Exoskeleton devices may impose kinematic constraints on a user’s motion and affect their stability due to added mass and inertia, but also due to the simplified mechanical design. This study explores the impact of kinematic constraints imposed by exoskeletons on user gait, stability, and perceived discomfort. Specifically, it examines how the varying degrees of freedom (DoF) in an ankle exoskeleton influence these factors. The exoskeleton utilized in this study can be configured to allow one, two, or three DoF, thereby simulating different levels of mechanical complexity and kinematic compatibility. A pilot study was conducted with six participants walking on a straight path to evaluate these effects. The findings indicate that increasing DoF of the exoskeleton improves several criteria, including kinematics and stability. In particular, the transition from 1 DoF to 2 DoF yielded a larger improvement than the transition from 2 DoF to 3 DoF, although the 3 DoF configuration produced the best overall results. Higher DoF configurations also resulted in stability values that resemble more closely those of walking without the exoskeleton, despite the added weight. Subjective feedback from participants corroborated these results, indicating the lowest discomfort with the 3 DoF ankle exoskeleton.
{"title":"Influence of Motion Restrictions in an Ankle Exoskeleton on Gait Kinematics and Stability in Straight Walking","authors":"Miha Dežman;Charlotte Marquardt;Adnan Üğür;Tobias Moeller;Tamim Asfour","doi":"10.1109/TMRB.2024.3503896","DOIUrl":"https://doi.org/10.1109/TMRB.2024.3503896","url":null,"abstract":"Exoskeleton devices may impose kinematic constraints on a user’s motion and affect their stability due to added mass and inertia, but also due to the simplified mechanical design. This study explores the impact of kinematic constraints imposed by exoskeletons on user gait, stability, and perceived discomfort. Specifically, it examines how the varying degrees of freedom (DoF) in an ankle exoskeleton influence these factors. The exoskeleton utilized in this study can be configured to allow one, two, or three DoF, thereby simulating different levels of mechanical complexity and kinematic compatibility. A pilot study was conducted with six participants walking on a straight path to evaluate these effects. The findings indicate that increasing DoF of the exoskeleton improves several criteria, including kinematics and stability. In particular, the transition from 1 DoF to 2 DoF yielded a larger improvement than the transition from 2 DoF to 3 DoF, although the 3 DoF configuration produced the best overall results. Higher DoF configurations also resulted in stability values that resemble more closely those of walking without the exoskeleton, despite the added weight. Subjective feedback from participants corroborated these results, indicating the lowest discomfort with the 3 DoF ankle exoskeleton.","PeriodicalId":73318,"journal":{"name":"IEEE transactions on medical robotics and bionics","volume":"7 1","pages":"114-122"},"PeriodicalIF":3.4,"publicationDate":"2024-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143529879","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-21DOI: 10.1109/TMRB.2024.3503913
Eisa Anwar;Sajeeva Abeywardena;Stuart C. Miller;Ildar Farkhatdinov
In manufacturing, construction, logistics, and other industrial tasks, human workers are required to handle and manipulate heavy loads such as loading packages in and out of warehouses, manipulating physical components on assembly lines, and more. However, repetitive manipulation of heavy loads can disrupt balance and lead to strains and injuries on the body, causing issues such as back pain. This concern is particularly significant in jobs involving awkward postures, e.g., aircraft and vehicle assembly and maintenance. To help address this challenge, we present a comprehensive scoping review examining robots capable of supporting physical balance in healthy individuals. Our analysis involved evaluating their capabilities, observing their functionality, and assessing their practicality. The majority of our findings (81%) were lower body exoskeletons, which, though highly mobile, can be constrained by slow control systems. Conversely, supernumerary robotic limbs and wearable gyroscopes allow unrestricted movement with less constrained control systems. Many experiments lack baseline comparisons without the robot, and some have limited participant recruitment, affecting representation. We recommend universally available testing procedures to effectively demonstrate and compare the capabilities of balance-supporting robots.
{"title":"How Robots Can Support Balancing in Healthy People","authors":"Eisa Anwar;Sajeeva Abeywardena;Stuart C. Miller;Ildar Farkhatdinov","doi":"10.1109/TMRB.2024.3503913","DOIUrl":"https://doi.org/10.1109/TMRB.2024.3503913","url":null,"abstract":"In manufacturing, construction, logistics, and other industrial tasks, human workers are required to handle and manipulate heavy loads such as loading packages in and out of warehouses, manipulating physical components on assembly lines, and more. However, repetitive manipulation of heavy loads can disrupt balance and lead to strains and injuries on the body, causing issues such as back pain. This concern is particularly significant in jobs involving awkward postures, e.g., aircraft and vehicle assembly and maintenance. To help address this challenge, we present a comprehensive scoping review examining robots capable of supporting physical balance in healthy individuals. Our analysis involved evaluating their capabilities, observing their functionality, and assessing their practicality. The majority of our findings (81%) were lower body exoskeletons, which, though highly mobile, can be constrained by slow control systems. Conversely, supernumerary robotic limbs and wearable gyroscopes allow unrestricted movement with less constrained control systems. Many experiments lack baseline comparisons without the robot, and some have limited participant recruitment, affecting representation. We recommend universally available testing procedures to effectively demonstrate and compare the capabilities of balance-supporting robots.","PeriodicalId":73318,"journal":{"name":"IEEE transactions on medical robotics and bionics","volume":"7 1","pages":"213-229"},"PeriodicalIF":3.4,"publicationDate":"2024-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143521533","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-21DOI: 10.1109/TMRB.2024.3504002
Prashant K. Jamwal;Shyngys Dauletbayev;Daulet Sagidoldin;Darkhan Keikibayev;Aibek Niyetkaliyev;Shahid Hussain;Sunil K. Agrawal
Robotic exoskeletons are being increasingly used in clinics for the treatment of medicable disabilities. These exoskeletons, which closely couple with patients’ limbs, need to move in harmony with the endoskeleton motions. To achieve coordination, exoskeletons should be transparent; in other words, they should not interfere with natural human motion or their underlying coordination strategies. Transparency can be achieved through a bio-inspired exoskeleton design and also by implementing appropriate force control methods to maneuver exoskeleton motions. A new hybrid active-passive Gait Exoskeleton-Assisted Rehabilitation (GEAR) robot is presented here for the rehabilitation of lower limb disabilities. The GEAR robot is designed to enhance transparency incorporating a flexible hip joint and a biomimetic knee joint. The proposed GEAR robot also integrates a Remote Centered Motion (RCM) based passive mechanism to support torso and pelvic motions in two planes and features actuated exoskeleton legs in the sagittal plane for treadmill-assisted walking. The exoskeleton legs are actuated at their hip and knee joints using backdrivable actuators. To provide a natural walking experience, the hip joints of the exoskeleton legs offer two passive degrees of freedom in the frontal and transverse planes in addition to the actuated sagittal plane motion. The biomimetic design of the exoskeleton knee joint ensures alignment with the human anatomical knee joint by closely tracking the latter’s instantaneous center of rotation (ICR). To evaluate GEAR robot’s transparency, a comparative study was conducted, involving three healthy subjects. The participants walked freely on a treadmill and then with the GEAR robot operated first in a completely backdrivable (i.e., passive) mode and subsequently in an active mode. The sEMG data collected during these experiments were analyzed to assess robot’s transparency.
{"title":"Design and Transparency Assessment of a Gait Rehabilitation Robot With Biomimetic Knee Joints","authors":"Prashant K. Jamwal;Shyngys Dauletbayev;Daulet Sagidoldin;Darkhan Keikibayev;Aibek Niyetkaliyev;Shahid Hussain;Sunil K. Agrawal","doi":"10.1109/TMRB.2024.3504002","DOIUrl":"https://doi.org/10.1109/TMRB.2024.3504002","url":null,"abstract":"Robotic exoskeletons are being increasingly used in clinics for the treatment of medicable disabilities. These exoskeletons, which closely couple with patients’ limbs, need to move in harmony with the endoskeleton motions. To achieve coordination, exoskeletons should be transparent; in other words, they should not interfere with natural human motion or their underlying coordination strategies. Transparency can be achieved through a bio-inspired exoskeleton design and also by implementing appropriate force control methods to maneuver exoskeleton motions. A new hybrid active-passive Gait Exoskeleton-Assisted Rehabilitation (GEAR) robot is presented here for the rehabilitation of lower limb disabilities. The GEAR robot is designed to enhance transparency incorporating a flexible hip joint and a biomimetic knee joint. The proposed GEAR robot also integrates a Remote Centered Motion (RCM) based passive mechanism to support torso and pelvic motions in two planes and features actuated exoskeleton legs in the sagittal plane for treadmill-assisted walking. The exoskeleton legs are actuated at their hip and knee joints using backdrivable actuators. To provide a natural walking experience, the hip joints of the exoskeleton legs offer two passive degrees of freedom in the frontal and transverse planes in addition to the actuated sagittal plane motion. The biomimetic design of the exoskeleton knee joint ensures alignment with the human anatomical knee joint by closely tracking the latter’s instantaneous center of rotation (ICR). To evaluate GEAR robot’s transparency, a comparative study was conducted, involving three healthy subjects. The participants walked freely on a treadmill and then with the GEAR robot operated first in a completely backdrivable (i.e., passive) mode and subsequently in an active mode. The sEMG data collected during these experiments were analyzed to assess robot’s transparency.","PeriodicalId":73318,"journal":{"name":"IEEE transactions on medical robotics and bionics","volume":"7 1","pages":"290-302"},"PeriodicalIF":3.4,"publicationDate":"2024-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143521347","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-21DOI: 10.1109/TMRB.2024.3503920
Lizhi Pan;Qiyang Li;Jianmin Li
Decoding human movements using electromyography (EMG) signals is important for the development of EMG-based human-machine interfaces (HMIs). This study proposed a novel muscle synergy-based musculoskeletal model (MM) for prediction of hand and wrist movements, including wrist flexion/extension, wrist adduction/abduction, wrist pronation/supination, and metacarpophalangeal (MCP) flexion/extension. Ten limb-intact subjects were recruited for the offline experiment, and 15-channel EMG signals from the subject’s forearm were recorded. Using the non-negative matrix factorization (NMF) algorithm, four pairs of excitation signals were extracted from the multi-channel EMG signals. Then the MM driven by the extracted muscle excitations was adopted to predict hand and wrist movements. The proposed method was compared with the NMF algorithm and artificial neural network (ANN), and the prediction performance of the three was evaluated with Pearson’s correlation coefficient (r) and normalized root mean square error (NRMSE). The total average r of the proposed MM was 0.8475 across all subjects and all movement types, approximately 0.123 higher than NMF algorithm and 0.106 higher than ANN. In addition, the total average NRMSE of the proposed MM was 0.16125 across all subjects and all movement types, approximately 0.074 lower than NMF algorithm and 0.037 lower than ANN. In brief, the proposed MM showed significantly improved prediction accuracy over the NMF algorithm and ANN. This study provides a promising approach for the control of robotic arms and prostheses in EMG-based HMIs.
{"title":"Musculoskeletal Modeling Based on Muscle Synergy for Prediction of Hand and Wrist Movements","authors":"Lizhi Pan;Qiyang Li;Jianmin Li","doi":"10.1109/TMRB.2024.3503920","DOIUrl":"https://doi.org/10.1109/TMRB.2024.3503920","url":null,"abstract":"Decoding human movements using electromyography (EMG) signals is important for the development of EMG-based human-machine interfaces (HMIs). This study proposed a novel muscle synergy-based musculoskeletal model (MM) for prediction of hand and wrist movements, including wrist flexion/extension, wrist adduction/abduction, wrist pronation/supination, and metacarpophalangeal (MCP) flexion/extension. Ten limb-intact subjects were recruited for the offline experiment, and 15-channel EMG signals from the subject’s forearm were recorded. Using the non-negative matrix factorization (NMF) algorithm, four pairs of excitation signals were extracted from the multi-channel EMG signals. Then the MM driven by the extracted muscle excitations was adopted to predict hand and wrist movements. The proposed method was compared with the NMF algorithm and artificial neural network (ANN), and the prediction performance of the three was evaluated with Pearson’s correlation coefficient (r) and normalized root mean square error (NRMSE). The total average r of the proposed MM was 0.8475 across all subjects and all movement types, approximately 0.123 higher than NMF algorithm and 0.106 higher than ANN. In addition, the total average NRMSE of the proposed MM was 0.16125 across all subjects and all movement types, approximately 0.074 lower than NMF algorithm and 0.037 lower than ANN. In brief, the proposed MM showed significantly improved prediction accuracy over the NMF algorithm and ANN. This study provides a promising approach for the control of robotic arms and prostheses in EMG-based HMIs.","PeriodicalId":73318,"journal":{"name":"IEEE transactions on medical robotics and bionics","volume":"7 1","pages":"337-346"},"PeriodicalIF":3.4,"publicationDate":"2024-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143521430","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-21DOI: 10.1109/TMRB.2024.3503894
Daniel S. Esser;Margaret F. Rox;Robert P. Naftel;D. Caleb Rucker;Eric J. Barth;Alan Kuntz;Robert J. Webster
Prior models of continuously flexible robots typically assume uniform stiffness, and in this paper we relax this assumption. Geometrically varying stiffness profiles provide additional design freedom to influence the motions and workspaces of continuum robots. These results are timely, because with recent rapid advancements in multimaterial additive manufacturing techniques, it is now straightforward to create more complex stiffness profiles in robots. The key insight of this paper is to project forces and moments applied to the robot onto its center of stiffness (i.e., the Young’s modulus-weighted center of each cross section). We show how the center of stiffness can be thought of as analogous to a “precurved backbone” in a robot with uniform stiffness. This analogy enables a large body of prior work in Cosserat Rod modeling of such robots to be applied directly to those with stiffness variations. We experimentally validate this approach using multimaterial, soft, tendon-actuated robots. Lastly, to illustrate how these results can be used in practice, we investigate how stiffness variation can improve performance in a neurosurgical task.
{"title":"Encoding Desired Deformation Profiles in Endoscope-Like Soft Robots","authors":"Daniel S. Esser;Margaret F. Rox;Robert P. Naftel;D. Caleb Rucker;Eric J. Barth;Alan Kuntz;Robert J. Webster","doi":"10.1109/TMRB.2024.3503894","DOIUrl":"https://doi.org/10.1109/TMRB.2024.3503894","url":null,"abstract":"Prior models of continuously flexible robots typically assume uniform stiffness, and in this paper we relax this assumption. Geometrically varying stiffness profiles provide additional design freedom to influence the motions and workspaces of continuum robots. These results are timely, because with recent rapid advancements in multimaterial additive manufacturing techniques, it is now straightforward to create more complex stiffness profiles in robots. The key insight of this paper is to project forces and moments applied to the robot onto its center of stiffness (i.e., the Young’s modulus-weighted center of each cross section). We show how the center of stiffness can be thought of as analogous to a “precurved backbone” in a robot with uniform stiffness. This analogy enables a large body of prior work in Cosserat Rod modeling of such robots to be applied directly to those with stiffness variations. We experimentally validate this approach using multimaterial, soft, tendon-actuated robots. Lastly, to illustrate how these results can be used in practice, we investigate how stiffness variation can improve performance in a neurosurgical task.","PeriodicalId":73318,"journal":{"name":"IEEE transactions on medical robotics and bionics","volume":"7 1","pages":"392-403"},"PeriodicalIF":3.4,"publicationDate":"2024-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10759847","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143521426","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-21DOI: 10.1109/TMRB.2024.3503893
Kai Pruyn;Rosemarie Murray;Lukas Gabert;Tommaso Lenzi
Hemiparetic gait is often characterized by ankle weakness, resulting in decreased propulsion and clearance, as well as knee hyperextension. These gait deviations reduce speed and efficiency while increasing the risk of falls and osteoarthritis. Powered ankle exoskeletons have the potential to address these issues. However, only a handful of studies have investigated their effects on hemiparetic gait. The results are often inconsistent, and the biomechanical analysis rarely includes the knee or hip joint or a direct clearance measure. In this case study, we assess the ankle, knee, and hip biomechanics with and without a new autonomous powered ankle exoskeleton across different speeds and inclines. Exoskeleton assistance resulted in more normative kinematics at the subject’s self-selected walking speed. The paretic ankle angle at heel strike increased from 10° plantarflexed without the exoskeleton to 0.5° dorsiflexed with the exoskeleton, and the peak plantarflexion angle during swing decreased from 28° without the exoskeleton to 12° with the exoskeleton. Furthermore, stance knee flexion increased from 7° without the exoskeleton to 20° with the exoskeleton. Finally, foot clearance increased with the exoskeleton for all conditions between 3.1 cm and 5.4 cm. This case study highlights new mechanisms for powered ankle exoskeletons to improve hemiparetic gait.
{"title":"Autonomous Powered Ankle Exoskeleton Improves Foot Clearance and Knee Hyperextension After Stroke: A Case Study","authors":"Kai Pruyn;Rosemarie Murray;Lukas Gabert;Tommaso Lenzi","doi":"10.1109/TMRB.2024.3503893","DOIUrl":"https://doi.org/10.1109/TMRB.2024.3503893","url":null,"abstract":"Hemiparetic gait is often characterized by ankle weakness, resulting in decreased propulsion and clearance, as well as knee hyperextension. These gait deviations reduce speed and efficiency while increasing the risk of falls and osteoarthritis. Powered ankle exoskeletons have the potential to address these issues. However, only a handful of studies have investigated their effects on hemiparetic gait. The results are often inconsistent, and the biomechanical analysis rarely includes the knee or hip joint or a direct clearance measure. In this case study, we assess the ankle, knee, and hip biomechanics with and without a new autonomous powered ankle exoskeleton across different speeds and inclines. Exoskeleton assistance resulted in more normative kinematics at the subject’s self-selected walking speed. The paretic ankle angle at heel strike increased from 10° plantarflexed without the exoskeleton to 0.5° dorsiflexed with the exoskeleton, and the peak plantarflexion angle during swing decreased from 28° without the exoskeleton to 12° with the exoskeleton. Furthermore, stance knee flexion increased from 7° without the exoskeleton to 20° with the exoskeleton. Finally, foot clearance increased with the exoskeleton for all conditions between 3.1 cm and 5.4 cm. This case study highlights new mechanisms for powered ankle exoskeletons to improve hemiparetic gait.","PeriodicalId":73318,"journal":{"name":"IEEE transactions on medical robotics and bionics","volume":"7 1","pages":"51-58"},"PeriodicalIF":3.4,"publicationDate":"2024-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10759770","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143529891","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-21DOI: 10.1109/TMRB.2024.3503900
Jan Willem A. Rook;Massimo Sartori;Mohamed Irfan Refai
Electromyography (EMG)-driven musculoskeletal models (EMS) of the trunk are used for estimating lumbosacral joint moments and compressive loads during lifting tasks. These models provide personalized estimates of the parameters using information from many sensors. However, to advance technology from labs to workplaces, there is a need for sensor reduction to improve wearability and applicability. Therefore we introduce an EMG sensor reduction approach based on inverse synergy extrapolation, to reconstruct unmeasured EMG signals for different box-lifting techniques. 12 participants performed an array of tasks (squat, stoop, unilateral twist and bilateral twist) with different weights (0 kg, 7.5 kg and 15 kg). We found that two synergies were sufficient to explain the different lifting tasks (median variance accounted for of 0.91). Building upon this, we used two sensors at optimal subject-specific muscle locations to reconstruct the EMG of four unmeasured channels. Evaluation of the reconstructed and reference EMG showed median coefficients of determination $(R^{2})$ between 0.70 and 0.86, with median root mean squared errors (RMSE) ranging from 0.02 to 0.04 relative to maximal voluntary contraction. This indicates that our proposed method shows promise for sensor reduction for driving a trunk EMS for ambulatory biomechanical risk assessment in occupational settings and exoskeleton control.
{"title":"Toward Wearable Electromyography for Personalized Musculoskeletal Trunk Models Using an Inverse Synergy-Based Approach","authors":"Jan Willem A. Rook;Massimo Sartori;Mohamed Irfan Refai","doi":"10.1109/TMRB.2024.3503900","DOIUrl":"https://doi.org/10.1109/TMRB.2024.3503900","url":null,"abstract":"Electromyography (EMG)-driven musculoskeletal models (EMS) of the trunk are used for estimating lumbosacral joint moments and compressive loads during lifting tasks. These models provide personalized estimates of the parameters using information from many sensors. However, to advance technology from labs to workplaces, there is a need for sensor reduction to improve wearability and applicability. Therefore we introduce an EMG sensor reduction approach based on inverse synergy extrapolation, to reconstruct unmeasured EMG signals for different box-lifting techniques. 12 participants performed an array of tasks (squat, stoop, unilateral twist and bilateral twist) with different weights (0 kg, 7.5 kg and 15 kg). We found that two synergies were sufficient to explain the different lifting tasks (median variance accounted for of 0.91). Building upon this, we used two sensors at optimal subject-specific muscle locations to reconstruct the EMG of four unmeasured channels. Evaluation of the reconstructed and reference EMG showed median coefficients of determination <inline-formula> <tex-math>$(R^{2})$ </tex-math></inline-formula> between 0.70 and 0.86, with median root mean squared errors (RMSE) ranging from 0.02 to 0.04 relative to maximal voluntary contraction. This indicates that our proposed method shows promise for sensor reduction for driving a trunk EMS for ambulatory biomechanical risk assessment in occupational settings and exoskeleton control.","PeriodicalId":73318,"journal":{"name":"IEEE transactions on medical robotics and bionics","volume":"7 1","pages":"13-19"},"PeriodicalIF":3.4,"publicationDate":"2024-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10759783","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143529894","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper proposes an underactuated hand exoskeleton designed to assist in recovering lost grasp function. Structurally, the design incorporates a multi-link coupling mechanism driven by a single motor equipped with a series elastic actuator (SEA). The SEA enables bidirectional compliant drive and fore feedback without the need for a force sensor. The connecting rod is optimized to facilitate the natural flexion and extension of the fingers. For control, an admittance control strategy based on real-time fusion of electromyography (EMG) and electroencephalogram (EEG) signals is proposed. EMG signals are used to estimate muscle strength and control the movement of the exoskeleton. EEG signals reflect the active intention of the subjects, and admittance control adjusts the rehabilitation strategy in real-time. For the first time, the degree of concentration is used as a parameter for subject adjustment of rehabilitation training. Finally, experiments on stiffness calibration, muscle force estimation, and admittance control based on EEG-EMG fusion were conducted. The results indicate that the normalized root-mean-square-error (NRMSE) of stiffness calibration is 8.32%. The average inconsistence of concentration and joint torque (ICJT) is 73.18%. The experimental results indicate that the proposed method can enhance the subjective participation of the subjects in the rehabilitation process, thereby improving the overall rehabilitation outcomes.
{"title":"Design and EMG-EEG Fusion-Based Admittance Control of a Hand Exoskeleton With Series Elastic Actuators","authors":"Haitao Zou;Qingcong Wu;Luo Yang;Yanghui Zhu;Hongtao Wu","doi":"10.1109/TMRB.2024.3503899","DOIUrl":"https://doi.org/10.1109/TMRB.2024.3503899","url":null,"abstract":"This paper proposes an underactuated hand exoskeleton designed to assist in recovering lost grasp function. Structurally, the design incorporates a multi-link coupling mechanism driven by a single motor equipped with a series elastic actuator (SEA). The SEA enables bidirectional compliant drive and fore feedback without the need for a force sensor. The connecting rod is optimized to facilitate the natural flexion and extension of the fingers. For control, an admittance control strategy based on real-time fusion of electromyography (EMG) and electroencephalogram (EEG) signals is proposed. EMG signals are used to estimate muscle strength and control the movement of the exoskeleton. EEG signals reflect the active intention of the subjects, and admittance control adjusts the rehabilitation strategy in real-time. For the first time, the degree of concentration is used as a parameter for subject adjustment of rehabilitation training. Finally, experiments on stiffness calibration, muscle force estimation, and admittance control based on EEG-EMG fusion were conducted. The results indicate that the normalized root-mean-square-error (NRMSE) of stiffness calibration is 8.32%. The average inconsistence of concentration and joint torque (ICJT) is 73.18%. The experimental results indicate that the proposed method can enhance the subjective participation of the subjects in the rehabilitation process, thereby improving the overall rehabilitation outcomes.","PeriodicalId":73318,"journal":{"name":"IEEE transactions on medical robotics and bionics","volume":"7 1","pages":"347-358"},"PeriodicalIF":3.4,"publicationDate":"2024-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143521350","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-21DOI: 10.1109/TMRB.2024.3503998
Tess B. Meier;Christopher J. Nycz;Andrew Daudelin;Gregory S. Fischer
Upper motor neuron injuries such as traumatic brain injury and stroke can cause hemiparesis and subsequent hand impairment. Repeated hand movements in physical therapy are shown to maintain flexibility and potentially facilitate regaining functionality. To further understand the impact of hand exoskeletons on motor impairment and recovery, we aim to study brain activation during rehabilitation and assistive hand exoskeleton use. Functional magnetic resonance imaging (fMRI) can be used to measure brain activation with high spatial resolution, but devices used within a magnetic resonance imaging (MRI) machine must be designed within several constraints. We present the design of a pneumatic hand orthosis with powered extension—the PneuHOPE Hand—a wearable MRI conditional research platform to enable the studying of brain activation in the presence of hand spasticity. To demonstrate its use as an MRI compatible platform, we conducted MRI conditionality testing. Additionally, we collected brain activation data from two healthy control subject’s using fMRI to show that the exoskeleton can be comfortably worn in the MRI scanner and that appropriate brain activation data can be collected during use. The results indicate the PneuHOPE Hand platform can be safely used for neuroimaging studies in the MRI with < 12% reduction in SNR for T1 images, < 32% reduction for T2, and no visible paramagnetic artifacts.
上运动神经元损伤（如创伤性脑损伤和中风）可导致偏瘫，进而造成手部功能障碍。物理治疗中反复的手部运动可保持灵活性，并有可能促进功能的恢复。为了进一步了解手部外骨骼对运动障碍和恢复的影响，我们旨在研究康复和辅助手部外骨骼使用过程中的大脑激活情况。功能磁共振成像（fMRI）可用于测量高空间分辨率的大脑激活，但在磁共振成像（MRI）机中使用的设备必须在若干限制条件下设计。我们介绍了一种具有动力伸展功能的气动手部矫形器--PneuHOPE 手--的设计，它是一种可穿戴的磁共振成像条件研究平台，可用于研究手部痉挛时的大脑激活情况。为了证明其作为磁共振成像兼容平台的用途，我们进行了磁共振成像条件性测试。此外，我们还使用 fMRI 收集了两名健康对照组受试者的脑激活数据，以证明外骨骼可以舒适地穿戴在核磁共振扫描仪中，并且可以在使用过程中收集适当的脑激活数据。结果表明，PneuHOPE Hand 平台可以安全地用于核磁共振成像中的神经成像研究，T1 图像的信噪比降低小于 12%，T2 图像的信噪比降低小于 32%，并且没有明显的顺磁性伪影。
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Pub Date : 2024-11-21DOI: 10.1109/TMRB.2024.3503922
Clément Lhoste;Emek Barış Küçüktabak;Lorenzo Vianello;Lorenzo Amato;Matthew R. Short;Kevin M. Lynch;Jose L. Pons
In the control of lower-limb exoskeletons with feet, the phase in the gait cycle can be identified by monitoring the weight distribution at the feet. This phase information can be used in the exoskeleton’s controller to compensate the dynamics of the exoskeleton and to assign impedance parameters. Typically the weight distribution is calculated using data from sensors such as treadmill force plates or insole force sensors. However, these solutions increase both the setup complexity and cost. For this reason, we propose a deep-learning approach that uses a short time window of joint kinematics to predict the weight distribution of an exoskeleton in real time. The model was trained on treadmill walking data from six users wearing a four-degree-of-freedom exoskeleton and tested in real time on three different users wearing the same device. This test set includes two users not present in the training set to demonstrate the model’s ability to generalize across individuals. Results show that the proposed method is able to fit the actual weight distribution with $R^{2}=0.9$ and is suitable for real-time control with prediction times less than 1 ms. Experiments in closed-loop exoskeleton control show that deep-learning-based weight distribution estimation can be used to replace force sensors in overground and treadmill walking.
{"title":"Deep-Learning Estimation of Weight Distribution Using Joint Kinematics for Lower-Limb Exoskeleton Control","authors":"Clément Lhoste;Emek Barış Küçüktabak;Lorenzo Vianello;Lorenzo Amato;Matthew R. Short;Kevin M. Lynch;Jose L. Pons","doi":"10.1109/TMRB.2024.3503922","DOIUrl":"https://doi.org/10.1109/TMRB.2024.3503922","url":null,"abstract":"In the control of lower-limb exoskeletons with feet, the phase in the gait cycle can be identified by monitoring the weight distribution at the feet. This phase information can be used in the exoskeleton’s controller to compensate the dynamics of the exoskeleton and to assign impedance parameters. Typically the weight distribution is calculated using data from sensors such as treadmill force plates or insole force sensors. However, these solutions increase both the setup complexity and cost. For this reason, we propose a deep-learning approach that uses a short time window of joint kinematics to predict the weight distribution of an exoskeleton in real time. The model was trained on treadmill walking data from six users wearing a four-degree-of-freedom exoskeleton and tested in real time on three different users wearing the same device. This test set includes two users not present in the training set to demonstrate the model’s ability to generalize across individuals. Results show that the proposed method is able to fit the actual weight distribution with <inline-formula> <tex-math>$R^{2}=0.9$ </tex-math></inline-formula> and is suitable for real-time control with prediction times less than 1 ms. Experiments in closed-loop exoskeleton control show that deep-learning-based weight distribution estimation can be used to replace force sensors in overground and treadmill walking.","PeriodicalId":73318,"journal":{"name":"IEEE transactions on medical robotics and bionics","volume":"7 1","pages":"20-26"},"PeriodicalIF":3.4,"publicationDate":"2024-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143529892","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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