Effects of End-effector Type Robot Assisted Gait Therapy on Gait Pattern and Energy Consumption in Chronic Post-stroke Hemiplegic Patients

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. 1. Medial GCM, Tibialis Anterior, Vastus Medialis, Rectus Femoris, Medial Hamstring, and Gluteus Maximus 2. Starting and ending points of muscle activation cycle 3. Muscle activation duration and RMS integral and peak value 4. The root mean square (RMS) value divided by 16 sections divided by time 5. Comparison between the right side and the left side 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)