Pub Date : 2018-08-01DOI: 10.1109/BIOROB.2018.8487211
J. Jung, D. Valencia, A. Belloso, C. Rodriguez-de-Pablo
The ArmAssist, developed by Tecnalia, is a portable cost-effective upper limb rehabilitation platform for at-home tele-rehabilitation after a stroke and a reaching exercise is one of important trainings offered by the ArmAssist. From previous pilot study of the ArmAssist, it has been found that in the reaching exercise, the device should provide comfortable and natural orientation of the forearm for effective and safe rehabilitation. Hence, in this study, we present preliminary measurements of natural orientation of the forearm, specifically yaw angle that corresponds to the orientation of the device during the exercise. Two healthy subjects participated in the measurements using the ArmAssist platform and the results show that comfortable and natural yaw angle of the forearm during the reaching exercise varies with the position while anthropometric information of the subject such as arm length also has an influence on the angle. These findings imply that the forearm position and subject's limb information should be taken into account to find the proper orientation of the device.
{"title":"Preliminary Measurements of Natural Yaw Angle of Forearm During Reaching Exercise for the Effective Robot-Mediated Upper Limb Rehabilitation","authors":"J. Jung, D. Valencia, A. Belloso, C. Rodriguez-de-Pablo","doi":"10.1109/BIOROB.2018.8487211","DOIUrl":"https://doi.org/10.1109/BIOROB.2018.8487211","url":null,"abstract":"The ArmAssist, developed by Tecnalia, is a portable cost-effective upper limb rehabilitation platform for at-home tele-rehabilitation after a stroke and a reaching exercise is one of important trainings offered by the ArmAssist. From previous pilot study of the ArmAssist, it has been found that in the reaching exercise, the device should provide comfortable and natural orientation of the forearm for effective and safe rehabilitation. Hence, in this study, we present preliminary measurements of natural orientation of the forearm, specifically yaw angle that corresponds to the orientation of the device during the exercise. Two healthy subjects participated in the measurements using the ArmAssist platform and the results show that comfortable and natural yaw angle of the forearm during the reaching exercise varies with the position while anthropometric information of the subject such as arm length also has an influence on the angle. These findings imply that the forearm position and subject's limb information should be taken into account to find the proper orientation of the device.","PeriodicalId":382522,"journal":{"name":"2018 7th IEEE International Conference on Biomedical Robotics and Biomechatronics (Biorob)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132993426","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 : 2018-08-01DOI: 10.1109/BIOROB.2018.8487224
Bernardo Noronha, M. Wessels, A. Keemink, A. Bergsma, B. Koopman
This study approaches the use of Bayesian networks to model the human arm movement in an anthropomorphic manner for the control of an upper limb assistive robot. The model receives as input a desired wrist position and outputs three angles, the swivel angle (i.e. the angle that represents the rotation of the plane formed by the upper and lower arm around the axis that passes through the shoulder and wrist) and two angles corresponding to two degrees of freedom of the sternoclavicular joint (elevation/depression and protraction/retraction). These angles, together with the wrist position, fully describe the position of the shoulder and the elbow. A set of recording sessions was conducted to acquire human motion data to train the model for four different activities of daily living. Performance was measured by the elbow and shoulder joints' end-point errors and Pearson's r. The model was able to predict accurately elbow movement (mean error $pmb{0.021}pm pmb{0.020}mathbf{m}$, Pearson's $r$ 0.86-0.99) and shoulder movement (mean error $pmb{0.014}pm pmb{0.011}mathbf{m}$, Pearson's $r$ 0.52-0.99) for wrist trajectories that fall in the set of training data. It was also able to create new motions that were not in the set of training data, with a better accuracy for the elbow joint (mean error $pmb{0.042}pm pmb{0.025}mathbf{m}$, Pearson's $r$ 0.59-0.99) and an average accuracy for the shoulder joint (mean error $pmb{ 0.026}pm pmb{0.012}mathbf{m}$, Pearson's r −0.12-0.99). The proposed model presents a novel method to solve the inverse kinematics problem in the scope of the human upper limb. It can also create movement out of its training data, although not highly correlated with the trajectory performed by a human.
{"title":"An Upper Limb Kinematic Graphical Model for the Prediction of Anthropomorphic Arm Trajectories","authors":"Bernardo Noronha, M. Wessels, A. Keemink, A. Bergsma, B. Koopman","doi":"10.1109/BIOROB.2018.8487224","DOIUrl":"https://doi.org/10.1109/BIOROB.2018.8487224","url":null,"abstract":"This study approaches the use of Bayesian networks to model the human arm movement in an anthropomorphic manner for the control of an upper limb assistive robot. The model receives as input a desired wrist position and outputs three angles, the swivel angle (i.e. the angle that represents the rotation of the plane formed by the upper and lower arm around the axis that passes through the shoulder and wrist) and two angles corresponding to two degrees of freedom of the sternoclavicular joint (elevation/depression and protraction/retraction). These angles, together with the wrist position, fully describe the position of the shoulder and the elbow. A set of recording sessions was conducted to acquire human motion data to train the model for four different activities of daily living. Performance was measured by the elbow and shoulder joints' end-point errors and Pearson's r. The model was able to predict accurately elbow movement (mean error $pmb{0.021}pm pmb{0.020}mathbf{m}$, Pearson's $r$ 0.86-0.99) and shoulder movement (mean error $pmb{0.014}pm pmb{0.011}mathbf{m}$, Pearson's $r$ 0.52-0.99) for wrist trajectories that fall in the set of training data. It was also able to create new motions that were not in the set of training data, with a better accuracy for the elbow joint (mean error $pmb{0.042}pm pmb{0.025}mathbf{m}$, Pearson's $r$ 0.59-0.99) and an average accuracy for the shoulder joint (mean error $pmb{ 0.026}pm pmb{0.012}mathbf{m}$, Pearson's r −0.12-0.99). The proposed model presents a novel method to solve the inverse kinematics problem in the scope of the human upper limb. It can also create movement out of its training data, although not highly correlated with the trajectory performed by a human.","PeriodicalId":382522,"journal":{"name":"2018 7th IEEE International Conference on Biomedical Robotics and Biomechatronics (Biorob)","volume":"167 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133893835","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 : 2018-08-01DOI: 10.1109/BIOROB.2018.8487682
C. Kanzler, Sofia Martinez Gomez, Mike D. Rinderknecht, R. Gassert, O. Lambercy
Quantifying upper limb impairment post-stroke is of essential importance to monitor motor recovery or to evaluate different therapeutic approaches. Instrumented assessments of upper limb function, such as the Virtual Peg Insertion Test (VPIT), often emulate a daily life manipulation activity that requires the subject to actively lift the arm against gravity, which can be challenging for severely impaired patients with arm weakness. With the aim of making the VPIT accessible to patients with severe arm weakness, we conducted a pilot study to analyze the feasability of combining this assessment with an arm weight support (AWS) device in 16 healthy subjects. Subjects performed the VPIT protocol without AWS device and with three different levels of weight support. Usability of combining the VPIT and the AWS device was high in healthy Subjects: The VPIT could be successfully completed without collisions with the AWS device, the duration to set up the AWS device was on average 1.5min, and subjects reported high levels of comfort while experiencing AWS. Metrics representing arm function were mostly not significantly influenced by the presence of the AWS device despite a decrease of 6.2% in movement smoothness, whereas grasping control was not significantly affected at all. The AWS level did not alter motor performance, even though subjects reported a decrease in perceived arm control with an increased AWS level. The high usability of combining the VPIT with an AWS device might enable the assessment of severely impaired patients in clinical practice. However, the influence of the AWS on outcome measures of the VPIT must be taken into account to make assessment results interpretable in the context of daily life reaching and manipulation situations without AWS.
{"title":"Influence of Arm Weight Support on a Robotic Assessment of Upper Limb Function","authors":"C. Kanzler, Sofia Martinez Gomez, Mike D. Rinderknecht, R. Gassert, O. Lambercy","doi":"10.1109/BIOROB.2018.8487682","DOIUrl":"https://doi.org/10.1109/BIOROB.2018.8487682","url":null,"abstract":"Quantifying upper limb impairment post-stroke is of essential importance to monitor motor recovery or to evaluate different therapeutic approaches. Instrumented assessments of upper limb function, such as the Virtual Peg Insertion Test (VPIT), often emulate a daily life manipulation activity that requires the subject to actively lift the arm against gravity, which can be challenging for severely impaired patients with arm weakness. With the aim of making the VPIT accessible to patients with severe arm weakness, we conducted a pilot study to analyze the feasability of combining this assessment with an arm weight support (AWS) device in 16 healthy subjects. Subjects performed the VPIT protocol without AWS device and with three different levels of weight support. Usability of combining the VPIT and the AWS device was high in healthy Subjects: The VPIT could be successfully completed without collisions with the AWS device, the duration to set up the AWS device was on average 1.5min, and subjects reported high levels of comfort while experiencing AWS. Metrics representing arm function were mostly not significantly influenced by the presence of the AWS device despite a decrease of 6.2% in movement smoothness, whereas grasping control was not significantly affected at all. The AWS level did not alter motor performance, even though subjects reported a decrease in perceived arm control with an increased AWS level. The high usability of combining the VPIT with an AWS device might enable the assessment of severely impaired patients in clinical practice. However, the influence of the AWS on outcome measures of the VPIT must be taken into account to make assessment results interpretable in the context of daily life reaching and manipulation situations without AWS.","PeriodicalId":382522,"journal":{"name":"2018 7th IEEE International Conference on Biomedical Robotics and Biomechatronics (Biorob)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131996853","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 : 2018-08-01DOI: 10.1109/BIOROB.2018.8487699
S. Ayad, Mohammed Ayad, Abderkader Megueni, H. Schiøler, L. Struijk
Due to the increase in number of patients with significant gait deficits, the need for sophisticated tools to assist the patient to perform different kinds of locomotion training exercises is highly relevant. Ground walking platforms (GWP) are some of the new robotic gait rehabilitation systems that aim to simulate different ground trajectories for a patient (e.g. plane ground, hill…etc.) with different haptic materials (e.g. water, sand, ice,… etc.). The system targeted in this study aims for providing the user with simulated ground reaction forces based on the users movements in a virtual reality environment by deploying two 6 Degree of Freedoms robotic footplates. This paper presents a part of such control study that aims to simulate the behavior and the interaction of the user, the GWP, and the virtual reality implemented in Unity3D. Using real data, results show good detection of interaction between foot and different medium, while the simulation of the robot gives actual results concerning the properties of simulated medium.
{"title":"Unity3D Based Control Method for a Robotic Ground Walking Platform in a Virtual Reality Environment","authors":"S. Ayad, Mohammed Ayad, Abderkader Megueni, H. Schiøler, L. Struijk","doi":"10.1109/BIOROB.2018.8487699","DOIUrl":"https://doi.org/10.1109/BIOROB.2018.8487699","url":null,"abstract":"Due to the increase in number of patients with significant gait deficits, the need for sophisticated tools to assist the patient to perform different kinds of locomotion training exercises is highly relevant. Ground walking platforms (GWP) are some of the new robotic gait rehabilitation systems that aim to simulate different ground trajectories for a patient (e.g. plane ground, hill…etc.) with different haptic materials (e.g. water, sand, ice,… etc.). The system targeted in this study aims for providing the user with simulated ground reaction forces based on the users movements in a virtual reality environment by deploying two 6 Degree of Freedoms robotic footplates. This paper presents a part of such control study that aims to simulate the behavior and the interaction of the user, the GWP, and the virtual reality implemented in Unity3D. Using real data, results show good detection of interaction between foot and different medium, while the simulation of the robot gives actual results concerning the properties of simulated medium.","PeriodicalId":382522,"journal":{"name":"2018 7th IEEE International Conference on Biomedical Robotics and Biomechatronics (Biorob)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129818426","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 : 2018-08-01DOI: 10.1109/BIOROB.2018.8487187
G. A. Fontanelli, M. Selvaggio, Marco Ferro, F. Ficuciello, M. Vendiuelli, B. Siciliano
In this work we present a V-REP simulator for the da Vinci Research Kit (dVRK). The simulator contains a full robot kinematic model and integrated sensors. A robot operating system (ROS) interface has been created for easy use and development of common software components. Moreover, several scenes have been implemented to illustrate the performance and potentiality of the developed simulator. Both the simulator and the example scenes are available to the community as an open source software.
{"title":"A V-REP Simulator for the da Vinci Research Kit Robotic Platform","authors":"G. A. Fontanelli, M. Selvaggio, Marco Ferro, F. Ficuciello, M. Vendiuelli, B. Siciliano","doi":"10.1109/BIOROB.2018.8487187","DOIUrl":"https://doi.org/10.1109/BIOROB.2018.8487187","url":null,"abstract":"In this work we present a V-REP simulator for the da Vinci Research Kit (dVRK). The simulator contains a full robot kinematic model and integrated sensors. A robot operating system (ROS) interface has been created for easy use and development of common software components. Moreover, several scenes have been implemented to illustrate the performance and potentiality of the developed simulator. Both the simulator and the example scenes are available to the community as an open source software.","PeriodicalId":382522,"journal":{"name":"2018 7th IEEE International Conference on Biomedical Robotics and Biomechatronics (Biorob)","volume":"05 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115093231","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 : 2018-08-01DOI: 10.1109/BIOROB.2018.8487223
Lesley-Ann Duflot, B. Tamadazte, N. Andreff, Alexandre Krupa
This paper deals with the development of an Optical Coherence Tomography (OCT) based visual servoing. The proposed control law uses the wavelet coefficients of the OCT images as the signal control inputs instead of the conventional geometric visual features (points, lines, moments, etc.). An important contribution is the determination of the interaction matrix that links the variation of the wavelet coefficients to the OCT probe (respectively to the robotic platform) spatial velocity. This interaction matrix, required in the visual control law, is obtained from time-derivation of the wavelet coefficients. This work is carried out in a medical context which consists of automatically moving a biological sample in such a way to go back to the position of a sample region that corresponds to a previous optical biopsy (OCT image). For instance, the proposed methodology makes it possible to follow accurately the progress of a pathological tissue between an optical biopsy and a former one. The developed method was experimentally validated using an OCT imaging system placed in an eye-to-hand configuration viewing the robotic platform sample holder. The obtained results demonstrated the feasibility of this type of visual servoing approach and promising performances in terms of convergence and accuracy.
{"title":"Wavelet-Based Visual Servoing Using OCT Images","authors":"Lesley-Ann Duflot, B. Tamadazte, N. Andreff, Alexandre Krupa","doi":"10.1109/BIOROB.2018.8487223","DOIUrl":"https://doi.org/10.1109/BIOROB.2018.8487223","url":null,"abstract":"This paper deals with the development of an Optical Coherence Tomography (OCT) based visual servoing. The proposed control law uses the wavelet coefficients of the OCT images as the signal control inputs instead of the conventional geometric visual features (points, lines, moments, etc.). An important contribution is the determination of the interaction matrix that links the variation of the wavelet coefficients to the OCT probe (respectively to the robotic platform) spatial velocity. This interaction matrix, required in the visual control law, is obtained from time-derivation of the wavelet coefficients. This work is carried out in a medical context which consists of automatically moving a biological sample in such a way to go back to the position of a sample region that corresponds to a previous optical biopsy (OCT image). For instance, the proposed methodology makes it possible to follow accurately the progress of a pathological tissue between an optical biopsy and a former one. The developed method was experimentally validated using an OCT imaging system placed in an eye-to-hand configuration viewing the robotic platform sample holder. The obtained results demonstrated the feasibility of this type of visual servoing approach and promising performances in terms of convergence and accuracy.","PeriodicalId":382522,"journal":{"name":"2018 7th IEEE International Conference on Biomedical Robotics and Biomechatronics (Biorob)","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115178183","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 : 2018-08-01DOI: 10.1109/BIOROB.2018.8487812
Leonard F. Engels, L. Cappello, C. Cipriani
Using a hand prosthesis means grasping without tactile information. Although supplementary sensory feedback has been investigated extensively, few study results could translate into clinical applications. Unreliable and imprecise feedforward control of current hand prostheses hinders the investigation of supplementary sensory feedback, so an ideal feedforward tool should be used. Thus, we aimed to create a device that would allow to use the sensory deprived human hand as an ideal tool without the need for local anesthesia. For this, we fashioned silicone digit extensions with integrated force sensors and tested the performance of 12 volunteers in grasping with these extensions. Two tests were performed: a simple pick and lift test to compare performance to anesthetized digits, and a virtual egg test to assess grasping efficiency. We found that the extensions significantly alter grasping. In future studies, these extensions will help us investigate how to artificially restore the information necessary for successful and efficient grasping with an ideal feedforward tool.
{"title":"Digital Extensions with Bi-axial Fingertip Sensors for Supplementary Tactile Feedback Studies","authors":"Leonard F. Engels, L. Cappello, C. Cipriani","doi":"10.1109/BIOROB.2018.8487812","DOIUrl":"https://doi.org/10.1109/BIOROB.2018.8487812","url":null,"abstract":"Using a hand prosthesis means grasping without tactile information. Although supplementary sensory feedback has been investigated extensively, few study results could translate into clinical applications. Unreliable and imprecise feedforward control of current hand prostheses hinders the investigation of supplementary sensory feedback, so an ideal feedforward tool should be used. Thus, we aimed to create a device that would allow to use the sensory deprived human hand as an ideal tool without the need for local anesthesia. For this, we fashioned silicone digit extensions with integrated force sensors and tested the performance of 12 volunteers in grasping with these extensions. Two tests were performed: a simple pick and lift test to compare performance to anesthetized digits, and a virtual egg test to assess grasping efficiency. We found that the extensions significantly alter grasping. In future studies, these extensions will help us investigate how to artificially restore the information necessary for successful and efficient grasping with an ideal feedforward tool.","PeriodicalId":382522,"journal":{"name":"2018 7th IEEE International Conference on Biomedical Robotics and Biomechatronics (Biorob)","volume":"21 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116699439","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 : 2018-08-01DOI: 10.1109/BIOROB.2018.8487750
Jakob Ziegler, H. Gattringer, A. Müller
Robotic systems for rehabilitation of movement disorders and motion assistance are gaining increased attention. Robust classification of motion data as well as reliable recognition of the user's intended movement play a major role in order to maximize wearability and effectiveness of such systems. Biological signals like electromyography (EMG) provide a direct connection to the motion intention of the wearer. This paper addresses the classification of stance phase and swing phase during healthy human gait based on the muscle activity in both legs using the theory of Support Vector Machines (SVM). A novel EMG feature calculated from the bilateral EMG signals of muscle pairs is introduced. The presented method shows promising results with classification accuracies of up to 96%.
{"title":"Classification of Gait Phases Based on Bilateral EMG Data Using Support Vector Machines","authors":"Jakob Ziegler, H. Gattringer, A. Müller","doi":"10.1109/BIOROB.2018.8487750","DOIUrl":"https://doi.org/10.1109/BIOROB.2018.8487750","url":null,"abstract":"Robotic systems for rehabilitation of movement disorders and motion assistance are gaining increased attention. Robust classification of motion data as well as reliable recognition of the user's intended movement play a major role in order to maximize wearability and effectiveness of such systems. Biological signals like electromyography (EMG) provide a direct connection to the motion intention of the wearer. This paper addresses the classification of stance phase and swing phase during healthy human gait based on the muscle activity in both legs using the theory of Support Vector Machines (SVM). A novel EMG feature calculated from the bilateral EMG signals of muscle pairs is introduced. The presented method shows promising results with classification accuracies of up to 96%.","PeriodicalId":382522,"journal":{"name":"2018 7th IEEE International Conference on Biomedical Robotics and Biomechatronics (Biorob)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132292908","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 : 2018-08-01DOI: 10.1109/BIOROB.2018.8487936
Christopher Caulcrick, Felix Russell, Samuel Wilson, Caleb Sawade, R. Vaidyanathan
This paper introduces a method which uses feedforward neural networks (FNNs) for estimating gait cycle progress using data recorded from inertial and muscle activity sensors attached to one side of the lower body. Three-axis inertial measurement unit (IMU) readings from accelerometers and gyroscopes located above the outer ankle and knee were fused with mechanomyogram (MMG) sensor readings from across major muscle groups on the left leg. Validation was against ground truth gathered concurrently with VICON motion capture. The performance was characterised by rms error (Erms) and max error (Emax), averaged across four cross-validated trials, and enhanced by adjusting number of sliding window frames and hidden layer neurons. The final configuration estimated gait cycle progress with Erms of 1.6% and Emax of 6.8%. This demonstrates promise for such a method to be used for control of unilateral robotic prostheses and exoskeletons, providing state estimation of gait progress from low power sensors limited to one side of the lower body.
{"title":"Unilateral Inertial and Muscle Activity Sensor Fusion for Gait Cycle Progress Estimation*","authors":"Christopher Caulcrick, Felix Russell, Samuel Wilson, Caleb Sawade, R. Vaidyanathan","doi":"10.1109/BIOROB.2018.8487936","DOIUrl":"https://doi.org/10.1109/BIOROB.2018.8487936","url":null,"abstract":"This paper introduces a method which uses feedforward neural networks (FNNs) for estimating gait cycle progress using data recorded from inertial and muscle activity sensors attached to one side of the lower body. Three-axis inertial measurement unit (IMU) readings from accelerometers and gyroscopes located above the outer ankle and knee were fused with mechanomyogram (MMG) sensor readings from across major muscle groups on the left leg. Validation was against ground truth gathered concurrently with VICON motion capture. The performance was characterised by rms error (Erms) and max error (Emax), averaged across four cross-validated trials, and enhanced by adjusting number of sliding window frames and hidden layer neurons. The final configuration estimated gait cycle progress with Erms of 1.6% and Emax of 6.8%. This demonstrates promise for such a method to be used for control of unilateral robotic prostheses and exoskeletons, providing state estimation of gait progress from low power sensors limited to one side of the lower body.","PeriodicalId":382522,"journal":{"name":"2018 7th IEEE International Conference on Biomedical Robotics and Biomechatronics (Biorob)","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134351552","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 : 2018-08-01DOI: 10.1109/BIOROB.2018.8487725
G. J. Vrooijink, Hassna Irzan, S. Misra
The improved natural hemodynamics offered by mitral valve (MV) repair strategies aims to prevent heart failure and to minimize the use of long-term anticoagulant. This combined with the reduced patient trauma offered by minimally invasive surgical (MIS) interventions, requires an increase in capabilities of MIS MV repair. The use of robotic catheters have been described in MIS applications such as navigational tasks, ablation and MV repair. The majority of the robotic catheters are evaluated in testbeds capable of partially mimicking the cardiac environment (e.g., beating heart motion or relevant anatomy), while the validation of robotic catheters in a clinical scenario is associated with significant preparation time and limited availability. Therefore, continuous catheter development could be aided by an accessible and available testbed capable of reproducing beating heart motions, circulation and the relevant anatomy in MIS cardiovascular interventions. In this study, we contribute a beating heart testbed for the evaluation of robotic catheters in MIS cardiovascular interventions. Our work describes a heart model with relevant interior structures and an integrated realistic MV model, which is attached to a Stewart platform in order to reproduce the beating heart motions based on pre-operative patient data. The beating heart model is extended with an artificial aortic valve, a systemic arterial model, a venous reservoir and a pulsatile pump to mimic the systemic circulation. Experimental evaluation showed systemic circulation and beating heart motion reproduction for 70 BPM with a mean absolute distance error of 1.26 mm, while a robotic catheter in the heart model is observed by ultrasound imaging and electromagnetic position tracking. Therefore, the presented testbed is capable of evaluating MIS robotic cardiovascular interventions such as MV repair, navigation tasks and ablation.
{"title":"A Beating Heart Testbed for the Evaluation of Robotic Cardiovascular Interventions","authors":"G. J. Vrooijink, Hassna Irzan, S. Misra","doi":"10.1109/BIOROB.2018.8487725","DOIUrl":"https://doi.org/10.1109/BIOROB.2018.8487725","url":null,"abstract":"The improved natural hemodynamics offered by mitral valve (MV) repair strategies aims to prevent heart failure and to minimize the use of long-term anticoagulant. This combined with the reduced patient trauma offered by minimally invasive surgical (MIS) interventions, requires an increase in capabilities of MIS MV repair. The use of robotic catheters have been described in MIS applications such as navigational tasks, ablation and MV repair. The majority of the robotic catheters are evaluated in testbeds capable of partially mimicking the cardiac environment (e.g., beating heart motion or relevant anatomy), while the validation of robotic catheters in a clinical scenario is associated with significant preparation time and limited availability. Therefore, continuous catheter development could be aided by an accessible and available testbed capable of reproducing beating heart motions, circulation and the relevant anatomy in MIS cardiovascular interventions. In this study, we contribute a beating heart testbed for the evaluation of robotic catheters in MIS cardiovascular interventions. Our work describes a heart model with relevant interior structures and an integrated realistic MV model, which is attached to a Stewart platform in order to reproduce the beating heart motions based on pre-operative patient data. The beating heart model is extended with an artificial aortic valve, a systemic arterial model, a venous reservoir and a pulsatile pump to mimic the systemic circulation. Experimental evaluation showed systemic circulation and beating heart motion reproduction for 70 BPM with a mean absolute distance error of 1.26 mm, while a robotic catheter in the heart model is observed by ultrasound imaging and electromagnetic position tracking. Therefore, the presented testbed is capable of evaluating MIS robotic cardiovascular interventions such as MV repair, navigation tasks and ablation.","PeriodicalId":382522,"journal":{"name":"2018 7th IEEE International Conference on Biomedical Robotics and Biomechatronics (Biorob)","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132796843","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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