. 2020

Sensors

View full text Add to dashboard Cite

show abstract

“…For example, when it comes to the variable stiffness mechanisms, a new structure combined with the parallel redundant mechanisms and variable impedance actuator is presented in [19], where a stiffness and position control for two degrees of freedom (DOF) is achieved by three actuators with flexible elements. By utilizing mechanical sensors or biological signals [20][21][22], finite state controllers decompose a gait into several different motion stages and tune a series of corresponding static parameters for each state of every subject. Each motion stage needs at least three parameters: stiffness, damping, and spring balance angle.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Robust Force Position Control for Flexible Active Prosthetic Knee Using Gait Trajectory

et al. 2020

View full text Add to dashboard Cite

Active prosthetic knees (APKs) are widely used in the past decades. However, it is still challenging to make them more natural and controllable because: (1) most existing APKs that use rigid actuators have difficulty obtaining more natural walking; and (2) traditional finite-state impedance control has difficulty adjusting parameters for different motions and users. In this paper, a flexible APK with a compact variable stiffness actuator (VSA) is designed for obtaining more flexible bionic characteristics. The VSA joint is implemented by two motors of different sizes, which connect the knee angle and the joint stiffness. Considering the complexity of prothetic lower limb control due to unknown APK dynamics, as well as strong coupling between biological joints and prosthetic joints, an adaptive robust force/position control method is designed for generating a desired gait trajectory of the prosthesis. It can operate without the explicit model of the system dynamics and multiple tuning parameters of different gaits. The proposed model-free scheme utilizes the time-delay estimation technique, sliding mode control, and fuzzy neural network to realize finite-time convergence and gait trajectory tracking. The virtual prototype of APK was established in ADAMS as a testing platform and compared with two traditional time-delay control schemes. Some demonstrations are illustrated, which show that the proposed method has superior tracking characteristics and stronger robustness under uncertain disturbances within the trajectory error in ± 0 . 5 degrees. The VSA joint can reduce energy consumption by adjusting stiffness appropriately. Furthermore, the feasibility of this method was verified in a human–machine hybrid control model.

show abstract

“…At present, most scholars in the field of prosthetic control still mainly focus on the recognition and control of walking state [ 6 , 7 , 8 , 9 , 10 ]. Little research in the field of bicycle riding of lower-limb prosthesis wears have been reported.…”

Section: Introductionmentioning

confidence: 99%

Bicycling Phase Recognition for Lower Limb Amputees Using Support Vector Machine Optimized by Particle Swarm Optimization

Liu

Gao

et al. 2020

Sensors

Self Cite

View full text Add to dashboard Cite

A novel method for recognizing the phases in bicycling of lower limb amputees using support vector machine (SVM) optimized by particle swarm optimization (PSO) is proposed in this paper. The method is essential for enhanced prosthetic knee joint control for lower limb amputees in carrying out bicycling activity. Some wireless wearable accelerometers and a knee joint angle sensor are installed in the prosthesis to obtain data on the knee joint and ankle joint horizontal, vertical acceleration signal and knee joint angle. In order to overcome the problem of high noise content in the collected data, a soft-hard threshold filter was used to remove the noise caused by the vibration. The filtered information is then used to extract the multi-dimensional feature vector for the training of SVM for performing bicycling phase recognition. The SVM is optimized by PSO to enhance its classification accuracy. The recognition accuracy of the PSO-SVM classification model on testing data is 93%, which is much higher than those of BP, SVM and PSO-BP classification models.

show abstract

Intent pattern recognition of lower-limb motion based on mechanical sensors

Cited by 54 publications

References 28 publications

Machine Learning Approaches for Activity Recognition and/or Activity Prediction in Locomotion Assistive Devices—A Systematic Review

Machine Learning Approaches for Activity Recognition and/or Activity Prediction in Locomotion Assistive Devices—A Systematic Review

Adaptive Robust Force Position Control for Flexible Active Prosthetic Knee Using Gait Trajectory

Bicycling Phase Recognition for Lower Limb Amputees Using Support Vector Machine Optimized by Particle Swarm Optimization

Contact Info

Product

Resources

About