Effects of End-effector Type Robot Assisted Gait Therapy on Gait Pattern and Energy Consumption in Chronic Post-stroke Hemiplegic Patients
Effects of End-effector Type Robot Assisted Gait Therapy on Gait Pattern and Energy Consumption in Chronic Post-stroke Hemiplegic Patients
Restoration of gait independence in stroke patients is one of the most important goals of rehabilitation therapy, and gait rehabilitation is one of the most important treatments in the treatment of stroke because it is a major factor affecting rehabilitation after stroke. In the rehabilitation of patients with post - stroke walking disorders, previous physical therapy was mainly manual therapy using therapist 's physical effort and walking training with walking aids. In recent years, however, emphasis has been placed on therapies based on motor learning concepts, which allow the patient to intensively train the exercise as closely as possible to the ultimate goal.
The robot used for walking rehabilitation includes exoskeleton walking robot such as Lokomat® (Hocoma AG, Switzerland), Walkbot-G® (P & S Mechanics, Korea), MorningWalk® (Curexo, Korea) According to the Systematic Review, which compares two types of robot-assisted gait treatment divided into end-effector type, which is not an exoskeletal type such as System® (Rehatech, Switzerland) It has been reported that the percentage of patients who were able to walk independently when treated with a robot was higher than that of an exoskeleton-type robot.
In this regard, in terms of acquisition of independent gait, studies on the therapeutic effect of the exoskeleton-type robot and the end-effector-type robot before and after the gait therapy were continuously performed, but 80% of the patients obtained independent gait, Despite the fact that many of these patients have abnormal walking, research has not yet been conducted. In previous studies, there was a statistically significant improvement in parameters of Gait speed, Cadence, and step length when compared with spatiotemporal parameters in training using exoskeleton robots for stroke patients. In another study, Gait speed and Cadence did not show a statistically significant improvement, and the effect on Gait speed and Cadence is still unknown. However, unlike exoskeletal robots, end-effector robotic gait training has been reported to improve Gait speed in most studies compared to conventional gait training. In addition, Cadence, Temporal symmetry ratio, Single, an improved side stride length, an improvement in the symmetry index of stance phase, and an improvement in Gait endurance.
In this way, the end effector type robot walking training is more likely to improve walking quality than the exoskeleton type robot. The end-effector type robot, which is different from the exoskeleton type, reproduces the gait using the ankle joint to induce the movement of the knee joint and the hip joint. Therefore, it is possible to control the ankle joint, which is essential for improving the gait pattern. It is considered that the end effector type robot which can control the ankle joint is more likely to induce the improvement of the gait pattern than the existing exoskeleton type robot because it shows limitations in reproducing the ankle rocker motion.
There are few studies on kinematic, kinematic, and energy consumption after robot training, so it is urgent to study this part. In a small retrospective open-label study, the results of spatiotemporal parameters and kinetic and kinematic analyzes of patients with chronic stroke in patients who underwent gait using an end-effector robot were compared with those of Gait speed, Cadence , Stride time, and stride speed, improvement of hip extension in kinematic analysis as a whole, and reduction of anterior tilting in pelvis. This suggests that robot-assisted gait training may improve the kinematic index Randomized Controlled Trial design is a systematic study.
In addition, it is important to evaluate the energy expenditure and cardiorespiratory load of robot-assisted walking therapy for the rehabilitation of patients at risk of cardiovascular disease and stroke patients with impaired cardiopulmonary function. The purpose of gait therapy in stroke patients is to improve the efficiency of energy consumption by calibrating patterns of gait and asymmetry of gait movements. This is also an important issue for gait researchers.
The authors reported that when using an end-effector type robot, the oxygen consumption was statistically significantly lower during the robot-assisted walking compared to when the robot was not assisted by the robot. During the walking with the exoskeleton type robot, and when compared to OTW (Overground treadmill walking) during ATW, there was a statistically significant decrease in mean oxygen consumption There was a report. However, previous researches did not compare the pre - treatment and post - treatment, but there is no report on the possibility of improvement of oxygen consumption after robot - assisted gait training.
In this study, we divided the patients into two groups. One group was treated with 6-week gait training using an end-effector type robot-assisted walking device and the other group was treated with gait therapy for the same period of time. Six weeks after the end of the treatment, three-dimensional motion analysis, foot pressure analysis and energy consumption analysis were performed to obtain robot assisted training in terms of space time index, kinematics, kinematic index, dynamic EMG activation pattern, The purpose of this study was to investigate whether the improvement in walking performance and the energy consumption efficiency of walkers are more effective than the conventional walking training group.
the three most natural walking cycles Calculate kinematical index and spatio-temporal index according to each gait cycle
Dynamic EMG analysis Dynamic EMG was performed by attaching surface EMG to the skin using Medial GCM, Tibialis Anterior, Vastus Medialis, Rectus Femoris, Medial Hamstring, and Gluteus Maximus of both lower limbs using a wireless Delsys Trigno Sensor System (Delsys Inc, USA) Measure the signal and convert it to Root mean square (RMS). (Figure 5) EMG signal sampling rate: 2000 samples / sec Filter: EMG signal bandwidth 20- 450 Hz Surface electrode: Parallel bar electrode
The measured EMG signals are obtained by measuring the duration and the period of activity according to the walking cycle and analyzing the degree of activation.
2-2. Energy consumption analysis Use K4b2 (COSMED, Italy) as a wearable metabolic system (Fig. 6) Measure O2 cost [ml / (m / kg)] and O2 rate (ml / min / kg) The walking distance was measured by walking with the self-selected gait velocity while wearing K4b2 (COSMED, Italy) for 5 minutes in total. The walking distance was measured for 3 minutes except the first 1 minute and the last 1 minute of oxygen consumption data for 5 minutes Using O2 rate and O2 cost
2-3. Foot pressure analysis The foot pressure was measured using a F-Scan system (Tekscan, USA) with a 0.16-mm thick, 980 force-sensing resistors (3.88 sensors per centimeter square) After inserting the pressure insoles, calibrate them according to the Tekscan user manual (Tekscan Research Software User Manual version 6.7 Rev. D, 2003) and measure them and analyze them as follows.
2-4. Fugl-Meyer Assessment(FMA) for Lower extremities 2-5. 10m walking test 2-6. Berg balance scale(BBS) 2-7. Timed up and go test(TUG) 2-8. Functional Ambulation Category(FAC) 2-9. Modified Ashworth Scale(MAS) 2-10. Rivermead Mobility Index(RMI) 2-11. Functional independence measure(FIM)
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