Pub Date : 2018-08-01DOI: 10.1109/BIOROB.2018.8488118
Bokeon Kwak, Dongyoung Lee, J. Bae
Some aquatic insects can rapidly dash over the water surface by secreting chemical material that lowers the surface tension behind. This locomotion is commonly known as Marangoni propulsion, and we built a non-tethered miniature robot inspired by their mobility. The robot had six circular footpads with equilateral triangular cross section, and weighed 14.8 gram including on-board electronics, a battery, and a servo motor. Although the robot successfully skimmed over the water surface by dripping alcohol (e.g., 3-Methyl-l-butanol), the robot could not maintain a linear motion by itself. Therefore, we designed and attached flexural joints at the hind legs of the robot to compensate its linear motion; the asymmetric force applied to the hind legs subsequently induced another counter moment due to the bending of flexural joints. During the experiments, these joints were effective at reducing undesired lateral deviation more than 3-fold compared to one without flexural joints. Also, the characteristics of the robot's locomotion was similar with the locomotion of aquatic arthropods according to the dimensionless number analysis.
{"title":"Flexural Joints for Improved Linear Motion of a Marangoni Propulsion Robot: Design and Experiment","authors":"Bokeon Kwak, Dongyoung Lee, J. Bae","doi":"10.1109/BIOROB.2018.8488118","DOIUrl":"https://doi.org/10.1109/BIOROB.2018.8488118","url":null,"abstract":"Some aquatic insects can rapidly dash over the water surface by secreting chemical material that lowers the surface tension behind. This locomotion is commonly known as Marangoni propulsion, and we built a non-tethered miniature robot inspired by their mobility. The robot had six circular footpads with equilateral triangular cross section, and weighed 14.8 gram including on-board electronics, a battery, and a servo motor. Although the robot successfully skimmed over the water surface by dripping alcohol (e.g., 3-Methyl-l-butanol), the robot could not maintain a linear motion by itself. Therefore, we designed and attached flexural joints at the hind legs of the robot to compensate its linear motion; the asymmetric force applied to the hind legs subsequently induced another counter moment due to the bending of flexural joints. During the experiments, these joints were effective at reducing undesired lateral deviation more than 3-fold compared to one without flexural joints. Also, the characteristics of the robot's locomotion was similar with the locomotion of aquatic arthropods according to the dimensionless number analysis.","PeriodicalId":382522,"journal":{"name":"2018 7th IEEE International Conference on Biomedical Robotics and Biomechatronics (Biorob)","volume":"145 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":"124646315","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.2018.8487220
Uriel Martinez-Hernandez, Adrian Rubio Solis, A. Dehghani
In this paper, a strategy for recognition of human walking activities and prediction of gait periods using wearable sensors is presented. First, a Convolutional Neural Network (CNN) is developed for the recognition of three walking activities (level-ground walking, ramp ascent and descent) and recognition of gait periods. Second, a first-order Markov Chain (MC) is employed for the prediction of gait periods, based on the observation of decisions made by the CNN for each walking activity. The validation of the proposed methods is performed using data from three inertial measurement units (IMU) attached to the lower limbs of participants. The results show that the CNN, together with the first-order MC, achieves mean accuracies of 100% and 98.32% for recognition of walking activities and gait periods, respectively. Prediction of gait periods are achieved with mean accuracies of 99.78%, 97.56% and 97.35% during level-ground walking, ramp ascent and descent, respectively. Overall, the benefits of our work for accurate recognition and prediction of walking activity and gait periods, make it a suitable high-level method for the development of intelligent assistive robots.
{"title":"Recognition of Walking Activity and Prediction of Gait Periods with a CNN and First-Order MC Strategy","authors":"Uriel Martinez-Hernandez, Adrian Rubio Solis, A. Dehghani","doi":"10.1109/BIOROB.2018.8487220","DOIUrl":"https://doi.org/10.1109/BIOROB.2018.8487220","url":null,"abstract":"In this paper, a strategy for recognition of human walking activities and prediction of gait periods using wearable sensors is presented. First, a Convolutional Neural Network (CNN) is developed for the recognition of three walking activities (level-ground walking, ramp ascent and descent) and recognition of gait periods. Second, a first-order Markov Chain (MC) is employed for the prediction of gait periods, based on the observation of decisions made by the CNN for each walking activity. The validation of the proposed methods is performed using data from three inertial measurement units (IMU) attached to the lower limbs of participants. The results show that the CNN, together with the first-order MC, achieves mean accuracies of 100% and 98.32% for recognition of walking activities and gait periods, respectively. Prediction of gait periods are achieved with mean accuracies of 99.78%, 97.56% and 97.35% during level-ground walking, ramp ascent and descent, respectively. Overall, the benefits of our work for accurate recognition and prediction of walking activity and gait periods, make it a suitable high-level method for the development of intelligent assistive robots.","PeriodicalId":382522,"journal":{"name":"2018 7th IEEE International Conference on Biomedical Robotics and Biomechatronics (Biorob)","volume":"104 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":"124742719","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.2018.8487912
Dalia De Santis, Patrycja Dzialecka, F. Mussa-Ivaldi
Interfaces that exploit biological signals or movements to control the operation of lower-dimensional systems external to the body are at the frontier for augmenting human abilities, but also constitute a learning challenge for their users. We developed and tested an unsupervised coadaptive algorithm that changed the mapping of a body machine interface to match the natural movement distribution of the users. Users controlled a cursor on a computer monitor using arm and shoulder motions captured by a set of inertial sensors in either of three conditions: i) a constant body-to-cursor map obtained through Principal Component Analysis of calibration movements, ii) a map that was recomputed at specified points in time, iii) a map that adaptively changed over time. We used recursive online PCA to incrementally shift the projection space towards the 2-dimensional subspace capturing the greatest sensor signal variance. Results suggest that training with the coadaptive BMI allows for faster internalization of the control space while reducing user's reliance on visual feedback.
{"title":"Unsupervised Coadaptation of an Assistive Interface to Facilitate Sensorimotor Learning of Redundant Control","authors":"Dalia De Santis, Patrycja Dzialecka, F. Mussa-Ivaldi","doi":"10.1109/BIOROB.2018.8487912","DOIUrl":"https://doi.org/10.1109/BIOROB.2018.8487912","url":null,"abstract":"Interfaces that exploit biological signals or movements to control the operation of lower-dimensional systems external to the body are at the frontier for augmenting human abilities, but also constitute a learning challenge for their users. We developed and tested an unsupervised coadaptive algorithm that changed the mapping of a body machine interface to match the natural movement distribution of the users. Users controlled a cursor on a computer monitor using arm and shoulder motions captured by a set of inertial sensors in either of three conditions: i) a constant body-to-cursor map obtained through Principal Component Analysis of calibration movements, ii) a map that was recomputed at specified points in time, iii) a map that adaptively changed over time. We used recursive online PCA to incrementally shift the projection space towards the 2-dimensional subspace capturing the greatest sensor signal variance. Results suggest that training with the coadaptive BMI allows for faster internalization of the control space while reducing user's reliance on visual feedback.","PeriodicalId":382522,"journal":{"name":"2018 7th IEEE International Conference on Biomedical Robotics and Biomechatronics (Biorob)","volume":"8 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":"122717714","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.2018.8487188
Francesca Stival, S. Michieletto, Andrea De Agnoi, E. Pagello
Ahstract- The interest on wearable prosthetic devices has boost the research for a robust framework to help injured subjects to regain their lost functionality. A great number of solutions exploit physiological human signals, such as Electromyography (EMG), to naturally control the prosthesis, reproducing what happens in the human limbs. In this paper, we propose for the first time a way to integrate EMG signals with Inertial Measurement Unit (IMU) information, as a way to improve subject-independent models for controlling robotic hands. EMG data are very sensitive to both physical and physiological variations, and this is particularly true between different subjects. The introduction of IMUs aims at enriching the subject-independent model, making it more robust with information not strictly dependent from the physiological characteristics of the subject. We compare three different models: the first based on EMG solely, the second merging data from EMG and the 2 best IMUs available, and the third using EMG and IMUs information corresponding to the same 3 electrodes. The three techniques are tested on two different movements executed by 35 healthy subjects, by using a leave-one-out approach. The framework is able to estimate online the bending angles of the joints involved in the motion, obtaining an accuracy up to 0.8634. The resulting joint angles are used to actuate a robotic hand in a simulated environment.
{"title":"Toward a Better Robotic Hand Prosthesis Control: Using EMG and IMU Features for a Subject Independent Multi Joint Regression Model","authors":"Francesca Stival, S. Michieletto, Andrea De Agnoi, E. Pagello","doi":"10.1109/BIOROB.2018.8487188","DOIUrl":"https://doi.org/10.1109/BIOROB.2018.8487188","url":null,"abstract":"Ahstract- The interest on wearable prosthetic devices has boost the research for a robust framework to help injured subjects to regain their lost functionality. A great number of solutions exploit physiological human signals, such as Electromyography (EMG), to naturally control the prosthesis, reproducing what happens in the human limbs. In this paper, we propose for the first time a way to integrate EMG signals with Inertial Measurement Unit (IMU) information, as a way to improve subject-independent models for controlling robotic hands. EMG data are very sensitive to both physical and physiological variations, and this is particularly true between different subjects. The introduction of IMUs aims at enriching the subject-independent model, making it more robust with information not strictly dependent from the physiological characteristics of the subject. We compare three different models: the first based on EMG solely, the second merging data from EMG and the 2 best IMUs available, and the third using EMG and IMUs information corresponding to the same 3 electrodes. The three techniques are tested on two different movements executed by 35 healthy subjects, by using a leave-one-out approach. The framework is able to estimate online the bending angles of the joints involved in the motion, obtaining an accuracy up to 0.8634. The resulting joint angles are used to actuate a robotic hand in a simulated environment.","PeriodicalId":382522,"journal":{"name":"2018 7th IEEE International Conference on Biomedical Robotics and Biomechatronics (Biorob)","volume":"58 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":"125133202","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.2018.8487654
Amar V. Krishna, S. Chandar, Rahul S. Bama, A. Roy
In this paper, we present an interactive visual task for robot-assisted gait training after stroke. This stand-alone game is interfaced with the impedance controlled modular ankle exoskeleton (“Anklebot”) that provides support only as needed to enhance ankle neuro-motor control in the context of treadmill walking. The interactive task is designed as a simple soccer-based computer video-game such that movement of the game cursor (soccer ball) towards the goal is determined by a patient's volitional ankle torque. Here, we present the design and features of this interactive video game, as well as the underlying biomechanical model that relates patient-to-game performance. Additionally, we embed simple Statistical analysis algorithms to auto-adjust game parameters in real-time based on patient performance for patient motivation. Finally, we present preliminary test results from a stroke subject trials to validate the video-game performance and its feasibility for clinical use.
{"title":"Novel Interactive Visual Task for Robot-Assisted Gait Training for Stroke Rehabilitation","authors":"Amar V. Krishna, S. Chandar, Rahul S. Bama, A. Roy","doi":"10.1109/BIOROB.2018.8487654","DOIUrl":"https://doi.org/10.1109/BIOROB.2018.8487654","url":null,"abstract":"In this paper, we present an interactive visual task for robot-assisted gait training after stroke. This stand-alone game is interfaced with the impedance controlled modular ankle exoskeleton (“Anklebot”) that provides support only as needed to enhance ankle neuro-motor control in the context of treadmill walking. The interactive task is designed as a simple soccer-based computer video-game such that movement of the game cursor (soccer ball) towards the goal is determined by a patient's volitional ankle torque. Here, we present the design and features of this interactive video game, as well as the underlying biomechanical model that relates patient-to-game performance. Additionally, we embed simple Statistical analysis algorithms to auto-adjust game parameters in real-time based on patient performance for patient motivation. Finally, we present preliminary test results from a stroke subject trials to validate the video-game performance and its feasibility for clinical use.","PeriodicalId":382522,"journal":{"name":"2018 7th IEEE International Conference on Biomedical Robotics and Biomechatronics (Biorob)","volume":"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":"125187039","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.2018.8488087
M. L. Delva, Maram Sakr, Rana Sadeghi Chegani, Mahta Khoshnam, C. Menon
Force Myography (FMG) quantifies the volumetric changes in a limb occurring with muscle contraction and can potentially be used to design convenient, low-cost interfaces to assist in activities of daily living (ADL). The aim of this study is to evaluate whether elders can effectively use an FMG-based wrist band to interact with their environment. In this regard, an FMG band consisted of an array of force-sensing resistors (FSRs) was designed. Ten participants were grouped in two classes, namely “senior” and “non-senior”, and were instructed to perform control gestures and unconstrained ADL tasks while wearing the designed wrist band. To evaluate the usability of the band, correct identification of hand gestures and reaction times were noted. Results showed that seniors were capable of successfully performing a control gesture within 1.4 s of displaying the instruction during online testing. The individually-trained gesture identification algorithm achieved an accuracy of 76.5% in this case. Non-seniors had a reaction time of 0.9 s with an overall classification accuracy of 91.2%. This preliminary study demonstrates the potential and feasibility of utilizing FMG-based technology to provide elders with assistance during activities of daily living.
{"title":"Investigation into the Potential to Create a Force Myography-based Smart-home Controller for Aging Populations","authors":"M. L. Delva, Maram Sakr, Rana Sadeghi Chegani, Mahta Khoshnam, C. Menon","doi":"10.1109/BIOROB.2018.8488087","DOIUrl":"https://doi.org/10.1109/BIOROB.2018.8488087","url":null,"abstract":"Force Myography (FMG) quantifies the volumetric changes in a limb occurring with muscle contraction and can potentially be used to design convenient, low-cost interfaces to assist in activities of daily living (ADL). The aim of this study is to evaluate whether elders can effectively use an FMG-based wrist band to interact with their environment. In this regard, an FMG band consisted of an array of force-sensing resistors (FSRs) was designed. Ten participants were grouped in two classes, namely “senior” and “non-senior”, and were instructed to perform control gestures and unconstrained ADL tasks while wearing the designed wrist band. To evaluate the usability of the band, correct identification of hand gestures and reaction times were noted. Results showed that seniors were capable of successfully performing a control gesture within 1.4 s of displaying the instruction during online testing. The individually-trained gesture identification algorithm achieved an accuracy of 76.5% in this case. Non-seniors had a reaction time of 0.9 s with an overall classification accuracy of 91.2%. This preliminary study demonstrates the potential and feasibility of utilizing FMG-based technology to provide elders with assistance during activities of daily living.","PeriodicalId":382522,"journal":{"name":"2018 7th IEEE International Conference on Biomedical Robotics and Biomechatronics (Biorob)","volume":"45 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":"125728248","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.2018.8487937
Elisa Donati, Fernando Perez-Pefia, C. Bartolozzi, G. Indiveri, E. Chicca
An ever increasing amount of robotic platforms are being equipped with a new generation of neuromorphic computing architectures. Neuromorphic computing systems represent a promising brain-inspired technology that use asynchronous pulses to encode, transmit, and process sensory signals, typically implemented in compact low-latency and low-power devices. However, although multiple examples of sensing and processing neuromorphic devices have been successfully deployed on robotic platforms, no example of event-based neuromorphic motor controller has been proposed yet. In this paper, we present an open-loop neuromorphic controller implemented using a full-custom spiking neural network VLSI chip interfaced to motors for performing position control. The proposed controller is based on biologically inspired principles by which the discharge of motor-neuron populations produces muscle contractions. Following these principles, we use the spikes of the silicon neurons present in the neuromorphic chip to encode the target position and drive the motors using Pulse Frequency Modulation (PFM) technique, rather than the more traditional Pulse Width Modulation (PWM) one.
{"title":"Open-Loop Neuromorphic Controller Implemented on VLSI Devices","authors":"Elisa Donati, Fernando Perez-Pefia, C. Bartolozzi, G. Indiveri, E. Chicca","doi":"10.1109/BIOROB.2018.8487937","DOIUrl":"https://doi.org/10.1109/BIOROB.2018.8487937","url":null,"abstract":"An ever increasing amount of robotic platforms are being equipped with a new generation of neuromorphic computing architectures. Neuromorphic computing systems represent a promising brain-inspired technology that use asynchronous pulses to encode, transmit, and process sensory signals, typically implemented in compact low-latency and low-power devices. However, although multiple examples of sensing and processing neuromorphic devices have been successfully deployed on robotic platforms, no example of event-based neuromorphic motor controller has been proposed yet. In this paper, we present an open-loop neuromorphic controller implemented using a full-custom spiking neural network VLSI chip interfaced to motors for performing position control. The proposed controller is based on biologically inspired principles by which the discharge of motor-neuron populations produces muscle contractions. Following these principles, we use the spikes of the silicon neurons present in the neuromorphic chip to encode the target position and drive the motors using Pulse Frequency Modulation (PFM) technique, rather than the more traditional Pulse Width Modulation (PWM) one.","PeriodicalId":382522,"journal":{"name":"2018 7th IEEE International Conference on Biomedical Robotics and Biomechatronics (Biorob)","volume":"9 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":"129317318","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.2018.8487795
R. Roy, Ankush Roy, M. Mahadevappa
In power grasping, all the fingers and thumb are moved simultaneously towards the object centre to form a stable grip. The force imparted on the object while grasping is distributed among all the phalanges. The calculation of interphalangeal flexion angles is essential to ensure their contact with the object surface. For holding cylindrical and spherical shaped objects, the flexion angles follow inverse proportionality with the diameter of the object. In this study, we have proposed a mathematical model by establishing a relationship of the interphalangeal flexion angles with the object diameter to replicate this natural manoeuvre in a hand prosthesis. We have derived that the sum of tangents of all the 14 interphalangeal flexion angles involved in power grasps, depends only on the length of intermediate phalanx of all the fingers and the object diameter. This relation eliminated the requirement of other phalangeal lengths and thus reduced overall variable complexity. To automate the computation of interphalangeal flexion angles, here we have implemented particle swarm optimisation (PSO). The relationship of the joint angle variation with the object diameter is used here as the fitness function. The resulted flexion angles were further evaluated for their efficacy in a simulated hand grasping model. In contrast to the generic prosthetic hands, where the joints are sequentially rotated according to their constraints from the object surface, this model allows simultaneous rotation of the joint angles according to the optimum fitness function using PSO.
{"title":"Adaptive Grasping Using an Interphalangeal Flexion Angle Model and Particle Swarm Optimization","authors":"R. Roy, Ankush Roy, M. Mahadevappa","doi":"10.1109/BIOROB.2018.8487795","DOIUrl":"https://doi.org/10.1109/BIOROB.2018.8487795","url":null,"abstract":"In power grasping, all the fingers and thumb are moved simultaneously towards the object centre to form a stable grip. The force imparted on the object while grasping is distributed among all the phalanges. The calculation of interphalangeal flexion angles is essential to ensure their contact with the object surface. For holding cylindrical and spherical shaped objects, the flexion angles follow inverse proportionality with the diameter of the object. In this study, we have proposed a mathematical model by establishing a relationship of the interphalangeal flexion angles with the object diameter to replicate this natural manoeuvre in a hand prosthesis. We have derived that the sum of tangents of all the 14 interphalangeal flexion angles involved in power grasps, depends only on the length of intermediate phalanx of all the fingers and the object diameter. This relation eliminated the requirement of other phalangeal lengths and thus reduced overall variable complexity. To automate the computation of interphalangeal flexion angles, here we have implemented particle swarm optimisation (PSO). The relationship of the joint angle variation with the object diameter is used here as the fitness function. The resulted flexion angles were further evaluated for their efficacy in a simulated hand grasping model. In contrast to the generic prosthetic hands, where the joints are sequentially rotated according to their constraints from the object surface, this model allows simultaneous rotation of the joint angles according to the optimum fitness function using PSO.","PeriodicalId":382522,"journal":{"name":"2018 7th IEEE International Conference on Biomedical Robotics and Biomechatronics (Biorob)","volume":"21 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":"124776968","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.2018.8488157
Brandon P. R. Edmonds, A. L. Trejos
Mechatronic rehabilitative devices have been proven to provide cost effective solutions to long term physical therapy for patients with musculoskeletal disorders. However, current actuator technologies limit the minimization of the overall size and weight of these devices preventing innovation into unobtrusive wearable form factors that are also effective and comfortable. This study is focused on a recently discovered smart actuator made from flexible nylon thread, which has exhibited a great potential for use in wearable mechatronic devices. This is known as the twisted coiled actuator (TCA) due to the hyper twisting and induced coiling involved in its fabrication process. One of the limiting factors of the TCA, is the thermal activation mechanism, which results in a slow cooling phase and a low working bandwidth. This paper is focused on optimizing an active cooling design using numerical analysis. To do this, a simple pipe geometry was designed and tested using fluid dynamics software. Three off-the-shelf fluidic pumps were simulated using varying tube diameters to find a sufficient cooling rate, a minimum fluid volume, and to select a proper pump for future testing. The results indicate that a global maximum cooling rate exists for each specific pump at a unique tube diameter. Additionally, the speed of cooling was under 500 ms concluding that the pumps tested can sufficiently provide the cooling rates required to assist motion in wearable devices. Furthermore, the process developed here provides quantitative support for the optimal selection of initial design parameters and can be translated to designs using different form factors and fluid properties.
{"title":"Computational Fluid Dynamics Study of a Soft Actuator for Use in Wearable Mechatronic Devices","authors":"Brandon P. R. Edmonds, A. L. Trejos","doi":"10.1109/BIOROB.2018.8488157","DOIUrl":"https://doi.org/10.1109/BIOROB.2018.8488157","url":null,"abstract":"Mechatronic rehabilitative devices have been proven to provide cost effective solutions to long term physical therapy for patients with musculoskeletal disorders. However, current actuator technologies limit the minimization of the overall size and weight of these devices preventing innovation into unobtrusive wearable form factors that are also effective and comfortable. This study is focused on a recently discovered smart actuator made from flexible nylon thread, which has exhibited a great potential for use in wearable mechatronic devices. This is known as the twisted coiled actuator (TCA) due to the hyper twisting and induced coiling involved in its fabrication process. One of the limiting factors of the TCA, is the thermal activation mechanism, which results in a slow cooling phase and a low working bandwidth. This paper is focused on optimizing an active cooling design using numerical analysis. To do this, a simple pipe geometry was designed and tested using fluid dynamics software. Three off-the-shelf fluidic pumps were simulated using varying tube diameters to find a sufficient cooling rate, a minimum fluid volume, and to select a proper pump for future testing. The results indicate that a global maximum cooling rate exists for each specific pump at a unique tube diameter. Additionally, the speed of cooling was under 500 ms concluding that the pumps tested can sufficiently provide the cooling rates required to assist motion in wearable devices. Furthermore, the process developed here provides quantitative support for the optimal selection of initial design parameters and can be translated to designs using different form factors and fluid properties.","PeriodicalId":382522,"journal":{"name":"2018 7th IEEE International Conference on Biomedical Robotics and Biomechatronics (Biorob)","volume":"21 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":"125071985","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.2018.8487204
C. Wai, T. C. Leong, Manik Gujral, Jeff Hung, T. Hui, Kew Kok Wen
This paper describes the Ambidexter, a low cost portable home-based robotic rehabilitation device for training fine motor skills. The Ambidexter is a 3 degree-of-freedom (DOF) robotic device designed for training hand opening/closing, forearm pronation/supination and wrist flexion/extension. The aim of physical/occupational therapy is to help the patients to improve the ability to perform activities in daily life (ADLs). Currently, due to the high cost and complexity, robotic assisted rehabilitation device are only available at rehabilitation center or therapeutic institution with proper supervision by trained therapist. A low-cost home-based robotic device is needed to solve the existing shortage of trained therapists and high number of patients needing upper limbs rehabilitation. Home-based device also enables patients to get more exercises with minimum assistance at the comfort of their home. It reduces the need to travel and the reliance on physical presence of trained therapists. This paper will present the design considerations and criteria adopted with the aim to reduce cost while maintaining the functionality and effectiveness of the robotic device.
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