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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.

We have been proposing a robotic finger rehabilitation support device for hemiplegic patients that can be used at home. This device instructs a patient to lift a finger voluntarily and provides assistance when the patient is impossible to lift. In previous studies, we have shown an automated evaluation method which monitors the level of involuntary finger movement. However, the detailed procedure of finger rehabilitation has not been clarified. In this paper, we show a practical procedure of finger rehabilitation as well as a hardware design of the device. We also discuss safety issues during finger lift assistance. Our design limits the speed of finger lift so that it avoids unwanted contraction of finger muscles by stretch reflex. Also, the angle of finger lift is limited in our design so that it will not exceed the maximum excursion of an MP joint.

Continuous hand rehabilitation after discharge is important for hemiplegic patients to regain an independent finger movement. However, most patients cannot rehabilitate by themselves without therapists. For this problem, robotic rehabilitation has been investigated to support patients even at home. Most of the programs performed by these robots are focusing on the assistance for voluntary movement. However, the approach to the voluntary movement is not enough for regaining dexterous movement. Voluntary suppression of body parts that should not move is important. However, previous studies focusing on voluntary suppression are few. In this paper, we show a detailed program for voluntary suppression rehabilitation. The program is performed by our robotic finger rehabilitation device aiming at home use. In this program, a patient is requested to flex and extend an index finger independently. During moving, individual pressure sensors monitor the other fingers. If the device detects unnecessary movements such as compensatory movement at some fingers, the patient is notified that unnecessary movements are found there. The detection is based on $3\sigma$ range of healthy subject’s finger pressure data which was constructed by using Gaussian Process Regression. Through experiments with hemiplegic patients, we have shown that the frequency of deviation of patients’ data from $3\sigma$ range of healthy subjects decreases according to the degree of recovery.

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.

Paralysis of fingers, which is caused by Hemiplegia, is difficult to recover. Patients often forced to leave hospital with paralysis remaining at hand. By this, a continuous rehabilitation at home is needed. However, it is difficult to carry out finger rehabilitation without help of therapists. To this end, we have been proposing an automated finger rehabilitation device which realizes home rehabilitation. A patient is asked by device to lift a finger, and the device measures whether undesirable movements are found on the other fingers by pressure sensors. To monitor an involuntary movement, it is necessary to evaluate the degree of the patient's condition of recovery. For this, we proposed a quantification method in our previous study. The method is based on the hypothesis that a patient is regarded as making recovery if his/her movement gets close to that of a healthy person. However, we consider only four fingers (index, middle, ring, little) are used to evaluate the degree of recovery because the thumb is different from the other finger in an anatomical structure. In this paper, we show a new recovery evaluation method that involves the sensor signals of all 5 fingers. We explain two possible evaluation methods: one is the model less simple integration method, and another is an integration by Generalized Linear Model (GLM). Comparing these methods, we conclude that the integration method by GLM provides a good scalar measurement of recovery, which was validated by the experiments conducted with patients who were previously evaluated by clinical scale.

In current health care system, it is difficult to have sufficiently long period of rehabilitation in hospital. Therefore, an automated rehabilitation support at home is needed. To this end, we developed a simple robotic device that will support index finger rehabilitation for patients who suffer from aftereffects of hemiplegia. The purpose of the device is to foster voluntary movement of index finger. It has pressure sensors for all fingers, and a power assisted index finger lift mechanism. The power assist lift supports to lift the patient's finger if the lift by voluntary movement is insufficient. The pressure sensors are used to monitor the simultaneous movement of all fingers. By this, the system checks if undesirable movements such as synergetic movements are found. The information is integrated to provide a quantitative measure of recovery. Patients are able to monitor his/her condition and continue rehabilitation as needed. Our device is simple and compact so that it should be placed on a desk at home. The device is used by placing the patient's hand on keyboards. This allows the patients to see his/her fingers during rehabilitation and is expected to enhance the effect of rehabilitation by seeing the result of voluntary movement at his/her real fingers. This paper shows these concepts and the hardware design of this device in detail. This paper also discusses the results of the measurement of hemiplegia patients and the healthy subjects by this device. The results show that the explicit difference was seen between the patients and the healthy subjects, which shows the effectiveness of this design.

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.

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