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.
Improvement in hand function to promote functional recovery is one of the major goals of stroke rehabilitation. This paper introduces a newly developed exoskeleton for hand rehabilitation with a user-centered design concept, which integrates the requirements of practical use, mechanical structure and control system. The paper also evaluated the function with two prototypes in a local hospital. Results of functional evaluation showed that significant improvements were found in ARAT (P=0.014), WMFT (P=0.020) and FMA_WH (P=0.021). Increase in the mean values of FMA_SE was observed but without significant difference (P=0.071). The improvement in ARAT score reflects the motor recovery in hand and finger functions. The increased FMA scores suggest there is motor improvement in the whole upper limb, and especially in the hand after the training. The product met patients’ requirements and has practical significance. It is portable, cost effective, easy to use and supports multiple control modes to adapt to different rehabilitation phases.
via Design and development of a portable exoskeleton for hand rehabilitation – IEEE Journals & Magazine
Research and development efforts into small upper limb rehabilitation robots for home-based rehabilitation have been made in order to reduce the patient burden associated with making visits to the hospital. However, currently, there are only a few small upper limb rehabilitation robots capable of providing training that is tailored to account for the differences in individual patients. This is because many robots use omni wheels for their movement mechanism, thus causing problems when measuring patient motor function because it is not possible to accurately estimate the position. To solve this problem, in this study, we propose a new small upper limb rehabilitation robot that switches the driving unit from an omni wheel to an omnidirectional drive gear mechanism, as a mechanism that does not cause slips. Although an omnidirectional drive gear poses problems in terms of machining difficulty and weight, these problems can be solved by using a 3D printer. We show that position errors in small upper limb rehabilitation robots are greatly reduced by introducing a gear mechanism.
via Desktop upper limb rehabilitation robot using omnidirectional drive gear – IEEE Conference Publication
In this study, we have been developing a rehabilitation system that combining a motor and sensory function recovery device and a measuring device for a hand sensory. These devices are purposely developed for paralyzed patients. The rehabilitation system was named HSRS (Hand Sensory Rehabilitation system) and it’s consists of a training device hand sensory function, a computer to control the device, and an external monitor displays an obtained data from sensors. The training device is able to applycontinuous mechanical stimulation to the hand of a user by grasping the device probe. On the monitor, the self-made Graphical User Interface (GUI) is displayed. An operator instructs the user to match the target value to the measured value in the training. When the user operates the switches, and the device measures the point that contacted with the probe. We did two experiments by using these devices. One is stimulus evaluation experiment. In this experiment, three frequencies (30 [Hz], 60[Hz], 100[Hz]) were given to the human hand and we verified which frequency was most effective. Using a slide caliper and the device to measure sensory function, we evaluated the difference in sensory degree of each healthy subject between before and after using the training device. As a result, we found 60[Hz] is the most effective frequency. In the second experiment, we let a subject follow a target value of GUI. In this experiment, we examined the relation between the sensory function and the motor function and investigated the best evaluation parameter when training the paralyzed patient. As a result, we didn’t get difference in the results when comparing in sports experience. However, we got a particularly big difference of the magnitude of the residual during accelerated and deceleration time of the gripping with other items. Moreover, when comparing those who are good and not good sensory in sensory function measurement experiments, there was a tendency that the subjects with not good sensory have the bigger difference with the target value. We confirmed the utility of the measuring devise and the relationship between motor and sensory function.
via Hand Sensory Rehabilitation System Which Incorporated Visual and Tactile Feedback – IEEE Conference Publication
Previously, we reported a novel bilateral upper-limb rehabilitation system, an adaptive admittance controller and a related bilateral recovery strategy. In this study, we want to get a stronger evidence to verify the robustness of the proposed system, controller and recovery strategy as well as to further investigate the possibility of bilateral trainings for clinical applications. To this end, ten healthy subjects took part in a 60-minute experiment. Trajectories of robots and interaction force were recorded under the proposed bilateral recovery strategy which contained four exercise modes. For mode-l and mode-2, results showed that the trajectories of master and slave robots can catch the reference trajectory very well, and be changed with active interaction force applied by participants. For mode-3 and mode-4, participants finished tasks very well by drawing the ‘square-shaped’ trajectories through their own force. In conclusion, the experimental results were good enough to provide a strong and positive evidence for the proposed system and controller. Moreover, according to the feedbacks from participants, the bilateral recovery strategy can be treated as a new and interesting training as compared to the traditional unilateral training, and could be tested in clinical applications further.
Compared to the traditional manual therapy, the robot involved therapy can alleviate labor-intensive aspects of conventional rehabilitation trainings, and provide precise passive/active repetitive trainings in a sufficiently long timeframe , . In terms of upper-limb rehabilitation trainings, some robotic systems have been developed for bilateral exercises, and figured out a problem that performing most activities of daily living tasks with one-hand is awkward, difficult and time-consuming .
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via A Bilateral Training System for Upper-limb Rehabilitation: A Follow-up Study – IEEE Conference Publication
Objective: Loss of arm function is common in individuals with neurological damage, such as stroke or cerebral palsy. Robotic devices that address muscle strength deficits in a task-specific manner can assist in the recovery of arm function; however, current devices are typically large, bulky, and expensive to be routinely used in the clinic or at home. This study sought to address this issue by developing a portable planar passive rehabilitation robot, PaRRo. Methods: We designed PaRRo with a mechanical layout that incorporated kinematic redundancies to generate forces that directly oppose the user’s movement. Cost-efficient eddy current brakes were used to provide scalable resistances. The lengths of the robot’s linkages were optimized to have a reasonably large workspace for human planar reaching. We then performed theoretical analysis of the robot’s resistive force generating capacity and steerable workspace using MATLAB simulations. We also validated the device by having a subject move the end-effector along different paths at a set velocity using a metronome while simultaneously collecting surface electromyography (EMG) and end-effector forces felt by the user. Results: Results from simulation experiments indicated that the robot was capable of producing sufficient end-effector forces for functional resistance training. We also found the endpoint forces from the user were similar to the theoretical forces expected at any direction of motion. EMG results indicated that the device was capable of providing adjustable resistances based on subjects’ ability levels, as the muscle activation levels scaled with increasing magnet exposures. Conclusion: These results indicate that PaRRo is a feasible approach to provide functional resistance training to the muscles along the upper extremity. Significance: The proposed robotic device could provide a technological breakthrough that will make rehabilitation robots accessible for small outpatient rehabilitation centers and in-home therapy.
via A Portable Passive Rehabilitation Robot for Upper-Extremity Functional Resistance Training – IEEE Journals & Magazine
Soft robotic devices have the potential to be widely used in daily lives for their inherent compliance and adaptability, which result in high safety under unexpected situations. System complexity and requirements are much lower, comparing with conventional rigid-bodied robotic devices, which also result in significantly lower costs. This paper presents a robotic glove by utilizing soft artificial muscles providing redundant degrees of freedom (DOFs) to generate both flexion and extension hand motions for daily grasping and manipulation tasks. Different with the existing devices, to minimize the weight applied to the user’s hands, pneumatic soft actuators were located on the fore arm and drive each finger via cable-transmission mechanisms. This actuation mechanism brings extra adaptability, motion smoothness, and user safety to the system. This design makes wearable robotic gloves more light-weight and user-friendly. Both theoretical and experimental analyses were conducted to explore the mechanical properties of pneumatic soft actuators. In addition, the fingertip trajectories were analyzed using Finite Element Methods, and a series of experiments were conducted evaluating both the technical and practical performances of the proposed glove.
Glove-type wearable robotic devices are developed to assist people with impaired hand functions both in their activities of daily living (ADLs) and in rehabilitation –. Most of such wearable robotic devices generate hand movements with linkage systems actuated by electrical motors which usually are heavy and inconvenient for using. Moreover, because of the human hand variation, most wearable robotic devices require customization in order to fulfill the geometrical fitting requirements between the exoskeleton device and the human hand joints. Approximating the high dexterity of human hands usually requires high complexity in both the mechanical and controller structures of the robotic systems, and hence also results in high costs for most users.
via A soft robotic glove for hand motion assistance – IEEE Conference Publication
After leaving hospital, patients can carry out rehabilitation by using rehabilitation devices. However, they cannot evaluate the recovery by themselves. For this problem, a device which can both carry out the rehabilitation and evaluation of the degree of recovery is required. This paper proposes the method that quantifies the recovery of the paralysis of fingers to evaluate a patient automatically. A finger movement is measured by a pressure sensor on the rehabilitation device we have developed. A measured data is used as a time-series signal, and the recovery of the paralysis is quantified by calculating the dissimilarity between a healthy subject’s signal and the patient’s signal. The results of those dissimilarities are integrated over all finger to be used as a quantitative scale of recovery. From the experiment conducted with hemiplegia patients and healthy subjects, we could trace the process of the recovery by the proposed method.
Source: Quantification method of motor function recovery of fingers by using the device for home rehabilitation – IEEE Conference Publication
Chronic wrist impairment is frequent following stroke and negatively impacts everyday life. Rehabilitation of the dysfunctional limb is possible but requires extensive training and motivation. Wearable training devices might offer new opportunities for rehabilitation. However, few devices are available to train wrist extension even though this movement is highly relevant for many upper limb activities of daily living. As a proof of concept, we developed the eWrist, a wearable one degree-of-freedom powered exoskeleton which supports wrist extension training. Conceptually one might think of an electric bike which provides mechanical support only when the rider moves the pedals, i.e. it enhances motor activity but does not replace it. Stroke patients may not have the ability to produce overt movements, but they might still be able to produce weak muscle activation that can be measured via surface electromyography (sEMG). By combining force and sEMG-based control in an assist-as-needed support strategy, we aim at providing a training device which enhances activity of the wrist extensor muscles in the context of daily life activities, thereby, driving cortical reorganization and recovery. Preliminary results show that the integration of sEMG signals in the control strategy allow for adjustable assistance with respect to a proxy measurement of corticomotor drive.
Source: The eWrist — A wearable wrist exoskeleton with sEMG-based force control for stroke rehabilitation – IEEE Xplore Document
Stroke survivors who experience severe hemipare-sis often cannot completely recover the use of their hand and arm. Many of the rehabilitation devices currently available are designed to increase the functional recovery right after the stroke when, in some cases, biological restoring and plastic reorganization of the central nervous system can take place. However, this is not always the case. Even after extensive therapeutic interventions, the probability of regaining functional use of the impaired hand is low. In this respect, we present a novel robotic system composed of a supernumerary robotic finger and a wearable cutaneous finger interface. The supernumerary finger is used to help grasping objects while the wearable interface provides information about the forces exerted by the robotic finger on the object being held. We carried out two experiments, enrolling 16 healthy subjects and 2 chronic stroke patients. Results showed that using the supernumerary finger greatly improved the grasping capabilities of the subjects. Moreover, providing cutaneous feedback significantly improved the performance of the considered task and was preferred by all subjects.
Source: A soft robotic supernumerary finger and a wearable cutaneous finger interface to compensate the missing grasping capabilities in chronic stroke patients – IEEE Xplore Document