Posts Tagged robots
[Conference Paper] HandMATE: Wearable Robotic Hand Exoskeleton and Integrated Android App for At Home Stroke Rehabilitation – Full Text
We have developed HandMATE (Hand Movement Assisting Therapy Exoskeleton); a wearable motorized hand exoskeleton for home-based movement therapy following stroke. Each finger and the thumb is powered by a linear actuator which provides flexion and extension assistance. Force sensitive resistors integrated into the design measure grasp and extension initiation force. An assistive therapy mode is based on an admittance control strategy. We evaluated our control system via subject and bench testing. Errors during a grip force tracking task while using the HandMATE were minimal (<1%) and comparable to unassisted healthy hand performance. We also outline a dedicated app we have developed for optimal use of HandMATE at home. The exoskeleton communicates wirelessly with an Android tablet which features guided exercises, therapeutic games and performance feedback. We surveyed 5 chronic stroke patients who used the HandMATE device to further evaluate our system, receiving positive feedback on the exoskeleton and integrated app.
Stroke is the leading cause of severe long-term disability in the US . The probability of regaining functional use of the impaired upper extremity is low . At 6 months post stroke, 62% of survivors failed to achieve some dexterity . Such impairments can inhibit the individual’s ability to perform activities of daily living (ADL). Subsequently, upper limb rehabilitation recovery to improve ADL is one of the main self-reported goals of stroke survivors .
Outpatient rehabilitation is recommended for survivors that have been discharged from inpatient rehabilitative services . However, outpatient rehabilitation in general is largely underutilized, with only 35.5% of stroke survivors using services . Factors inhibiting outpatient therapy include cost, lack of resources and transportation. Wearable robotics that enable home-based therapy have the potential to overcome these barriers. They provide assistive movement forces which enable task-specific training in real-life situations that patients are often unable to practice without a clinician. See  for wearable hand robots for rehabilitation review.
At home therapy is not without its limitations. The inability to motivate oneself and fatigue are the most common reported factors resulting in failure to adhere to home based exercise programs for stroke recovery . While wearable robotics can reduce fatigue during exercise, it does not directly address lack of motivation. Research has shown incorporating games into home therapy can encourage compliance . Zondervan et al. showed that use of an instrumented sensor glove, named the MusicGlove, improved self-reported use and quality of movement, greater than convention at home exercises . Other studies showed increased motivation to complete the therapeutic exercises and optimized movement when the user is given feedback of their performance via the Microsoft Kinect . Wearable robotic systems that offer feedback and gaming capability may optimize at home stroke therapy.
Such a system was presented by Nijenhuis et al. in which stroke survivors showed motor improvements after completing a 6 week self-administered training program comprised of a dynamic hand orthosis and gaming environment . However, the hand device was passive, assisting only with extension, which limits the range of stroke survivors who could utilize such a system. Research groups have proposed combining their powered take-home wearable hand devices with custom integrated gaming systems , or guided exercises ; however, they have yet to conduct clinical trials. Notably, Ghassemi et al., have developed an integrated multi-user VR system to use with their X-Glove actuated orthosis, which will allow for client-therapist sessions without the patient having to travel .
Tablets are relatively inexpensive, portable, and straight forward to use, with 47% of internet users globally already owning one . Furthermore, a recent study demonstrated the success of a tablet based at home exercise program in improving the recovery of stroke survivors . Notably, the study evaluated the accessibility of tablets, concluding every participant used the tablet successfully. Therefore a wearable powered hand robot with a dedicated tablet app which will provide functional games, task-specific guided exercises and feedback of movement, could optimize at home stroke therapy.
The goal of this project was to create a wearable robotic exoskeleton that enables repetitive practice of task-specific and goal orientated movements, which translates into improvements in ADL. Furthermore, for maximum use and successful integration into home-based rehabilitation, we aimed to create an Android application compatible with the robotic exoskeleton.
To meet these goals, the following design objectives were established: 1) Assistance with finger flex/extension. 2) Assistance with thumb carpometacarpal (CMC) add/abduction and thumb metacarpophalangeal (MCP) flex/extension. 3) Independent assistive control of each finger and thumb. 4) Portable for at home use, meaning the device has to be lightweight and wireless. 5) Relatively affordable. 6) Integrated with android tablet app. Specific design goals for the app included: 1) Easy to use. 2) Allow the user to control the exoskeletons assistance mode through the app. 3) Records the user’s data and prompts the user via notifications to complete the allocated daily or weekly recommended activity time.
In this paper we will evaluate if the proposed device and app goals have been achieved via bench and subject testing.
The HandMATE device (Fig. 1) builds upon the Hand Spring Operated Movement Enhancer (HandSOME) devices , , . The HandSOME devices are non-motorized wearable exoskeletons that assists stroke patients with finger and thumb extension movements. The HandSOME I device assists with gross whole hand opening movements, while the HandSOME II assists isolated extension movement of 15 finger and thumb degrees of freedom (DOF), allowing performance of various grip patterns used in ADL. While both devices have been shown to significantly increase range of motion (ROM) and functional ability in chronic stroke subjects ,, the HandSOME devices only assist with extension movements and require enough flexion activity to overcome the assistance of the extension springs. As many stroke patients also suffer finger and thumb flexion weakness, we decided to build upon the work of the high DOF HandSOME II and additionally utilize power actuation so we can assist with both flexion and extension movements.
[Abstract] A 5-Degrees-of-Freedom Lightweight Elbow-Wrist Exoskeleton for Forearm Fine-Motion Rehabilitation
[Abstract] Robotic Exoskeleton for Wrist and Fingers Joint in Post-Stroke Neuro-Rehabilitation for Low-Resource Settings
[Abstract] An Elbow Exoskeleton for Upper Limb Rehabilitation With Series Elastic Actuator and Cable-Driven Differential
[ARTICLE] A Feasibility Study of SSVEP-Based Passive Training on an Ankle Rehabilitation Robot – Full Text
Objective. This study aims to establish a steady-state visual evoked potential- (SSVEP-) based passive training protocol on an ankle rehabilitation robot and validate its feasibility. Method. This paper combines SSVEP signals and the virtual reality circumstance through constructing information transmission loops between brains and ankle robots. The robot can judge motion intentions of subjects and trigger the training when subjects pay their attention on one of the four flickering circles. The virtual reality training circumstance provides real-time visual feedback of ankle rotation. Result. All five subjects succeeded in conducting ankle training based on the SSVEP-triggered training strategy following their motion intentions. The lowest success rate is 80%, and the highest one is 100%. The lowest information transfer rate (ITR) is 11.5 bits/min when the biggest one of the robots for this proposed training is set as 24 bits/min. Conclusion. The proposed training strategy is feasible and promising to be combined with a robot for ankle rehabilitation. Future work will focus on adopting more advanced data process techniques to improve the reliability of intention detection and investigating how patients respond to such a training strategy.
Stroke is one of the main root causes leading patients unable to comfortably control their muscles and bodies in the daily living, and even lose the ability [1–3]. The ability of body controlling is inversely proportional to the distance between brains and limbs, which means that the longer the distance is, the lower the ability is . Motor function of injured ankles will be recovered more difficult than one of the hands with a similar disability.
For early stage rehabilitation of injured ankles, if without sufficient rotations, ankle joints could gradually become stiff, and finally, foot drop will be generated [5, 6]. In order to avoid being stiff, muscle stretching and joint rotating are regarded as one of the important methods in traditional therapy of injured ankle joints. Traditional physical therapy is usually operated manually by therapists. It has a unique advantage, which therapists can observe real-time feedback from patients through their body reaction and communication and thus adjust the process accordingly. However, it also has several limitations: (1) therapists can feel weary for long-time operation; (2) operating strength cannot be kept uniformly during the whole process; (3) mental state of therapists is one of the key factors to affect therapy effect .
In order to release manpower and address those limitations, robots have been invented to substitute partial functions of traditional therapy [8, 9]. For ankle rehabilitation, there are two kinds of robots invented, one of which is platform-based robots, and the other is wearable devices . When training on platform-based robots, subjects are normally in a sitting position to train their physical function of muscle stretching and joint rotating [8, 10]. When training on wearable ankle robots, subjects are required to be in a standing position to improve their ability on walking . Therefore, platform-based robots can provide better rehabilitation for subjects with weak motion ability of ankle joints, while targeted subjects of wearable ankle robots are those whose motion ability of ankle joints is strong enough to walk, but gait needs to be rebuilt and improved further recovery .
Passive training is one of the basic functions of platform-based robots. Different with common passive stretching with constant speed, Zhang et al.  proposed an intelligent passive stretching strategy in ankle dorsiflexion/plantarflexion (DF/PF) for safety. During intelligent passive stretching, rotating speed of the robot was inversely proportional to resistance torque. As soon as predefined maximum resistance torque was reached, ankle joints would be held at the extreme position for a period of time to allow stress relaxation. For robot-assisted passive ankle training, subjects are requested to keep relaxed to follow up trajectories of robots [3, 10]. After experiencing passive training, physic function of ankle joints can be kept to a certain degree and foot drop can be alleviated correspondingly [5, 8, 12].
Active training is another function of platform-based robots, where subjects are requested to actuate robots to track targets by allowing the foot to follow visual or auditory instructions [1, 10, 13, 14]. Visual reality circumstance has been widely applied in robot-assisted active ankle training. Girone et al.  proposed a virtual reality exercise library on the Rutgers Ankle. Subjects could conduct simulation exercise of strength, flexibility, and balance with haptic and visual feedback. Burdea et al.  proposed rehabilitation games including the airplane game and breakout 3D game. Michmizos et al.  proposed three goal-directed serious games especially for children. In this study, visual reality circumstance is set as a game of whack-a-mole, which four hamsters are arranged in four directions as targets, and a hammer is initially located in the center as the movable cursor. The vertical trajectory of hammer is projected to DF/PF, while the horizontal one is corresponded to inversion/eversion (INV/EV).
For passive training, subjects do not need to exert active effort, and thus few information transmission loops between brains and ankles exist . A prerequisite of conducting active training is that subjects should have enough motion ability of ankle joints to trigger robots . Therefore, for subjects whose motion intentions of ankle joints cannot be detected by built-in force sensors of robots, solving the problem of how they can actively conduct ankle training is a big challenge. This study aims to construct an information transmission loop between brains and ankle robots and enable subjects with weak motion ability of ankle joints to actively conduct robot-assisted ankle training.
When subjects focus their attention on a flickering source with frequency above 6 Hz, electroencephalography (EEG) signals originated from their visual cortex are named SSVEP, spectrum of which shows peak at the flickering frequency and its harmonics . SSVEP signals have been extracted and applied in many fields, such as controlling the robotic wheelchair , the humanoid robot navigation [20, 21], the semiautonomous mobile robotic car operation , and the artificial upper limb .
In this study, SSVEP signals are introduced and used for passive training on an ankle rehabilitation robot, in which motion intentions of subjects can be extracted to trigger related passive training. Four flickering circles with the diameter of 22 mm are arranged in four directions. Flickering frequencies are set as 10 Hz for the upper, 12 Hz for the bottom, 8.6 Hz for the left, and 15 Hz for the right . For subjects, gazing at the upper flickering circle represents the motion intention for DF, the bottom for PF, the left for INV, and the right for EV.
To enable subjects with weak motion ability of ankle joints to conduct motion intention-directed passive training, this study develops a SSVEP-based passive training strategy through combining SSVEP signals and virtual reality circumstance on an ankle robot. To verify its feasibility, this study recruited five healthy subjects for preliminary evaluation.
2.1. Ankle Rehabilitation Robot
The ankle rehabilitation robot applied in this study is an improved version of the one used in  by adding adjustable robot structure and was briefly introduced as in Figure 1(a). The footplate of the ankle robot could move with three degrees of freedom, which are corresponding to ankle DF/PF, INV/EV, and adduction/abduction (AA). The robot is actuated in parallel by four FFMs (FESTO DMSP-20-400N), pressure control of which is regulated by four proportional pressure regulators (FESTO VPPM-6L-L-1-G18-0L6H). Three magnetic rotary encoders (AMS AS5048A) are installed along each axis to measure angular positions forming a three-dimensional coordinate system of the footplate. Four single-axis load cells (FUTEK LCM 300) are installed to measure contraction forces generated by FFMs. A six-axis load cell (SRI M3715C) is installed below the footplate to measure interaction forces and torques between human feet and the footplate.[…]
[Abstract + References] Self-paced movement intention recognition from EEG signals during upper limb robot-assisted rehabilitation
[Abstract] Decoupling Finger Joint Motion in an Exoskeletal Hand: A Design for Robot-assisted Rehabilitation
In this study, a cable-driven exoskeleton device is developed for stroke patients to enable them to perform passive range of motion exercises and teleoperation rehabilitation of their impaired hands. Each exoskeleton finger is controlled by an actuator via two cables. The motions between the metacarpophalangeal and distal/proximal interphalangeal joints are decoupled, through which the movement pattern is analogous to that observed in the human hand. A dynamic model based on the Lagrange method is derived to estimate how cable tension varies with the angular position of the finger joints. Two discernable phases are observed, each of which reflects the motion of the metacarpophalangeal and distal/proximal interphalangeal joints. The tension profiles of exoskeleton fingers predicted by the Lagrange model are verified through a mechatronic integrated platform. The model can precisely estimate the tensions at different movement velocities, and it shows that the characteristics of two independent phases remain the same even for a variety of movement velocities. The feasibility for measuring resistance when manipulating a patient’s finger is demonstrated in human experiments. Specifically, the net force required to move a subject’s finger joints can be accounted for by the Lagrange model.
[Abstract] The wearable hand robot: supporting impaired hand function in activities of daily living and rehabilitation
New developments, based on the concept of wearable soft-robotic devices, make it possible to support impaired hand function during the performance of daily activities and intensive task-specific training. The ironHand and HandinMind systems are examples of such novel wearable soft-robotic systems that have been developed in the ironHand and HandinMind projects. Both systems are developed to provide grip support during a wide range of daily activities. The ironHand system consists of a 3-finger wearable soft-robotic glove, tailored to older adults with a variety of physical age-related hand function limitations. The HandinMind system consists of a 5-finger wearable soft-robotic glove, dedicated towards application in stroke. In both cases, the wearable soft-robotic system could be connected to a computer with custom software to train specific aspects of hand function in a motivating game-like environment with multiple levels of difficulty. By adding the game environment, an assistive device is transformed into a dedicated training device.