Search

There is an increasing need for patients affected by cerebrovascular diseases to receive continuous and proper rehabilitation support in his/her home. To this end, many robotic rehabilitation support devices for finger therapy have been proposed. However, there has been no consideration for treatment of “fingertips” with these finger therapy support devices. An active touch uses fingertip to discriminate the object in touch. It uses both sensational and motor control nerve systems. Therefore, there is a possibility that the active touch will improve the activities of a spinal cord, and therefore contribute to improve the effectiveness of a finger rehabilitation. A new robotic device that includes the active touch task with finger movements will provide an ideal home rehabilitation device. In this paper, we show that an active touch for pattern discrimination task will improve the activities of spinal cord. The measurement was performed by using F-waves and M-waves given by the evoked-EMG signals applied to healthy subjects. By the experiments, it was shown that the activity is significantly high for the cases of performing active touch tasks. We would like to utilize the results of this study for an exploitation of a robotic finger functional treatment machine used at home., doi: 10.1109/BMEiCON.2015.7399527

As the number of patients suffering from cerebrovascular disease (CVD) increases, it is desired that the patients will be able to receive proper rehabilitation at home without specialists' help. For this, we have developed a finger-dedicated robotic rehabilitation support device that is compact enough to be used at home. The device induces voluntary muscular contraction at a finger by the help of power assisted finger lifter. The lifter is a simple lever on which the patient places his/her finger. Although the mechanism is simple to be used at home, there is a question whether applying only a finger-dedicated rehabilitation is sufficient to improve the quality of life of a patient including recovery of upper limb functions. To answer this question, we conducted experiments that show skill transfer from finger to elbow. In the experiments, subjects are asked to train to produce a specified force at their fingers. After training, it was shown that the subjects are able to perform the same skill by using elbow, where no training was performed. The results support that the finger-dedicated rehabilitation would be appropriate as a target for a robotic home rehabilitation device., doi: 10.18178/ijmerr.5.4.288-294

Home rehabilitation is much in need in ageing societies. Especially, for hemiplegia patients who have paralysis at fingers, it is known that long continuous rehabilitation is effective for recovery. To automate home rehabilitation without the help of a medical specialist, it is desirable not only to provide a rehabilitation procedure but also to give the condition of the paralysis of the patient. In our previous studies, we proposed a robotic device to foster separative voluntary finger lift movement. The device mounts pressure sensors at each finger to monitor the degree of unwanted finger movements. However, it is not clear which is an effective way to measure the condition of paralysis by these finger pressure time series data. In this paper, we propose a new measurement method that is based on the “coordinated movement” of fingers. A patient is asked to perform 4 tasks: pinch2 (pinch movement by index finger and thumb), pinch3 (pinch movement by index, middle and thumb), grasp2 (grasp movement by the ring and little finger), and grasp3 (grasp movement by the middle, ring, and little finger). All pressure time series of finger movements are quantified their dissimilarities with data of standard healthy subjects. We found that the grasp3 has the largest correspondence relation with the degree of paralysis.

Many hemiplegic patients, especially those having paralysis at hand, need continuous rehabilitation after leaving hospitals. To support rehabilitation at home, we have developed a robotic device which provides automated rehabilitation and evaluation of hand motor function recovery. The device monitors the patient's finger movements during voluntary finger tasks. Our idea is to calculate the extent of recovery by asking a patient to perform a specific voluntary movement (task) and calculating the dissimilarity between a typical healthy person's movement and that measured by this device. In our previous studies, we only clarified the evaluation methods for a single task. This paper proposes the integration of these values into a single measurement value by using binary logistic regression. This integration provides the patient with a probability of being classified as a patient and a healthy subject. A home user should easily understand the value. The experimental results show that the integrated evaluation has higher performance over the single task evaluation.

To realise an automated self-rehabilitation at home, it is necessary to provide proper feedback on the status of the recovery of the patient. In a simple finger rehabilitation, a fingertip force monitor is used to detect undesired and paralyzed movement, which is used to determine the degree of recovery. In contrast to the fingertip force, the balance of flexion and extension of finger muscles is another essential feature of the paralysis. However, simultaneous monitoring of flexor and extensor is impossible by using a single fingertip pressure sensor. Usually, EMG is used to monitor individual muscular activity. However, inexperienced home user will not be able to deal with electrodes properly. In this paper, we propose a device to monitor flexion and extension by a unit which is easy to handle. Also, we propose a method to derive a degree of recovery using the signals collected from this device. The results by data collected from healthy and stroke patients show the potential effectiveness of our method.