Soft robotic glove for hand rehabilitation and task specific training

Polygerinos, Panagiotis; Galloway, Kevin C.; Savage, Emily; Herman, Maxwell; Donnell, Kathleen O; Walsh, Conor J.

doi:10.1109/icra.2015.7139597

Cited by 216 publications

(130 citation statements)

References 21 publications

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“…fluid pressurization). Several research groups have developed wearable soft exoskeletons by combining soft gloves with soft elastomeric actuators [14][15][16][17]. These devices provide a greater chance of user acceptance due to their compactness, affordability and customizability [18,19].…”

Section: Introductionmentioning

confidence: 99%

A fabric-regulated soft robotic glove with user intent detection using EMG and RFID for hand assistive application

Yap

Ang

Lim

et al. 2016

2016 IEEE International Conference on Robotics and Automation (ICRA)

103

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This paper presents a soft robotic glove designed to assist individuals with functional grasp pathologies in performing activities of daily living. The glove utilizes soft fabric-regulated pneumatic actuators that are low-profile and require lower pressure than previously developed actuators. They are able to support fingers and thumb motions during hand closure. Upon pressurization, the actuators are able to generate sufficient force to assist in hand closing and grasping during different manipulation tasks. In this work, experiments were conducted to evaluate the performances of the actuators as well as the glove in terms of its kinetic and kinematic assistance on a healthy participant. Additionally, surface electromyography and radio-frequency identification techniques were adopted to detect user intent to activate or deactivate the glove. Lastly, we present preliminary results of a healthy participant performing different manipulation tasks with the soft robotic glove controlled by surface electromyography and radio-frequency identification techniques.

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Section: Introductionmentioning

confidence: 99%

A fabric-regulated soft robotic glove with user intent detection using EMG and RFID for hand assistive application

Yap

Ang

Lim

et al. 2016

2016 IEEE International Conference on Robotics and Automation (ICRA)

103

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“…Therefore, EMG signals are often used as the switch signal in low-level strategies in order to develop it control algorithm into high-level control strategies. For instance, Harvard University Hand I and II [ [3,18,19]], they adopted the EMG signals as the control switch signal to implement the human intention control scheme. The National University of Okayama also uses the EMG signal to realize their intelligent control system.…”

Section: ) Control Signalsmentioning

confidence: 99%

Soft Robotic Hand Exoskeleton Systems: Review and Challenges Surrounding the Technology

Meng¹,

Xiang²,

Yu³

2017

Proceedings of the 2017 2nd International Conference on Electrical, Automation and Mechanical Engineering (EAME 2017)

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Abstract-Soft robotic hand exoskeleton systems are taken into account to solve the problems happened in the hand rehabilitation, such as long-term training for stroke patients and heavy workload for physicians. These systems are one of the hot issues in recent robotic research because that they are not only applied in rehabilitation but also in assisting the human beings. With the difference of the rigid robotic exoskeleton hand systems, the soft exoskeleton hand systems should be designed together with the human hand since they directly interact with the patients. This paper briefly reviews different robotic hand exoskeleton systems from two aspects: mechanical design and control system. The existing soft hand exoskeleton technologies and challenges are discussed in this paper.

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“…The use of orthotic devices in such situations is an effective method of returning a modicum of motor control to patients. Multiple such orthotic devices have been developed, including, but not limited to, gloves (Radder et al, 2015), (Adler, 2016), braces (Linn et al, 2012), and soft-muscle pneumatic tubes (Yanchev, 2015;Polygerinos, 2015). However, many of these devices, must be specially constructed per each patient, and requires multiple rounds of testing and data acquisition before completion.…”

Section: Relevant Background and Definitionsmentioning

confidence: 99%

Musculoskeletal Modeling of the Hand and Contact Object in Modelica

Swaminathan¹,

Andreasson²

2017

Linköping Electronic Conference Proceedings

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The paper's primary goal is to develop a mathematical model that could be used towards the development and improvement of orthotic assist gloves. The model is constructed using component based modeling in the object-oriented declarative language Modelica, specifically the MultiBody Modelica library. Multiple hand models currently do exist; however, they are mainly causal, and require separate development and validation of mathematical solvers before use. By using Modelica, the model is constructed from the system's physical equations, thereby relieving issues regarding validity of the model's computational equations; the acausality inherent in Modelica allows for model development that more closely mirrors relations in the physical world. The model is scoped to be able to model the kinematics and dynamics of the hand when grasping a spherical object -both bone structure and muscle geometry and actuation are simplifications based off anatomy literature. The contact model is developed as a separate component from the hand system. The main design goal of the contact model is to represent the characteristics of a relatively rigid object that still maintains a degree of friction and pliability on the surface layer.The main two grasps tested in the paper are the prehensile and precision grasps (powerful and dexterous grasps). The muscle actuation profiles per each finger are adjusted until the desired dynamic profile is achieved for each type of grasp. The main data points of interests are the joint angles and contact forces for each finger. Further verification of the model is done using the animation automatically generated by the tool. Simulation testing results indicate that the model can successfully simulate contractions at all levels of abstraction of the hand's components (basic bone-joint components, finger components, and the overall hand system). The results also indicate that both prehensile and precision grasps are possible, given appropriate muscle actuation and finger orientation parameter values.

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Soft robotic glove for hand rehabilitation and task specific training

Cited by 216 publications

References 21 publications

A fabric-regulated soft robotic glove with user intent detection using EMG and RFID for hand assistive application

A fabric-regulated soft robotic glove with user intent detection using EMG and RFID for hand assistive application

Soft Robotic Hand Exoskeleton Systems: Review and Challenges Surrounding the Technology

Musculoskeletal Modeling of the Hand and Contact Object in Modelica

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