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Hu X, Ma Z, Zhao F, Guo S. Recent Advances in Self-Powered Wearable Flexible Sensors for Human Gaits Analysis. NANOMATERIALS (BASEL, SWITZERLAND) 2024; 14:1173. [PMID: 39057851 PMCID: PMC11279839 DOI: 10.3390/nano14141173] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/06/2024] [Revised: 07/05/2024] [Accepted: 07/08/2024] [Indexed: 07/28/2024]
Abstract
The rapid progress of flexible electronics has met the growing need for detecting human movement information in exoskeleton auxiliary equipment. This study provides a review of recent advancements in the design and fabrication of flexible electronics used for human motion detection. Firstly, a comprehensive introduction is provided on various self-powered wearable flexible sensors employed in detecting human movement information. Subsequently, the algorithms utilized to provide feedback on human movement are presented, followed by a thorough discussion of their methods and effectiveness. Finally, the review concludes with perspectives on the current challenges and opportunities in implementing self-powered wearable flexible sensors in exoskeleton technology.
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Affiliation(s)
- Xiaohe Hu
- School of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University, Beijing 100044, China; (X.H.); (F.Z.)
| | - Zhiqiang Ma
- Department of Biomedical Engineering, City University of Hong Kong, 83 Tat Chee Avenue, Kowloon, Hong Kong 999077, China
| | - Fuqun Zhao
- School of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University, Beijing 100044, China; (X.H.); (F.Z.)
| | - Sheng Guo
- School of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University, Beijing 100044, China; (X.H.); (F.Z.)
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2
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Choi S, Ko C, Kong K. Walking-Speed-Adaptive Gait Phase Estimation for Wearable Robots. SENSORS (BASEL, SWITZERLAND) 2023; 23:8276. [PMID: 37837106 PMCID: PMC10575403 DOI: 10.3390/s23198276] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Revised: 10/02/2023] [Accepted: 10/03/2023] [Indexed: 10/15/2023]
Abstract
This paper introduces a Gait Phase Estimation Module (GPEM) and its real-time algorithm designed to estimate gait phases continuously and monotonically across a range of walking speeds and accelerations/decelerations. To address the challenges of real-world applications, we propose a speed-adaptive online gait phase estimation algorithm, which enables precise estimation of gait phases during both constant speed locomotion and dynamic speed changes. Experimental verification demonstrates that the proposed method offers smooth, continuous, and repetitive gait phase estimation when compared to conventional approaches such as the phase portrait method and time-based estimation. The proposed method achieved a 48% reduction in gait phase deviation compared to time-based estimation and a 48.29% reduction compared to the phase portrait method. The proposed algorithm is integrated within the GPEM, allowing for its versatile application in controlling gait assistive robots without incurring additional computational burden. The results of this study contribute to the development of robust and efficient gait phase estimation techniques for various robotic applications.
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Affiliation(s)
| | | | - Kyoungchul Kong
- Department of Mechanical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea; (S.C.); (C.K.)
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3
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Hu X, Duan Q, Tang J, Chen G, Zhao Z, Sun Z, Chen C, Qu X. A Low-Cost Instrumented Shoe System for Gait Phase Detection Based on Foot Plantar Pressure Data. IEEE JOURNAL OF TRANSLATIONAL ENGINEERING IN HEALTH AND MEDICINE 2023; 12:84-96. [PMID: 38089000 PMCID: PMC10712682 DOI: 10.1109/jtehm.2023.3319576] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Revised: 09/18/2023] [Accepted: 09/21/2023] [Indexed: 12/18/2023]
Abstract
This paper presents a novel low-cost and fully-portable instrumented shoe system for gait phase detection. The instrumented shoe consists of 174 independent sensing units constructed based on an off-the-shelf force-sensitive film known as the Velostat conductive copolymer. A zero potential method was implemented to address the crosstalk effect among the matrix-formed sensing arrays. A customized algorithm for gait event and phase detection was developed to estimate stance sub-phases including initial contact, flat foot, and push off. Experiments were carried out to evaluate the performance of the proposed instrumented shoe system in gait phase detection for both straight-line walking and turning walking. The results showed that the mean absolute time differences between the estimated phases by the proposed instrumented shoe system and the reference measurement ranged from 45 to 58 ms during straight-line walking and from 51 to 77 ms during turning walking, which were comparable to the state of art.Clinical and Translational Impact Statement-By allowing convenient gait monitoring in home healthcare settings, the proposed system enables extensive ADL data collection and facilitates developing effective treatment and rehabilitation strategies for patients with movement disorders.
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Affiliation(s)
- Xinyao Hu
- Institute of Human Factors and Ergonomics, College of Mechatronics and Control EngineeringShenzhen UniversityShenzhen518060China
| | - Qingsong Duan
- Institute of Human Factors and Ergonomics, College of Mechatronics and Control EngineeringShenzhen UniversityShenzhen518060China
| | - Junpeng Tang
- Institute of Human Factors and Ergonomics, College of Mechatronics and Control EngineeringShenzhen UniversityShenzhen518060China
| | - Gengshu Chen
- Institute of Human Factors and Ergonomics, College of Mechatronics and Control EngineeringShenzhen UniversityShenzhen518060China
| | - Zhong Zhao
- Institute of Human Factors and Ergonomics, College of Mechatronics and Control EngineeringShenzhen UniversityShenzhen518060China
| | - Zhenglong Sun
- School of Science and EngineeringThe Chinese University of Hong KongShenzhen518172China
| | - Chao Chen
- Department of OrthopedicsSchool of Traditional Chinese MedicineSouthern Medical UniversityGuangzhouGuangdong510515China
| | - Xingda Qu
- Institute of Human Factors and Ergonomics, College of Mechatronics and Control EngineeringShenzhen UniversityShenzhen518060China
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4
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Xu S, Dong H, Xu R, Meng L, Ming D. A Real-Time Gait Phase Detection Method Based on BiLSTM-Attention Model. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-4. [PMID: 38083747 DOI: 10.1109/embc40787.2023.10340216] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
Real-time gait phase detection is essential to achieve accurate and stable walking assistance in intelligent rehabilitation training for patients with motor disorders. This study proposed an efficient real-time detection method to detect three gait phases (loading response, stance, and swing) based on a bidirectional long short-term memory network with an attention layer (BiLSTM-Attention). We validated our method on a public dataset where eight healthy subjects' data during treadmill walking were employed. A single inertial measurement unit (IMU) was attached to the shank to measure the sagittal plane acceleration of the lower leg and the angular velocity around the central lateral axis. These data were transposed and segmented into data sequences based on labels using a sliding window method. The data from 8 participants were divided into the training, validation, and test sets (5:1:2). Results showed the average recognition accuracy of the proposed model on new subjects was 97.40% with an average time delay of 15.7±10.1ms, showing the method's potential to be applied for practice use.
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5
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Zhao H, Qiu Z, Peng D, Wang F, Wang Z, Qiu S, Shi X, Chu Q. Prediction of Joint Angles Based on Human Lower Limb Surface Electromyography. SENSORS (BASEL, SWITZERLAND) 2023; 23:5404. [PMID: 37420573 DOI: 10.3390/s23125404] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Revised: 06/02/2023] [Accepted: 06/05/2023] [Indexed: 07/09/2023]
Abstract
Wearable exoskeletons can help people with mobility impairments by improving their rehabilitation. As electromyography (EMG) signals occur before movement, they can be used as input signals for the exoskeletons to predict the body's movement intention. In this paper, the OpenSim software is used to determine the muscle sites to be measured, i.e., rectus femoris, vastus lateralis, semitendinosus, biceps femoris, lateral gastrocnemius, and tibial anterior. The surface electromyography (sEMG) signals and inertial data are collected from the lower limbs while the human body is walking, going upstairs, and going uphill. The sEMG noise is reduced by a wavelet-threshold-based complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) reduction algorithm, and the time-domain features are extracted from the noise-reduced sEMG signals. Knee and hip angles during motion are calculated using quaternions through coordinate transformations. The random forest (RF) regression algorithm optimized by cuckoo search (CS), shortened as CS-RF, is used to establish the prediction model of lower limb joint angles by sEMG signals. Finally, root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R2) are used as evaluation metrics to compare the prediction performance of the RF, support vector machine (SVM), back propagation (BP) neural network, and CS-RF. The evaluation results of CS-RF are superior to other algorithms under the three motion scenarios, with optimal metric values of 1.9167, 1.3893, and 0.9815, respectively.
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Affiliation(s)
- Hongyu Zhao
- Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education, Dalian University of Technology, Dalian 116024, China
- School of Control Science and Engineering, Dalian University of Technology, Dalian 116024, China
| | - Zhibo Qiu
- Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education, Dalian University of Technology, Dalian 116024, China
- School of Control Science and Engineering, Dalian University of Technology, Dalian 116024, China
| | - Daoyong Peng
- Neurology Department, Dalian Municipal Central Hospital, Dalian 116024, China
| | - Fang Wang
- Neurology Department, Dalian Municipal Central Hospital, Dalian 116024, China
| | - Zhelong Wang
- Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education, Dalian University of Technology, Dalian 116024, China
- School of Control Science and Engineering, Dalian University of Technology, Dalian 116024, China
| | - Sen Qiu
- Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education, Dalian University of Technology, Dalian 116024, China
- School of Control Science and Engineering, Dalian University of Technology, Dalian 116024, China
| | - Xin Shi
- Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education, Dalian University of Technology, Dalian 116024, China
- School of Control Science and Engineering, Dalian University of Technology, Dalian 116024, China
| | - Qinghao Chu
- Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education, Dalian University of Technology, Dalian 116024, China
- School of Control Science and Engineering, Dalian University of Technology, Dalian 116024, China
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6
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Vu HTT, Cao HL, Dong D, Verstraten T, Geeroms J, Vanderborght B. Comparison of machine learning and deep learning-based methods for locomotion mode recognition using a single inertial measurement unit. Front Neurorobot 2022; 16:923164. [PMID: 36524219 PMCID: PMC9745042 DOI: 10.3389/fnbot.2022.923164] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Accepted: 09/06/2022] [Indexed: 09/09/2023] Open
Abstract
Locomotion mode recognition provides the prosthesis control with the information on when to switch between different walking modes, whereas the gait phase detection indicates where we are in the gait cycle. But powered prostheses often implement a different control strategy for each locomotion mode to improve the functionality of the prosthesis. Existing studies employed several classical machine learning methods for locomotion mode recognition. However, these methods were less effective for data with complex decision boundaries and resulted in misclassifications of motion recognition. Deep learning-based methods potentially resolve these limitations as it is a special type of machine learning method with more sophistication. Therefore, this study evaluated three deep learning-based models for locomotion mode recognition, namely recurrent neural network (RNN), long short-term memory (LSTM) neural network, and convolutional neural network (CNN), and compared the recognition performance of deep learning models to the machine learning model with random forest classifier (RFC). The models are trained from data of one inertial measurement unit (IMU) placed on the lower shanks of four able-bodied subjects to perform four walking modes, including level ground walking (LW), standing (ST), and stair ascent/stair descent (SA/SD). The results indicated that CNN and LSTM models outperformed other models, and these models were promising for applying locomotion mode recognition in real-time for robotic prostheses.
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Affiliation(s)
- Huong Thi Thu Vu
- Brubotics, Vrije Universiteit Brussel and imec, Brussels, Belgium
- Faculty of Electronics Engineering Technology, Hanoi University of Industry, Hanoi, Vietnam
| | - Hoang-Long Cao
- Brubotics, Vrije Universiteit Brussel and Flanders Make, Brussels, Belgium
- College of Engineering Technology, Can Tho University, Can Tho, Vietnam
| | - Dianbiao Dong
- School of Mechanical Engineering, Northwestern Polytechnical University, Xi'an, China
| | - Tom Verstraten
- Brubotics, Vrije Universiteit Brussel and Flanders Make, Brussels, Belgium
| | - Joost Geeroms
- Brubotics, Vrije Universiteit Brussel and Flanders Make, Brussels, Belgium
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7
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Zhang X, Tricomi E, Missiroli F, Lotti N, Bokranz C, Nicklas D, Masia L. Enhancing Gait Assistance Control Robustness of a Hip Exosuit by Means of Machine Learning. IEEE Robot Autom Lett 2022. [DOI: 10.1109/lra.2022.3183791] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Affiliation(s)
- Xiaohui Zhang
- Institut für Technische Informatik (ZITI), Heidelberg University, Heidelberg, Deutschland
| | - Enrica Tricomi
- Institut für Technische Informatik (ZITI), Heidelberg University, Heidelberg, Deutschland
| | - Francesco Missiroli
- Institut für Technische Informatik (ZITI), Heidelberg University, Heidelberg, Deutschland
| | - Nicola Lotti
- Institut für Technische Informatik (ZITI), Heidelberg University, Heidelberg, Deutschland
| | - Casimir Bokranz
- Institut für Technische Informatik (ZITI), Heidelberg University, Heidelberg, Deutschland
| | - Daniela Nicklas
- Institut für Technische Informatik (ZITI), Heidelberg University, Heidelberg, Deutschland
| | - Lorenzo Masia
- Institut für Technische Informatik (ZITI), Heidelberg University, Heidelberg, Deutschland
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8
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Calle-Siguencia J, Callejas-Cuervo M, García-Reino S. Integration of Inertial Sensors in a Lower Limb Robotic Exoskeleton. SENSORS (BASEL, SWITZERLAND) 2022; 22:4559. [PMID: 35746340 PMCID: PMC9229016 DOI: 10.3390/s22124559] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/06/2022] [Revised: 06/09/2022] [Accepted: 06/10/2022] [Indexed: 06/15/2023]
Abstract
Motion assistance exoskeletons are designed to support the joint movement of people who perform repetitive tasks that cause damage to their health. To guarantee motion accompaniment, the integration between sensors and actuators should ensure a near-zero delay between the signal acquisition and the actuator response. This study presents the integration of a platform based on Imocap-GIS inertial sensors, with a motion assistance exoskeleton that generates joint movement by means of Maxon motors and Harmonic drive reducers, where a near zero-lag is required for the gait accompaniment to be correct. The Imocap-GIS sensors acquire positional data from the user's lower limbs and send the information through the UDP protocol to the CompactRio system, which constitutes a high-performance controller. These data are processed by the card and subsequently a control signal is sent to the motors that move the exoskeleton joints. Simulations of the proposed controller performance were conducted. The experimental results show that the motion accompaniment exhibits a delay of between 20 and 30 ms, and consequently, it may be stated that the integration between the exoskeleton and the sensors achieves a high efficiency. In this work, the integration between inertial sensors and an exoskeleton prototype has been proposed, where it is evident that the integration met the initial objective. In addition, the integration between the exoskeleton and IMOCAP is among the highest efficiency ranges of similar systems that are currently being developed, and the response lag that was obtained could be improved by means of the incorporation of complementary systems.
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Affiliation(s)
- John Calle-Siguencia
- GIIB Research Department, Universidad Politécnica Salesiana, Cuenca 010102, Ecuador; (J.C.-S.); (S.G.-R.)
| | - Mauro Callejas-Cuervo
- Software Research Group, Engineering Department, Universidad Pedagógica y Tecnológica de Colombia, Tunja 150003, Colombia
| | - Sebastián García-Reino
- GIIB Research Department, Universidad Politécnica Salesiana, Cuenca 010102, Ecuador; (J.C.-S.); (S.G.-R.)
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9
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FSM-HSVM-Based Locomotion Mode Recognition for Exoskeleton Robot. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12115483] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/10/2022]
Abstract
This paper proposes a hierarchical support vector machine recognition algorithm based on a finite state machine (FSM-HSVM) to accurately and reliably recognize the locomotion mode recognition of an exoskeleton robot. As input signals, this method utilizes the angle information of the hip joint and knee joint collected by inertial sensing units (IMUs) on the thighs and shanks of the exoskeleton and the plantar pressure information collected by force sensitive resistors (FSRs) are used as input signals. This method establishes a framework for mode transition by combining the finite state machine (FSM) with the common locomotion modes. The hierarchical support vector machine (HSVM) recognition model is then tightly integrated with the mode transition framework to recognize five typical locomotion modes and eight locomotion mode transitions in real-time. The algorithm not only reduces the abrupt change in the recognition of locomotion mode, but also significantly improves the recognition efficiency. To evaluate recognition performance, separate experiments are conducted on six subjects. According to the results, the average accuracy of all motion modes is 97.106% ± 0.955%, and the average recognition delay rate is only 25.017% ± 6.074%. This method has the benefits of a small calculation amount and high recognition efficiency, and it can be applied extensively in the field of robotics.
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10
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Motion Trajectories Prediction of Lower Limb Exoskeleton Based on Long Short-Term Memory (LSTM) Networks. ACTUATORS 2022. [DOI: 10.3390/act11030073] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
A typical man–machine coupling system could provide the wearer a coordinated and assisted movement by the lower limb exoskeleton. The process of cooperative movement relies on the accurate perception of the wearer’s human movement information and the accurate planning and control of the joint movement of the lower limb exoskeleton. In this paper, a neural network and a Long-Short Term Memory (LSTM) machine learning model method is proposed to predict the actual movement trajectory of the human body’s lower limbs. Then a wearable joint angle measurement device was designed for gait trajectory prediction, which can be used for predictive control through machine learning methods. The experimental results show that the LSTM model can accurately predict the gait trajectory with an average mean square error. This method has practical significance for prediction the trajectory of the lower limb exoskeleton.
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11
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Tiboni M, Borboni A, Vérité F, Bregoli C, Amici C. Sensors and Actuation Technologies in Exoskeletons: A Review. SENSORS (BASEL, SWITZERLAND) 2022; 22:884. [PMID: 35161629 PMCID: PMC8839165 DOI: 10.3390/s22030884] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/31/2021] [Revised: 01/16/2022] [Accepted: 01/19/2022] [Indexed: 02/06/2023]
Abstract
Exoskeletons are robots that closely interact with humans and that are increasingly used for different purposes, such as rehabilitation, assistance in the activities of daily living (ADLs), performance augmentation or as haptic devices. In the last few decades, the research activity on these robots has grown exponentially, and sensors and actuation technologies are two fundamental research themes for their development. In this review, an in-depth study of the works related to exoskeletons and specifically to these two main aspects is carried out. A preliminary phase investigates the temporal distribution of scientific publications to capture the interest in studying and developing novel ideas, methods or solutions for exoskeleton design, actuation and sensors. The distribution of the works is also analyzed with respect to the device purpose, body part to which the device is dedicated, operation mode and design methods. Subsequently, actuation and sensing solutions for the exoskeletons described by the studies in literature are analyzed in detail, highlighting the main trends in their development and spread. The results are presented with a schematic approach, and cross analyses among taxonomies are also proposed to emphasize emerging peculiarities.
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Affiliation(s)
- Monica Tiboni
- Department of Mechanical and Industrial Engineering, University of Brescia, Via Branze, 38, 25123 Brescia, Italy; (M.T.); (C.A.)
| | - Alberto Borboni
- Department of Mechanical and Industrial Engineering, University of Brescia, Via Branze, 38, 25123 Brescia, Italy; (M.T.); (C.A.)
| | - Fabien Vérité
- Agathe Group INSERM U 1150, UMR 7222 CNRS, ISIR (Institute of Intelligent Systems and Robotics), Sorbonne Université, 75005 Paris, France;
| | - Chiara Bregoli
- Institute of Condensed Matter Chemistry and Technologies for Energy (ICMATE), National Research Council (CNR), Via Previati 1/E, 23900 Lecco, Italy;
| | - Cinzia Amici
- Department of Mechanical and Industrial Engineering, University of Brescia, Via Branze, 38, 25123 Brescia, Italy; (M.T.); (C.A.)
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12
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Analyzing Dynamic Operational Conditions of Limb Prosthetic Sockets with a Mechatronics-Twin Framework. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12030986] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
Lower limb prostheses offer a solution to restore the ambulation and self-esteem of amputees. One key component is the prosthetic socket that serves as the interface between prosthetic device and amputee stump and thereby has a wide range of impacts on efficient fitting, appropriate load transmission, operational stability, and control. For the design and optimization of a prosthetic socket, an understanding of the actual intra-socket operational conditions becomes therefore necessary. This is however a difficult task due to the inherent complexity and restricted observability of socket operation. In this study, an innovative mechatronics-twin framework that integrates advanced biomechanical models and simulations with physical prototyping and dynamic operation testing for effective exploration of operational behaviors of prosthetic sockets with amputees is proposed. Within this framework, a specific Stewart manipulator is developed to enable dynamic operation testing, in particular for a well-managed generation of dynamic intra-socket loads and behaviors that are otherwise difficult to observe or realize with the real amputees. A combination of deep learning and Bayesian Inference algorithms is then employed for analyzing the intra-socket load conditions and revealing possible anomalous.
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13
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Zhang Y, Cao G, Ling Z, Li W, Cheng H, He B, Cao S, Zhu A. A Multi-Information Fusion Method for Gait Phase Classification in Lower Limb Rehabilitation Exoskeleton. Front Neurorobot 2021; 15:692539. [PMID: 34795571 PMCID: PMC8594738 DOI: 10.3389/fnbot.2021.692539] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2021] [Accepted: 09/28/2021] [Indexed: 11/30/2022] Open
Abstract
Gait phase classification is important for rehabilitation training in patients with lower extremity motor dysfunction. Classification accuracy of the gait phase also directly affects the effect and rehabilitation training cycle. In this article, a multiple information (multi-information) fusion method for gait phase classification in lower limb rehabilitation exoskeleton is proposed to improve the classification accuracy. The advantage of this method is that a multi-information acquisition system is constructed, and a variety of information directly related to gait movement is synchronously collected. Multi-information includes the surface electromyography (sEMG) signals of the human lower limb during the gait movement, the angle information of the knee joints, and the plantar pressure information. The acquired multi-information is processed and input into a modified convolutional neural network (CNN) model to classify the gait phase. The experiment of gait phase classification with multi-information is carried out under different speed conditions, and the experiment is analyzed to obtain higher accuracy. At the same time, the gait phase classification results of multi-information and single information are compared. The experimental results verify the effectiveness of the multi-information fusion method. In addition, the delay time of each sensor and model classification time is measured, which shows that the system has tremendous real-time performance.
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Affiliation(s)
- Yuepeng Zhang
- Guangdong Key Laboratory of Electromagnetic Control and Intelligent Robots, Shenzhen University, Shenzhen, China
| | - Guangzhong Cao
- Guangdong Key Laboratory of Electromagnetic Control and Intelligent Robots, Shenzhen University, Shenzhen, China
| | - Ziqin Ling
- Guangdong Key Laboratory of Electromagnetic Control and Intelligent Robots, Shenzhen University, Shenzhen, China
| | - WenZhou Li
- Guangdong Key Laboratory of Electromagnetic Control and Intelligent Robots, Shenzhen University, Shenzhen, China
| | - Haoran Cheng
- Guangdong Key Laboratory of Electromagnetic Control and Intelligent Robots, Shenzhen University, Shenzhen, China
| | - Binbin He
- Guangdong Key Laboratory of Electromagnetic Control and Intelligent Robots, Shenzhen University, Shenzhen, China
| | - Shengbin Cao
- Guangdong Key Laboratory of Electromagnetic Control and Intelligent Robots, Shenzhen University, Shenzhen, China
| | - Aibin Zhu
- Institute of Robotics and Intelligent Systems, Xi'an Jiaotong University, Xi'an, China
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14
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Gutierrez F, Razghandi K. MotorSkins-a bio-inspired design approach towards an interactive soft-robotic exosuit. BIOINSPIRATION & BIOMIMETICS 2021; 16:066013. [PMID: 34530414 DOI: 10.1088/1748-3190/ac2785] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/21/2021] [Accepted: 09/16/2021] [Indexed: 06/13/2023]
Abstract
The work presents a bio-inspired design approach to a soft-robotic solution for assisting the knee-bending in users with reduced mobility in lower limbs. Exosuits and fluid-driven actuators are fabric-based devices that are gaining increasing relevance as alternatives assistive technologies that can provide simpler, more flexible solutions in comparison with the rigid exoskeletons. These devices, however, commonly require an external energy supply or a pressurized-fluid reservoir, which considerably constrain the autonomy of such solutions. In this work, we introduce an event-based energy cycle (EBEC) design concept, that can harvest, store, and release the required energy for assisting the knee-bending, in a synchronised interaction with the user and the environment, thus eliminating any need for external energy or control input. Ice-plant hydro-actuation system served as the source of inspiration to address the specific requirements of such interactive exosuit through a fluid-driven material system. Based on the EBEC design concepts and the abstracted bio-inspired principles, a series of (material and process driven) design experimentations helped to address the challenges of realising various functionalities of the harvest, storage, actuation and control instances within a closed hydraulic circuit. Sealing and defining various areas of water-tight seam made out of thermoplastic elastomers provided the base material system to program various chambers, channels, flow-check valves etc of such EBEC system. The resulting fluid-driven EBEC-skin served as a proof of concept for such active exosuit, that brings these functionalities into an integrated 'sense-acting' material system, realising an auto-synchronised energy and information cycles. The proposed design concept can serve as a model for development of similar fluid-driven EBEC soft-machines for further applications. On the more general scheme, the work presents an interdisciplinary design-science approach to bio-inspiration and showcases how biological material solutions can be looked at from a design/designer perspective to bridge the bottom-up and top-down approach to bio-inspiration.
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Affiliation(s)
- Facundo Gutierrez
- MotorSkins, Motionlab, Bouchéstraße 12, Halle20, Berlin, Berlin 12435, Germany
| | - Khashayar Razghandi
- Max Planck Institute of Colloids and Interfaces, Biomaterials Department, Potsdam, Germany
- Matters of Activity, Image Space Material, Cluster of Excellence Humboldt-Universität zu Berlin, Germany
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15
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Wu X, Ma Y, Yong X, Wang C, He Y, Li N. Locomotion Mode Identification and Gait Phase Estimation for Exoskeletons During Continuous Multilocomotion Tasks. IEEE Trans Cogn Dev Syst 2021. [DOI: 10.1109/tcds.2019.2933648] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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16
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Gait Phase Detection Based on Muscle Deformation with Static Standing-Based Calibration. SENSORS 2021; 21:s21041081. [PMID: 33557373 PMCID: PMC7914874 DOI: 10.3390/s21041081] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/05/2020] [Revised: 01/25/2021] [Accepted: 02/01/2021] [Indexed: 01/14/2023]
Abstract
Gait phase detection, which detects foot-contact and foot-off states during walking, is important for various applications, such as synchronous robotic assistance and health monitoring. Gait phase detection systems have been proposed with various wearable devices, sensing inertial, electromyography, or force myography information. In this paper, we present a novel gait phase detection system with static standing-based calibration using muscle deformation information. The gait phase detection algorithm can be calibrated within a short time using muscle deformation data by standing in several postures; it is not necessary to collect data while walking for calibration. A logistic regression algorithm is used as the machine learning algorithm, and the probability output is adjusted based on the angular velocity of the sensor. An experiment is performed with 10 subjects, and the detection accuracy of foot-contact and foot-off states is evaluated using video data for each subject. The median accuracy is approximately 90% during walking based on calibration for 60 s, which shows the feasibility of the static standing-based calibration method using muscle deformation information for foot-contact and foot-off state detection.
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17
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Livolsi C, Conti R, Giovacchini F, Vitiello N, Crea S. A Novel Wavelet-Based Gait Segmentation Method for a Portable hip Exoskeleton. IEEE T ROBOT 2021. [DOI: 10.1109/tro.2021.3122975] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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18
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Miyake T, Kobayashi Y, Fujie MG, Sugano S. Gait event detection based on inter-joint coordination using only angular information. Adv Robot 2020. [DOI: 10.1080/01691864.2020.1803126] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
Affiliation(s)
- Tamon Miyake
- Faculty of Science and Engineering, Waseda University, Tokyo, Japan
| | - Yo Kobayashi
- Graduate school of Engineering Science, Osaka University, Osaka, Japan
| | | | - Shigeki Sugano
- Faculty of Science and Engineering, Waseda University, Tokyo, Japan
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19
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Vu HTT, Dong D, Cao HL, Verstraten T, Lefeber D, Vanderborght B, Geeroms J. A Review of Gait Phase Detection Algorithms for Lower Limb Prostheses. SENSORS (BASEL, SWITZERLAND) 2020; 20:E3972. [PMID: 32708924 PMCID: PMC7411778 DOI: 10.3390/s20143972] [Citation(s) in RCA: 42] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/11/2020] [Revised: 07/08/2020] [Accepted: 07/15/2020] [Indexed: 01/01/2023]
Abstract
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.
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Affiliation(s)
- Huong Thi Thu Vu
- Robotics & Multibody Mechanics Research Group (R & MM), Vrije Universiteit Brussel and Flanders Make, 1050 Brussels, Belgium; (D.D.); (H.-L.C.); (T.V.); (D.L.); (B.V.); (J.G.)
- Faculty of Electronics Engineering Technology, Hanoi University of Industry, Hanoi 100000, Vietnam
| | - Dianbiao Dong
- Robotics & Multibody Mechanics Research Group (R & MM), Vrije Universiteit Brussel and Flanders Make, 1050 Brussels, Belgium; (D.D.); (H.-L.C.); (T.V.); (D.L.); (B.V.); (J.G.)
- School of Mechanical Engineering, Northwestern Polytechnical University, Xi’an 710072, China
| | - Hoang-Long Cao
- Robotics & Multibody Mechanics Research Group (R & MM), Vrije Universiteit Brussel and Flanders Make, 1050 Brussels, Belgium; (D.D.); (H.-L.C.); (T.V.); (D.L.); (B.V.); (J.G.)
- College of Engineering Technology, Can Tho University, Can Tho 90000, Vietnam
| | - Tom Verstraten
- Robotics & Multibody Mechanics Research Group (R & MM), Vrije Universiteit Brussel and Flanders Make, 1050 Brussels, Belgium; (D.D.); (H.-L.C.); (T.V.); (D.L.); (B.V.); (J.G.)
| | - Dirk Lefeber
- Robotics & Multibody Mechanics Research Group (R & MM), Vrije Universiteit Brussel and Flanders Make, 1050 Brussels, Belgium; (D.D.); (H.-L.C.); (T.V.); (D.L.); (B.V.); (J.G.)
| | - Bram Vanderborght
- Robotics & Multibody Mechanics Research Group (R & MM), Vrije Universiteit Brussel and Flanders Make, 1050 Brussels, Belgium; (D.D.); (H.-L.C.); (T.V.); (D.L.); (B.V.); (J.G.)
| | - Joost Geeroms
- Robotics & Multibody Mechanics Research Group (R & MM), Vrije Universiteit Brussel and Flanders Make, 1050 Brussels, Belgium; (D.D.); (H.-L.C.); (T.V.); (D.L.); (B.V.); (J.G.)
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20
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Pasinetti S, Nuzzi C, Covre N, Luchetti A, Maule L, Serpelloni M, Lancini M. Validation of Marker-Less System for the Assessment of Upper Joints Reaction Forces in Exoskeleton Users. SENSORS (BASEL, SWITZERLAND) 2020; 20:E3899. [PMID: 32668739 PMCID: PMC7412171 DOI: 10.3390/s20143899] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/16/2020] [Revised: 07/03/2020] [Accepted: 07/09/2020] [Indexed: 11/16/2022]
Abstract
This paper presents the validation of a marker-less motion capture system used to evaluate the upper limb stress of subjects using exoskeletons for locomotion. The system fuses the human skeletonization provided by commercial 3D cameras with forces exchanged by the user to the ground through upper limbs utilizing instrumented crutches. The aim is to provide a low cost, accurate, and reliable technology useful to provide the trainer a quantitative evaluation of the impact of assisted gait on the subject without the need to use an instrumented gait lab. The reaction forces at the upper limbs' joints are measured to provide a validation focused on clinically relevant quantities for this application. The system was used simultaneously with a reference motion capture system inside a clinical gait analysis lab. An expert user performed 20 walking tests using instrumented crutches and force platforms inside the observed volume. The mechanical model was applied to data from the system and the reference motion capture, and numerical simulations were performed to assess the internal joint reaction of the subject's upper limbs. A comparison between the two results shows a root mean square error of less than 2% of the subject's body weight.
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Affiliation(s)
- Simone Pasinetti
- Department of Mechanical and Industrial Engineering (DIMI), University of Brescia, 25123 Brescia, Italy; (S.P.); (M.L.)
| | - Cristina Nuzzi
- Department of Mechanical and Industrial Engineering (DIMI), University of Brescia, 25123 Brescia, Italy; (S.P.); (M.L.)
| | - Nicola Covre
- Department of Industrial Engineering (DII), University of Trento, 38123 Trento, Italy; (N.C.); (A.L.); (L.M.)
| | - Alessandro Luchetti
- Department of Industrial Engineering (DII), University of Trento, 38123 Trento, Italy; (N.C.); (A.L.); (L.M.)
| | - Luca Maule
- Department of Industrial Engineering (DII), University of Trento, 38123 Trento, Italy; (N.C.); (A.L.); (L.M.)
| | - Mauro Serpelloni
- Department of Information Engineering (DII), University of Brescia, 25123 Brescia, Italy;
| | - Matteo Lancini
- Department of Mechanical and Industrial Engineering (DIMI), University of Brescia, 25123 Brescia, Italy; (S.P.); (M.L.)
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21
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Effects of Different Feature Parameters of sEMG on Human Motion Pattern Recognition Using Multilayer Perceptrons and LSTM Neural Networks. APPLIED SCIENCES-BASEL 2020. [DOI: 10.3390/app10103358] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
In response to the need for an exoskeleton to quickly identify the wearer’s movement mode in the mixed control mode, this paper studies the impact of different feature parameters of the surface electromyography (sEMG) signal on the accuracy of human motion pattern recognition using multilayer perceptrons and long short-term memory (LSTM) neural networks. The sEMG signals are extracted from the seven common human motion patterns in daily life, and the time domain and frequency domain features are extracted to build a feature parameter dataset for training the classifier. Recognition of human lower extremity movement patterns based on multilayer perceptrons and the LSTM neural network were carried out, and the final recognition accuracy rates of different feature parameters and different classifier model parameters were compared in the process of establishing the dataset. The experimental results show that the best accuracy rate of human motion pattern recognition using multilayer perceptrons is 95.53%, and the best accuracy rate of human motion pattern recognition using the LSTM neural network is 96.57%.
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22
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Recognition of Gait Phases with a Single Knee Electrogoniometer: A Deep Learning Approach. ELECTRONICS 2020. [DOI: 10.3390/electronics9020355] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Artificial neural networks were satisfactorily implemented for assessing gait events from different walking data. This study aims to propose a novel approach for recognizing gait phases and events, based on deep-learning analysis of only sagittal knee-joint angle measured by a single electrogoniometer per leg. Promising classification/prediction performances have been previously achieved by surface-EMG studies; thus, a further aim is to test if adding electrogoniometer data could improve classification performances of state-of-the-art methods. Gait data are measured in about 10,000 strides from 23 healthy adults, during ground walking. A multi-layer perceptron model is implemented, composed of three hidden layers and a one-dimensional output. Classification/prediction accuracy is tested vs. ground truth represented by foot–floor-contact signals, through samples acquired from subjects not seen during training phase. Average classification-accuracy of 90.6 ± 2.9% and mean absolute value (MAE) of 29.4 ± 13.7 and 99.5 ± 28.9 ms in assessing heel-strike and toe-off timing are achieved in unseen subjects. Improvement of classification-accuracy (four points) and reduction of MAE (at least 35%) are achieved when knee-angle data are used to enhance sEMG-data prediction. Comparison of the two approaches shows as the reduction of set-up complexity implies a worsening of mainly toe-off prediction. Thus, the present electrogoniometer approach is particularly suitable for the classification tasks where only heel-strike event is involved, such as stride recognition, stride-time computation, and identification of toe walking.
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23
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Ma Y, Wu X, Wang C, Yi Z, Liang G. Gait Phase Classification and Assist Torque Prediction for a Lower Limb Exoskeleton System Using Kernel Recursive Least-Squares Method. SENSORS (BASEL, SWITZERLAND) 2019; 19:s19245449. [PMID: 31835626 PMCID: PMC6961050 DOI: 10.3390/s19245449] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/18/2019] [Revised: 11/23/2019] [Accepted: 11/26/2019] [Indexed: 06/10/2023]
Abstract
The gait phase classification method is a key technique to control an exoskeleton robot. Different people have different gait features while wearing an exoskeleton robot due to the gap between the exoskeleton and the wearer and their operation habits, such as the correspondence between the joint angle and the moment at which the foot contacts the ground, the amplitude of the joint angle and others. In order to enhance the performance of the gait phase classification in an exoskeleton robot using only the angle of hip and knee joints, a kernel recursive least-squares (KRLS) algorithm is introduced to build a gait phase classification model. We also build an assist torque predictor based on the KRLS algorithm in this work considering the adaptation of unique gait features. In this paper, we evaluate the classification performance of the KRLS model by comparing with two other commonly used gait recognition methods-the multi-layer perceptron neural network (MLPNN) method and the support vector machine (SVM) algorithm. In this experiment, the training and testing datasets for the models built by KRLS, MLPNN and SVM were collected from 10 healthy volunteers. The gait data are collected from the exoskeleton robot that we designed rather than collected from the human body. These data depict the human-robot coupling gait that includes unique gait features. The KRLS classification results are in average 3% higher than MLPNN and SVM. The testing average accuracy of KRLS is about 86%. The prediction results of KRLS are twice as good as MLPNN in assist torque prediction experiments. The KRLS performs in a good, stable, and robust way and shows model generalization abilities.
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Affiliation(s)
- Yue Ma
- Guangdong Provincial Key Laboratory of Robotics and Intelligent System, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China; (Y.M.); (X.W.); (Z.Y.); (G.L.)
- Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Beijing 100049, China
- CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institutes of Advanced Technology, Shenzhen 518055, China
| | - Xinyu Wu
- Guangdong Provincial Key Laboratory of Robotics and Intelligent System, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China; (Y.M.); (X.W.); (Z.Y.); (G.L.)
- CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institutes of Advanced Technology, Shenzhen 518055, China
- SIAT Branch, Shenzhen Institute of Artificial Intelligence and Robotics for Society, Shenzhen 518055, China
| | - Can Wang
- Guangdong Provincial Key Laboratory of Robotics and Intelligent System, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China; (Y.M.); (X.W.); (Z.Y.); (G.L.)
- CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institutes of Advanced Technology, Shenzhen 518055, China
- SIAT Branch, Shenzhen Institute of Artificial Intelligence and Robotics for Society, Shenzhen 518055, China
| | - Zhengkun Yi
- Guangdong Provincial Key Laboratory of Robotics and Intelligent System, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China; (Y.M.); (X.W.); (Z.Y.); (G.L.)
- CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institutes of Advanced Technology, Shenzhen 518055, China
| | - Guoyuan Liang
- Guangdong Provincial Key Laboratory of Robotics and Intelligent System, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China; (Y.M.); (X.W.); (Z.Y.); (G.L.)
- CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institutes of Advanced Technology, Shenzhen 518055, China
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24
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Yong X, Jing X, Wu X, Jiang Y, Yokoi H. Design and Implementation of Arch Function for Adaptive Multi-Finger Prosthetic Hand. SENSORS 2019; 19:s19163539. [PMID: 31412642 PMCID: PMC6720182 DOI: 10.3390/s19163539] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/05/2019] [Revised: 08/06/2019] [Accepted: 08/08/2019] [Indexed: 11/30/2022]
Abstract
Although arch motions of the palm substantially contribute to frequent hand grasping, they are usually neglected in the development of prosthetic hands which focuses on digit movements. We designed the arch function for its implementation on an adaptive multi-finger prosthetic hand. The digits from the developed hand can perform adaptive grasping, and two carpometacarpal joints enable the palm of the prosthetic hand to form an arch with the thumb. Moreover, the arch posture can be passively released, mimicking the human hand switching between sphere and medium wrap grasps according to the situation. Other requirements such as weight, cost, and size limitations for hand prostheses were also considered. As a result, we only used three actuators fully embedded in the palm through a novel tendon-driven transmission. Although the prosthetic hand is almost the same size of an adult hand, it weighs only 146 g and can perform 70% of the 10 most frequent grasps.
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Affiliation(s)
- Xu Yong
- Guangdong Provincial Key Laboratory of Robotics and Intelligent System, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences (CAS), Shenzhen 518055, China
- CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences (CAS), Shenzhen 518055, China
- Department of Mechanical Engineering and Intelligent Systems, The University of Electro-Communications, 1-5-1 Chofugaoka, Chofu, Tokyo 1828585, Japan
| | - Xiaobei Jing
- CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences (CAS), Shenzhen 518055, China
- Department of Mechanical Engineering and Intelligent Systems, The University of Electro-Communications, 1-5-1 Chofugaoka, Chofu, Tokyo 1828585, Japan
| | - Xinyu Wu
- Guangdong Provincial Key Laboratory of Robotics and Intelligent System, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences (CAS), Shenzhen 518055, China.
- CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences (CAS), Shenzhen 518055, China.
- SIAT Branch, Shenzhen Institute of Artificial Intelligence and Robotics for Society, Shenzhen 518055, China.
| | - Yinlai Jiang
- Department of Mechanical Engineering and Intelligent Systems, The University of Electro-Communications, 1-5-1 Chofugaoka, Chofu, Tokyo 1828585, Japan.
- Center for Neuroscience and Biomedical Engineering, The University of Electro-communications, Tokyo 1828585, Japan.
- Beijing Innovation Center for Intelligent Robots and Systems, Beijing 100081, China.
| | - Hiroshi Yokoi
- Department of Mechanical Engineering and Intelligent Systems, The University of Electro-Communications, 1-5-1 Chofugaoka, Chofu, Tokyo 1828585, Japan
- Center for Neuroscience and Biomedical Engineering, The University of Electro-communications, Tokyo 1828585, Japan
- Beijing Innovation Center for Intelligent Robots and Systems, Beijing 100081, China
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25
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Grimmer M, Schmidt K, Duarte JE, Neuner L, Koginov G, Riener R. Stance and Swing Detection Based on the Angular Velocity of Lower Limb Segments During Walking. Front Neurorobot 2019; 13:57. [PMID: 31396072 PMCID: PMC6667673 DOI: 10.3389/fnbot.2019.00057] [Citation(s) in RCA: 35] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2019] [Accepted: 07/10/2019] [Indexed: 12/21/2022] Open
Abstract
Lower limb exoskeletons require the correct support magnitude and timing to achieve user assistance. This study evaluated whether the sign of the angular velocity of lower limb segments can be used to determine the timing of the stance and the swing phase during walking. We assumed that stance phase is characterized by a positive, swing phase by a negative angular velocity. Thus, the transitions can be used to also identify heel-strike and toe-off. Thirteen subjects without gait impairments walked on a treadmill at speeds between 0.5 and 2.1 m/s on level ground and inclinations between −10 and +10°. Kinematic and kinetic data was measured simultaneously from an optical motion capture system, force plates, and five inertial measurement units (IMUs). These recordings were used to compute the angular velocities of four lower limb segments: two biological (thigh, shank) and two virtual that were geometrical projections of the biological segments (virtual leg, virtual extended leg). We analyzed the reliability (two sign changes of the angular velocity per stride) and the accuracy (offset in timing between sign change and ground reaction force based timing) of the virtual and biological segments for detecting the gait phases stance and swing. The motion capture data revealed that virtual limb segments seem superior to the biological limb segments in the reliability of stance and swing detection. However, increased signal noise when using the IMUs required additional rule sets for reliable stance and swing detection. With IMUs, the biological shank segment had the least variability in accuracy. The IMU-based heel-strike events of the shank and both virtual segment were slightly early (3.3–4.8% of the gait cycle) compared to the ground reaction force-based timing. Toe-off event timing showed more variability (9.0% too early to 7.3% too late) between the segments and changed with walking speed. The results show that the detection of the heel-strike, and thus stance phase, based on IMU angular velocity is possible for different segments when additional rule sets are included. Further work is required to improve the timing accuracy for the toe-off detection (swing).
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Affiliation(s)
- Martin Grimmer
- Lauflabor Locomotion Laboratory, Department of Human Sciences, Institute of Sports Science, Technische Universität Darmstadt, Darmstadt, Germany
| | - Kai Schmidt
- Sensory-Motor Systems (SMS) Lab, Department of Health Sciences and Technology, Institute of Robotics and Intelligent Systems (IRIS), ETH Zurich, Zurich, Switzerland.,Spinal Cord Injury Center, University Hospital Balgrist, Zurich, Switzerland
| | - Jaime E Duarte
- Sensory-Motor Systems (SMS) Lab, Department of Health Sciences and Technology, Institute of Robotics and Intelligent Systems (IRIS), ETH Zurich, Zurich, Switzerland.,Spinal Cord Injury Center, University Hospital Balgrist, Zurich, Switzerland
| | - Lukas Neuner
- Sensory-Motor Systems (SMS) Lab, Department of Health Sciences and Technology, Institute of Robotics and Intelligent Systems (IRIS), ETH Zurich, Zurich, Switzerland
| | - Gleb Koginov
- Sensory-Motor Systems (SMS) Lab, Department of Health Sciences and Technology, Institute of Robotics and Intelligent Systems (IRIS), ETH Zurich, Zurich, Switzerland
| | - Robert Riener
- Sensory-Motor Systems (SMS) Lab, Department of Health Sciences and Technology, Institute of Robotics and Intelligent Systems (IRIS), ETH Zurich, Zurich, Switzerland.,Spinal Cord Injury Center, University Hospital Balgrist, Zurich, Switzerland
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26
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Zaroug A, Proud JK, Lai DTH, Mudie K, Billing D, Begg R. Overview of Computational Intelligence (CI) Techniques for Powered Exoskeletons. COMPUTATIONAL INTELLIGENCE IN SENSOR NETWORKS 2019. [DOI: 10.1007/978-3-662-57277-1_15] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
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27
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Anwary AR, Yu H, Vassallo M. Gait Evaluation Using Procrustes and Euclidean Distance Matrix Analysis. IEEE J Biomed Health Inform 2018; 23:2021-2029. [PMID: 30418928 DOI: 10.1109/jbhi.2018.2875812] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Objective assessment of gait is important in the treatment and rehabilitation of patients with different diseases. In this paper, we propose a gait evaluation system using the Procrustes and Euclidean distance matrix analysis. We design and develop an android app to collect real time synchronous accelerometer and gyroscope data from two inertial measurement unit sensors through Bluetooth connectivity. The data is collected from 12 young (ten for modeling and two for validation) and 20 older subjects. We analyze the data collected from real world for stride, step, stance, and swing gait features. We validate our method with the measurements of gait features. The generalized Procrustes analysis is used to estimate a standard normal mean gait shape (NMGS) for ten young subjects. Each gait feature of both young and older subjects is then converted to find the best match with the NMGS using the ordinary Procrustes analysis. The shape distance between the NMGS and each gait shape is estimated using Riemannian shape distance, Riemannian size-and-shape distance, Procrustes size-and-shape distance, and root-mean-square deviation. A t-test is performed to provide statistical evidence of gait shape differences between young and older gaits. A mean form, which is considered as a standard normal mean gait form (NMGF), and inter-feature distances are estimated from the set of ten young subjects. The form difference is estimated between the NMGF and individual gaits of young and older. The degree of abnormality is then estimated for individual features and the result is plotted to visualize the feature in a gait. Experimental results demonstrate the performance of the proposed method.
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28
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Vu HTT, Gomez F, Cherelle P, Lefeber D, Nowé A, Vanderborght B. ED-FNN: A New Deep Learning Algorithm to Detect Percentage of the Gait Cycle for Powered Prostheses. SENSORS (BASEL, SWITZERLAND) 2018; 18:E2389. [PMID: 30041421 PMCID: PMC6068484 DOI: 10.3390/s18072389] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/11/2018] [Revised: 07/19/2018] [Accepted: 07/20/2018] [Indexed: 11/16/2022]
Abstract
Throughout the last decade, a whole new generation of powered transtibial prostheses and exoskeletons has been developed. However, these technologies are limited by a gait phase detection which controls the wearable device as a function of the activities of the wearer. Consequently, gait phase detection is considered to be of great importance, as achieving high detection accuracy will produce a more precise, stable, and safe rehabilitation device. In this paper, we propose a novel gait percent detection algorithm that can predict a full gait cycle discretised within a 1% interval. We called this algorithm an exponentially delayed fully connected neural network (ED-FNN). A dataset was obtained from seven healthy subjects that performed daily walking activities on the flat ground and a 15-degree slope. The signals were taken from only one inertial measurement unit (IMU) attached to the lower shank. The dataset was divided into training and validation datasets for every subject, and the mean square error (MSE) error between the model prediction and the real percentage of the gait was computed. An average MSE of 0.00522 was obtained for every subject in both training and validation sets, and an average MSE of 0.006 for the training set and 0.0116 for the validation set was obtained when combining all subjects' signals together. Although our experiments were conducted in an offline setting, due to the forecasting capabilities of the ED-FNN, our system provides an opportunity to eliminate detection delays for real-time applications.
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Affiliation(s)
- Huong Thi Thu Vu
- Robotics & MultiBody Mechanics Research Group (R& MM) and Artificial Intelligence Lab, Vrije Universiteit Brussel and Flanders Make; Pleinlaan 2, 1050 Brussel, Belgium.
| | - Felipe Gomez
- Robotics & MultiBody Mechanics Research Group (R& MM) and Artificial Intelligence Lab, Vrije Universiteit Brussel and Flanders Make; Pleinlaan 2, 1050 Brussel, Belgium.
| | - Pierre Cherelle
- Robotics & MultiBody Mechanics Research Group (R& MM) and Artificial Intelligence Lab, Vrije Universiteit Brussel and Flanders Make; Pleinlaan 2, 1050 Brussel, Belgium.
| | - Dirk Lefeber
- Robotics & MultiBody Mechanics Research Group (R& MM) and Artificial Intelligence Lab, Vrije Universiteit Brussel and Flanders Make; Pleinlaan 2, 1050 Brussel, Belgium.
| | - Ann Nowé
- Robotics & MultiBody Mechanics Research Group (R& MM) and Artificial Intelligence Lab, Vrije Universiteit Brussel and Flanders Make; Pleinlaan 2, 1050 Brussel, Belgium.
| | - Bram Vanderborght
- Robotics & MultiBody Mechanics Research Group (R& MM) and Artificial Intelligence Lab, Vrije Universiteit Brussel and Flanders Make; Pleinlaan 2, 1050 Brussel, Belgium.
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An Automatic Gait Feature Extraction Method for Identifying Gait Asymmetry Using Wearable Sensors. SENSORS 2018; 18:s18020676. [PMID: 29495299 PMCID: PMC5855014 DOI: 10.3390/s18020676] [Citation(s) in RCA: 38] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/06/2018] [Revised: 02/08/2018] [Accepted: 02/20/2018] [Indexed: 11/17/2022]
Abstract
This paper aims to assess the use of Inertial Measurement Unit (IMU) sensors to identify gait asymmetry by extracting automatic gait features. We design and develop an android app to collect real time synchronous IMU data from legs. The results from our method are validated using a Qualisys Motion Capture System. The data are collected from 10 young and 10 older subjects. Each performed a trial in a straight corridor comprising 15 strides of normal walking, a turn around and another 15 strides. We analyse the data for total distance, total time, total velocity, stride, step, cadence, step ratio, stance, and swing. The accuracy of detecting the stride number using the proposed method is 100% for young and 92.67% for older subjects. The accuracy of estimating travelled distance using the proposed method for young subjects is 97.73% and 98.82% for right and left legs; and for the older, is 88.71% and 89.88% for right and left legs. The average travelled distance is 37.77 (95% CI ± 3.57) meters for young subjects and is 22.50 (95% CI ± 2.34) meters for older subjects. The average travelled time for young subjects is 51.85 (95% CI ± 3.08) seconds and for older subjects is 84.02 (95% CI ± 9.98) seconds. The results show that wearable sensors can be used for identifying gait asymmetry without the requirement and expense of an elaborate laboratory setup. This can serve as a tool in diagnosing gait abnormalities in individuals and opens the possibilities for home based self-gait asymmetry assessment.
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Alvarez MT, Torricelli D, Del-Ama AJ, Pinto D, Gonzalez-Vargas J, Moreno JC, Gil-Agudo A, Pons JL. Simultaneous estimation of human and exoskeleton motion: A simplified protocol. IEEE Int Conf Rehabil Robot 2017; 2017:1431-1436. [PMID: 28814021 DOI: 10.1109/icorr.2017.8009449] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Adequate benchmarking procedures in the area of wearable robots is gaining importance in order to compare different devices on a quantitative basis, improve them and support the standardization and regulation procedures. Performance assessment usually focuses on the execution of locomotion tasks, and is mostly based on kinematic-related measures. Typical drawbacks of marker-based motion capture systems, gold standard for measure of human limb motion, become challenging when measuring limb kinematics, due to the concomitant presence of the robot. This work answers the question of how to reliably assess the subject's body motion by placing markers over the exoskeleton. Focusing on the ankle joint, the proposed methodology showed that it is possible to reconstruct the trajectory of the subject's joint by placing markers on the exoskeleton, although foot flexibility during walking can impact the reconstruction accuracy. More experiments are needed to confirm this hypothesis, and more subjects and walking conditions are needed to better characterize the errors of the proposed methodology, although our results are promising, indicating small errors.
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Cross View Gait Recognition Using Joint-Direct Linear Discriminant Analysis. SENSORS 2016; 17:s17010006. [PMID: 28025484 PMCID: PMC5298579 DOI: 10.3390/s17010006] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/02/2016] [Revised: 12/20/2016] [Accepted: 12/20/2016] [Indexed: 11/17/2022]
Abstract
This paper proposes a view-invariant gait recognition framework that employs a unique view invariant model that profits from the dimensionality reduction provided by Direct Linear Discriminant Analysis (DLDA). The framework, which employs gait energy images (GEIs), creates a single joint model that accurately classifies GEIs captured at different angles. Moreover, the proposed framework also helps to reduce the under-sampling problem (USP) that usually appears when the number of training samples is much smaller than the dimension of the feature space. Evaluation experiments compare the proposed framework's computational complexity and recognition accuracy against those of other view-invariant methods. Results show improvements in both computational complexity and recognition accuracy.
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