Pub Date : 2019-06-01DOI: 10.1109/ICORR.2019.8779485
S. Kager, Asif Hussain, Adele Cherpin, A. Melendez-Calderon, Atsushi Takagi, S. Endo, E. Burdet, S. Hirche, M. Ang, D. Campolo
Dyadic interaction between humans has gained great research interest in the last years. The effects of factors that influence the interaction, as e.g. roles or skill level matching, are still not well understood. In this paper, we further investigated the effect of skill level matching between partners on learning of a visuo-motor task. Understanding the effect of skill level matching is crucial for applications in collaborative rehabilitation. Fifteen healthy participants were asked to trace a path while being subjected to a visuo-motor rotation (Novice). The Novices were paired with a partner, forming one of the three Dyad Types: a) haptic connection to another Novice, b) haptic connection to an Expert (no visuo-motor rotation), or c) no haptic. The intervention consisted of a Familiarization phase, followed by a Training phase, in which the Novices were learning the task in the respective Dyad Type, and a Test phase in which the learning was assessed (haptic connection removed, if any). Results suggest that learning of the task with a haptic connection to an Expert was least beneficial. However, during the Training phase the dyads comprising an Expert clearly outperformed the dyads with matched skill levels. The results point towards the same direction as previous findings in literature and can be explained by current motor-learning theories. Future work needs to corroborate these preliminary results.
{"title":"The effect of skill level matching in dyadic interaction on learning of a tracing task","authors":"S. Kager, Asif Hussain, Adele Cherpin, A. Melendez-Calderon, Atsushi Takagi, S. Endo, E. Burdet, S. Hirche, M. Ang, D. Campolo","doi":"10.1109/ICORR.2019.8779485","DOIUrl":"https://doi.org/10.1109/ICORR.2019.8779485","url":null,"abstract":"Dyadic interaction between humans has gained great research interest in the last years. The effects of factors that influence the interaction, as e.g. roles or skill level matching, are still not well understood. In this paper, we further investigated the effect of skill level matching between partners on learning of a visuo-motor task. Understanding the effect of skill level matching is crucial for applications in collaborative rehabilitation. Fifteen healthy participants were asked to trace a path while being subjected to a visuo-motor rotation (Novice). The Novices were paired with a partner, forming one of the three Dyad Types: a) haptic connection to another Novice, b) haptic connection to an Expert (no visuo-motor rotation), or c) no haptic. The intervention consisted of a Familiarization phase, followed by a Training phase, in which the Novices were learning the task in the respective Dyad Type, and a Test phase in which the learning was assessed (haptic connection removed, if any). Results suggest that learning of the task with a haptic connection to an Expert was least beneficial. However, during the Training phase the dyads comprising an Expert clearly outperformed the dyads with matched skill levels. The results point towards the same direction as previous findings in literature and can be explained by current motor-learning theories. Future work needs to corroborate these preliminary results.","PeriodicalId":130415,"journal":{"name":"2019 IEEE 16th International Conference on Rehabilitation Robotics (ICORR)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128549621","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 : 2019-06-01DOI: 10.1109/ICORR.2019.8779493
Fabio Rizzoglio, F. Sciandra, Elisa Galofaro, L. Losio, E. Quinland, Clara Leoncini, A. Massone, F. Mussa-Ivaldi, M. Casadio
Myoelectric Computer Interfaces (MCIs) are a viable option to promote the recovery of movements following spinal cord injury (SCI), stroke, or other neurological disorders that impair motor functions. We developed and tested a MCI interface with the goal of reducing abnormal muscular activations due to compensatory strategies or undesired co-contraction after SCI. The interface mapped surface electromyographic signals (sEMG) into the movement of a cursor on a computer monitor. First, we aimed to reduce the co-activation of muscles pairs: the activation of two muscles controlled orthogonal directions of the cursor movements. Furthermore, to decrease the undesired concurrent activation of a third muscle, we modulated the visual feedback related to the position of the cursor on the screen based on the activation of this muscle. We tested the interface with six unimpaired and two SCI participants. Participants were able to decrease the activity of the targeted muscle when it was associated with the visual feedback of the cursor, but, interestingly, after training, its activity increased again. As for the SCI participants, one successfully decreased the co-activation of arm muscles, while the other successfully improved the selective activation of leg muscles. This is a first proof of concept that people with SCI can acquire, through the proposed MCI, a greater awareness of their muscular activity, reducing abnormal muscle simultaneous activations.
{"title":"A Myoelectric Computer Interface for Reducing Abnormal Muscle Activations after Spinal Cord Injury","authors":"Fabio Rizzoglio, F. Sciandra, Elisa Galofaro, L. Losio, E. Quinland, Clara Leoncini, A. Massone, F. Mussa-Ivaldi, M. Casadio","doi":"10.1109/ICORR.2019.8779493","DOIUrl":"https://doi.org/10.1109/ICORR.2019.8779493","url":null,"abstract":"Myoelectric Computer Interfaces (MCIs) are a viable option to promote the recovery of movements following spinal cord injury (SCI), stroke, or other neurological disorders that impair motor functions. We developed and tested a MCI interface with the goal of reducing abnormal muscular activations due to compensatory strategies or undesired co-contraction after SCI. The interface mapped surface electromyographic signals (sEMG) into the movement of a cursor on a computer monitor. First, we aimed to reduce the co-activation of muscles pairs: the activation of two muscles controlled orthogonal directions of the cursor movements. Furthermore, to decrease the undesired concurrent activation of a third muscle, we modulated the visual feedback related to the position of the cursor on the screen based on the activation of this muscle. We tested the interface with six unimpaired and two SCI participants. Participants were able to decrease the activity of the targeted muscle when it was associated with the visual feedback of the cursor, but, interestingly, after training, its activity increased again. As for the SCI participants, one successfully decreased the co-activation of arm muscles, while the other successfully improved the selective activation of leg muscles. This is a first proof of concept that people with SCI can acquire, through the proposed MCI, a greater awareness of their muscular activity, reducing abnormal muscle simultaneous activations.","PeriodicalId":130415,"journal":{"name":"2019 IEEE 16th International Conference on Rehabilitation Robotics (ICORR)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133610184","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 : 2019-06-01DOI: 10.1109/ICORR.2019.8779403
Emiliano Quiñones Yumbla, Ruby Afriyie Obeng, J. Ward, T. Sugar, P. Artemiadis
Locomotion is paramount in enabling human beings to effectively respond in space and time to meet different needs. There are 2 million Americans living with an amputation and the majority of those amputations are of the lower limbs. Although current powered prostheses can accommodate walking, and in some cases running, basic functions like hiking or walking on various non-rigid or dynamic terrains are requirements that have yet to be met. This paper focuses on the mechanisms involved during human locomotion, while transitioning from rigid to compliant surfaces such as from pavement to sand, grass or granular media. Utilizing a unique tool, the Variable Stiffness Treadmill (VST), as the platform for human locomotion, rigid to compliant surface transitions are simulated. The analysis of muscular activation during the transition from rigid to compliant surfaces reveals specific anticipatory muscle activation that precedes stepping on the compliant surface. These results are novel and important since the evoked activation changes can be used for altering the powered prosthesis control parameters to adapt to the new surface, and therefore result in significantly increased robustness for smart powered lower limb prostheses.
{"title":"Anticipatory muscle responses in transitions from rigid to compliant surfaces: towards smart ankle-foot prostheses","authors":"Emiliano Quiñones Yumbla, Ruby Afriyie Obeng, J. Ward, T. Sugar, P. Artemiadis","doi":"10.1109/ICORR.2019.8779403","DOIUrl":"https://doi.org/10.1109/ICORR.2019.8779403","url":null,"abstract":"Locomotion is paramount in enabling human beings to effectively respond in space and time to meet different needs. There are 2 million Americans living with an amputation and the majority of those amputations are of the lower limbs. Although current powered prostheses can accommodate walking, and in some cases running, basic functions like hiking or walking on various non-rigid or dynamic terrains are requirements that have yet to be met. This paper focuses on the mechanisms involved during human locomotion, while transitioning from rigid to compliant surfaces such as from pavement to sand, grass or granular media. Utilizing a unique tool, the Variable Stiffness Treadmill (VST), as the platform for human locomotion, rigid to compliant surface transitions are simulated. The analysis of muscular activation during the transition from rigid to compliant surfaces reveals specific anticipatory muscle activation that precedes stepping on the compliant surface. These results are novel and important since the evoked activation changes can be used for altering the powered prosthesis control parameters to adapt to the new surface, and therefore result in significantly increased robustness for smart powered lower limb prostheses.","PeriodicalId":130415,"journal":{"name":"2019 IEEE 16th International Conference on Rehabilitation Robotics (ICORR)","volume":"1 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133674022","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 : 2019-06-01DOI: 10.1109/ICORR.2019.8779383
Kazuya Morito, Yuri Hasegawa, K. Kubota, T. Tsuji
Motor learning issues for hemiplegics not only include motor impairments such as spastic paralysis, but reportedly also an inability to appropriately recognize somatic sensations. In this regard, biofeedback of movement information through visual information and auditory information has been found effective as a method for drawing attention to appropriate somatic sensations. In this context, here, we propose a novel eccentric training system utilizing visual biofeedback of force information. We first develop a compact and highly portable rehabilitation robot for home use. The robot estimates the force on the tiptoe without the use of a force sensor, and a display connected to the robot presents the force information to the trainee. Clinical trials with two chronic hemiplegics have been conducted. The results show that the timed up and go tests of both trainees are shortened after training twice a week for three weeks (six times in total). Simultaneously, the co-contraction index scores of the tibialis anterior and gastrocnemius muscles decrease. These findings in conjunction with previous results suggest that training with visual biofeedback of force information may enhance reciprocal inhibition of the tibialis anterior muscle and reduces co-contraction.
{"title":"Visual Biofeedback of Force Information for Eccentric Training of Hemiplegic Patients","authors":"Kazuya Morito, Yuri Hasegawa, K. Kubota, T. Tsuji","doi":"10.1109/ICORR.2019.8779383","DOIUrl":"https://doi.org/10.1109/ICORR.2019.8779383","url":null,"abstract":"Motor learning issues for hemiplegics not only include motor impairments such as spastic paralysis, but reportedly also an inability to appropriately recognize somatic sensations. In this regard, biofeedback of movement information through visual information and auditory information has been found effective as a method for drawing attention to appropriate somatic sensations. In this context, here, we propose a novel eccentric training system utilizing visual biofeedback of force information. We first develop a compact and highly portable rehabilitation robot for home use. The robot estimates the force on the tiptoe without the use of a force sensor, and a display connected to the robot presents the force information to the trainee. Clinical trials with two chronic hemiplegics have been conducted. The results show that the timed up and go tests of both trainees are shortened after training twice a week for three weeks (six times in total). Simultaneously, the co-contraction index scores of the tibialis anterior and gastrocnemius muscles decrease. These findings in conjunction with previous results suggest that training with visual biofeedback of force information may enhance reciprocal inhibition of the tibialis anterior muscle and reduces co-contraction.","PeriodicalId":130415,"journal":{"name":"2019 IEEE 16th International Conference on Rehabilitation Robotics (ICORR)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125989619","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 : 2019-06-01DOI: 10.1109/ICORR.2019.8779513
Ji Chen, D. Damiano, Z. Lerner, T. Bulea
Advanced control strategies that can adjust assistance based volitional effort from the user may be beneficial for deploying exoskeletons for overground gait training in ambulatory populations, such as children with cerebral palsy (CP). In this study, we evaluate the ability to predict biological knee moment during stance phase of walking with an exoskeleton in two children subjects with crouch gait from CP. The predictive model characterized the knee as a rotational spring with the addition of correction factors at knee extensor moment extrema to predict the instantaneous knee moment profile from the knee angle. Our model prediction performance was comparable to previous studies for weight acceptance (WA) and mid-stance (MS) phases in both assisted (Assist) and non-assisted (Zero) modes based on normalized root mean square error (RMSE), demonstrating the feasibility of joint moment estimation during exoskeleton walking. RMSE was highest in late stance phase, likely due to the non-linear knee stiffness exhibited during this phase in one participant. Overall, our results support real-time implementation of the joint moment prediction model for control of exoskeleton knee extension assistance in children with CP
{"title":"Validating Model-Based Prediction Of Biological Knee Moment During Walking With An Exoskeleton in Crouch Gait: Potential Application for Exoskeleton Control","authors":"Ji Chen, D. Damiano, Z. Lerner, T. Bulea","doi":"10.1109/ICORR.2019.8779513","DOIUrl":"https://doi.org/10.1109/ICORR.2019.8779513","url":null,"abstract":"Advanced control strategies that can adjust assistance based volitional effort from the user may be beneficial for deploying exoskeletons for overground gait training in ambulatory populations, such as children with cerebral palsy (CP). In this study, we evaluate the ability to predict biological knee moment during stance phase of walking with an exoskeleton in two children subjects with crouch gait from CP. The predictive model characterized the knee as a rotational spring with the addition of correction factors at knee extensor moment extrema to predict the instantaneous knee moment profile from the knee angle. Our model prediction performance was comparable to previous studies for weight acceptance (WA) and mid-stance (MS) phases in both assisted (Assist) and non-assisted (Zero) modes based on normalized root mean square error (RMSE), demonstrating the feasibility of joint moment estimation during exoskeleton walking. RMSE was highest in late stance phase, likely due to the non-linear knee stiffness exhibited during this phase in one participant. Overall, our results support real-time implementation of the joint moment prediction model for control of exoskeleton knee extension assistance in children with CP","PeriodicalId":130415,"journal":{"name":"2019 IEEE 16th International Conference on Rehabilitation Robotics (ICORR)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124202469","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 : 2019-06-01DOI: 10.1109/ICORR.2019.8779540
Brock Laschowski, William J. McNally, A. Wong, J. McPhee
Drawing inspiration from autonomous vehicles, using future environment information could improve the control of wearable biomechatronic devices for assisting human locomotion. To the authors knowledge, this research represents the first documented investigation using machine vision and deep convolutional neural networks for environment recognition to support the predictive control of robotic lower-limb prostheses and exoskeletons. One participant was instrumented with a battery-powered, chest-mounted RGB camera system. Approximately 10 hours of video footage were experimentally collected while ambulating throughout unknown outdoor and indoor environments. The sampled images were preprocessed and individually labelled. A deep convolutional neural network was developed and trained to automatically recognize three walking environments: level-ground, incline staircases, and decline staircases. The environment recognition system achieved 94.85% overall image classification accuracy. Extending these preliminary findings, future research should incorporate other environment classes (e.g., incline ramps) and integrate the environment recognition system with electromechanical sensors and/or surface electromyography for automated locomotion mode recognition. The challenges associated with implementing deep learning on wearable biomechatronic devices are discussed.
{"title":"Preliminary Design of an Environment Recognition System for Controlling Robotic Lower-Limb Prostheses and Exoskeletons","authors":"Brock Laschowski, William J. McNally, A. Wong, J. McPhee","doi":"10.1109/ICORR.2019.8779540","DOIUrl":"https://doi.org/10.1109/ICORR.2019.8779540","url":null,"abstract":"Drawing inspiration from autonomous vehicles, using future environment information could improve the control of wearable biomechatronic devices for assisting human locomotion. To the authors knowledge, this research represents the first documented investigation using machine vision and deep convolutional neural networks for environment recognition to support the predictive control of robotic lower-limb prostheses and exoskeletons. One participant was instrumented with a battery-powered, chest-mounted RGB camera system. Approximately 10 hours of video footage were experimentally collected while ambulating throughout unknown outdoor and indoor environments. The sampled images were preprocessed and individually labelled. A deep convolutional neural network was developed and trained to automatically recognize three walking environments: level-ground, incline staircases, and decline staircases. The environment recognition system achieved 94.85% overall image classification accuracy. Extending these preliminary findings, future research should incorporate other environment classes (e.g., incline ramps) and integrate the environment recognition system with electromechanical sensors and/or surface electromyography for automated locomotion mode recognition. The challenges associated with implementing deep learning on wearable biomechatronic devices are discussed.","PeriodicalId":130415,"journal":{"name":"2019 IEEE 16th International Conference on Rehabilitation Robotics (ICORR)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127169406","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 : 2019-06-01DOI: 10.1109/ICORR.2019.8779556
Wakako Sato, Yoshiki Tsuchida, Pengcheng Li, T. Hasegawa, Yoji Yamada, Y. Uchiyama
Progression of technology has expanded applications of smart walkers in clinical fields. However, it is essential to investigate the effects of different types of gait guidance in order to introduce smart walkers more widely throughout these fields. The purpose of this study was to identify the effects of assistive and resistive guidance on the gait of elderly people using a smart walker. Gait parameters, surface electromyography of lower limb muscles, and trunk acceleration were measured. The assistive guidance force significantly increased gait speed, step length, and cadence while increasing trunk acceleration variability. The same amount of resistive guidance force did not change gait parameters; instead, however, it restrained the speed-dependent increase of trunk acceleration variability in the mediolateral direction. An analysis of muscle activity suggested that the lower limb muscle activity could be increased by varying gait parameters including speed, step length, and cadence.
{"title":"Identifying the Effects of Assistive and Resistive Guidance on the Gait of Elderly People Using a Smart Walker","authors":"Wakako Sato, Yoshiki Tsuchida, Pengcheng Li, T. Hasegawa, Yoji Yamada, Y. Uchiyama","doi":"10.1109/ICORR.2019.8779556","DOIUrl":"https://doi.org/10.1109/ICORR.2019.8779556","url":null,"abstract":"Progression of technology has expanded applications of smart walkers in clinical fields. However, it is essential to investigate the effects of different types of gait guidance in order to introduce smart walkers more widely throughout these fields. The purpose of this study was to identify the effects of assistive and resistive guidance on the gait of elderly people using a smart walker. Gait parameters, surface electromyography of lower limb muscles, and trunk acceleration were measured. The assistive guidance force significantly increased gait speed, step length, and cadence while increasing trunk acceleration variability. The same amount of resistive guidance force did not change gait parameters; instead, however, it restrained the speed-dependent increase of trunk acceleration variability in the mediolateral direction. An analysis of muscle activity suggested that the lower limb muscle activity could be increased by varying gait parameters including speed, step length, and cadence.","PeriodicalId":130415,"journal":{"name":"2019 IEEE 16th International Conference on Rehabilitation Robotics (ICORR)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130455462","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 : 2019-06-01DOI: 10.1109/ICORR.2019.8779437
Curt A. Laubscher, J. Sawicki
The nominal gait of each individual is unique and varies with the walking speed of the person. This poses a difficult problem for powered rehabilitative orthoses since control strategies often require a reference trajectory and give little control to the patient. This paper describes a simple control approach which applies torque resistive to joint movement that is unnatural for healthy individuals in the hip and knee joints during the swing phase of gait. The controller uses a configuration-dependent orthonormal basis to represent vectors in terms of components which are tangent and normal to healthy gait patterns for a continuum of gait speeds. The controller damps motion in the normal direction, thereby resisting movement which is unnatural for healthy individuals. With this control law, subjects are not restricted to a particular reference trajectory and have a large degree of volition over spatiotemporal gait parameters (e.g., stride length, swing time, and cadence). Experiments are conducted to check the feasibility of the control law in a provisional powered pediatric lower-limb orthosis. The gait guidance controller is used in conjunction with a human controller representing an individual with gait impairment. The main results compare gait shape quality when the gait guidance controller is enabled versus disabled, and show how the gait guidance controller is able to reshape gait to more closely resemble that of a healthy individual for various cadences.
{"title":"Gait Guidance Control for Damping of Unnatural Motion in a Powered Pediatric Lower-Limb Orthosis","authors":"Curt A. Laubscher, J. Sawicki","doi":"10.1109/ICORR.2019.8779437","DOIUrl":"https://doi.org/10.1109/ICORR.2019.8779437","url":null,"abstract":"The nominal gait of each individual is unique and varies with the walking speed of the person. This poses a difficult problem for powered rehabilitative orthoses since control strategies often require a reference trajectory and give little control to the patient. This paper describes a simple control approach which applies torque resistive to joint movement that is unnatural for healthy individuals in the hip and knee joints during the swing phase of gait. The controller uses a configuration-dependent orthonormal basis to represent vectors in terms of components which are tangent and normal to healthy gait patterns for a continuum of gait speeds. The controller damps motion in the normal direction, thereby resisting movement which is unnatural for healthy individuals. With this control law, subjects are not restricted to a particular reference trajectory and have a large degree of volition over spatiotemporal gait parameters (e.g., stride length, swing time, and cadence). Experiments are conducted to check the feasibility of the control law in a provisional powered pediatric lower-limb orthosis. The gait guidance controller is used in conjunction with a human controller representing an individual with gait impairment. The main results compare gait shape quality when the gait guidance controller is enabled versus disabled, and show how the gait guidance controller is able to reshape gait to more closely resemble that of a healthy individual for various cadences.","PeriodicalId":130415,"journal":{"name":"2019 IEEE 16th International Conference on Rehabilitation Robotics (ICORR)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130492695","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 : 2019-06-01DOI: 10.1109/ICORR.2019.8779396
Özhan Özen, Flavio Traversa, Sofiane Gadi, Karin A. Buetler, T. Nef, L. Marchal-Crespo
One of the main challenges in robotic neuroreha-bilitation is to understand how robots should physically interact with trainees to optimize motor leaning. There is evidence that motor exploration (i.e., the active exploration of new motor tasks) is crucial to boost motor learning. Furthermore, effectiveness of a robotic training strategy depends on several factors, such as task type and trainee’s skill level. We propose that Model Predictive Controllers (MPC) can satisfy many training/trainee’s needs simultaneously, while providing a safe environment without restricting trainees to a fixed trajectory. We designed two nonlinear MPCs to support training of a rich dynamic task (a pendulum task) with a delta robot. These MPCs differ from each other in terms of the application point of the intervention force: (i) to the virtual pendulum mass, and (ii) the virtual rod holding point, which corresponds to the robot end-effector. The effect of the MPCs on task performance, physical effort, motivation and sense of agency was evaluated in fourteen healthy participants. We found that the location of the applied controller force affects the task performance -i.e., the MPC that actuates on the pendulum mass significantly reduced performance errors and sense of agency during training, while the other MPC did not, probably due to low force saturation limits and slow optimization speed of the solver. Participants applied significantly more forces when training with the MPC that actuates on the pendulum holding point, probably because they reacted against the robotic assistance. Although MPCs look very promising for neurorehabilitation, further steps have to be taken to improve their technical limitations. Moreover, the effects of MPCs on motor learning should be evaluated.
{"title":"Multi-purpose Robotic Training Strategies for Neurorehabilitation with Model Predictive Controllers","authors":"Özhan Özen, Flavio Traversa, Sofiane Gadi, Karin A. Buetler, T. Nef, L. Marchal-Crespo","doi":"10.1109/ICORR.2019.8779396","DOIUrl":"https://doi.org/10.1109/ICORR.2019.8779396","url":null,"abstract":"One of the main challenges in robotic neuroreha-bilitation is to understand how robots should physically interact with trainees to optimize motor leaning. There is evidence that motor exploration (i.e., the active exploration of new motor tasks) is crucial to boost motor learning. Furthermore, effectiveness of a robotic training strategy depends on several factors, such as task type and trainee’s skill level. We propose that Model Predictive Controllers (MPC) can satisfy many training/trainee’s needs simultaneously, while providing a safe environment without restricting trainees to a fixed trajectory. We designed two nonlinear MPCs to support training of a rich dynamic task (a pendulum task) with a delta robot. These MPCs differ from each other in terms of the application point of the intervention force: (i) to the virtual pendulum mass, and (ii) the virtual rod holding point, which corresponds to the robot end-effector. The effect of the MPCs on task performance, physical effort, motivation and sense of agency was evaluated in fourteen healthy participants. We found that the location of the applied controller force affects the task performance -i.e., the MPC that actuates on the pendulum mass significantly reduced performance errors and sense of agency during training, while the other MPC did not, probably due to low force saturation limits and slow optimization speed of the solver. Participants applied significantly more forces when training with the MPC that actuates on the pendulum holding point, probably because they reacted against the robotic assistance. Although MPCs look very promising for neurorehabilitation, further steps have to be taken to improve their technical limitations. Moreover, the effects of MPCs on motor learning should be evaluated.","PeriodicalId":130415,"journal":{"name":"2019 IEEE 16th International Conference on Rehabilitation Robotics (ICORR)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117015628","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 : 2019-06-01DOI: 10.1109/ICORR.2019.8779519
Tommaso Poliero, Stefano Toxiri, S. Anastasi, L. Monica, D. Caldwell, J. Ortiz
Despite the growing interest, the adoption of industrial exoskeletons may still be held back by technical limitations. To enhance versatility and promote adoption, one aspect of interest could be represented by the potential of active and quasi-passive devices to automatically distinguish different activities and adjust their assistive profiles accordingly. This contribution focuses on an active back-support exoskeleton and extends previous work proposing the use of a Support Vector Machine to classify walking, bending and standing. Thanks to the introduction of a new feature-forearm muscle activity-this study shows that it is possible to perform reliable online classification. As a consequence, the authors introduce a new hierarchically-structured controller for the exoskeleton under analysis.
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