• PNF and Movement
• /
• v.17 no.1
• /
• pp.111-118
• /
• 2019

Purpose: This study aimed to investigate the correlation between trunk stabilization muscle activation and the parameters of gait analysis in healthy individuals. Methods: Thirty healthy adults (15 male, 15 female) with no history of lower back pain (LBP) or current musculoskeletal and neurological injuries were studied. Trunk stabilization muscle activation (e.g., external oblique, internal oblique, transverse abdominis, erector spinae) were assessed using surface electromyography. To analyze gait, we measured temporal parameters (e.g., gait velocity, single support phase, double support phase, swing phase, and stance phase) and a spatial parameter (e.g., H-H base of support). Results: A statistically significant correlation was found between the internal oblique, transverse abdominis, and erector spinae muscle activity and gait velocity, single support phase, double support phase, swing phase, and stance phase. No statistically significant correlation was found between the external oblique muscle activity and the gait velocity, single support phase, double support phase, swing phase, and stance phase. No statistically significant correlation was found between the external oblique, internal oblique, transverse abdominis, and erector spinae muscle activity and the spatial parameter. Conclusion: This study demonstrated that a relationship exists between trunk stabilization muscle activation and temporal parameter (i.e., gait velocity, single support phase, double support phase, swing phase, and stance phase) during gait analysis. Therefore, the trunk's stabilizer muscles play an important role in the gait of healthy individuals.

• The Journal of Internal Korean Medicine
• /
• v.37 no.5
• /
• pp.763-769
• /
• 2016

Objective: To confirm the immediate gait improvement in a post-stroke gait disturbance patient equipped with a weighted vest. Methods: We selected a patient who was able to walk without another’s help or with tools. The selected patient had an unstable gait because she had only started an independent gait within the past week, so we thought that a weighted vest could be very helpful for her. We first collected gait parameters using a treadmill gait analysis system while the patient walked on the treadmill without the weighted vest. After a 10-minute break, gait parameters were collected again while the patient walked on the treadmill while wearing the weighted vest. The gait parameters we collected included step length (cm), stance phase (%), swing phase (%), SW/ST, and gait line length (mm). For objective evaluation of gait improvement, we calculated the ratio of gait parameters of the right and left limbs. Results: The gait of the post-stroke patient was more symmetrical when wearing the weighted vest than without the weighted vest. Without the weighted vest, her step length ratio was 0.78, stance phase ratio was 0.88, swing phase ratio was 1.50, SW/ST ratio was 1.70, and gait line length ratio was 0.91. With the weighted vest, her step length ratio was 0.88, stance phase ratio was 0.90, swing phase ratio was 1.38, SW/ST ratio was 1.54, and gait line length ratio was 0.98. No side effects were observed due to the weighted vest.

• Journal of the Korean Society for Precision Engineering
• /
• v.21 no.10
• /
• pp.196-203
• /
• 2004

In this study, a new gait phase detection system using both FSR(Force Sensing Resister) sensors and a gyrosensor was developed to detect various gait patterns. FSR sensors were put in self-designed shoe insoles and a gyrosensor was attached to the posterior aspect of a shoe. An algorithm was also developed to determine eight different gait transitions among four gait phases: heel-strike, foot-flat, heel-off and swing. The developed system was compared with the conventional gait phase detection system using only FSR sensors in various gait experiments such as level walking, fore-foot walking and stair walking. In fore-foot walking and stair walking, the developed system showed much better accuracy and reliability to detect gait phases. The developed gait phase detection system using both FSR sensors and a gyrosensor will be helpful not only to determine pathological gait phases but to apply prosthetics, orthotics and functional electrical stimulation to patients with gait disorders.

• The Journal of Korea Robotics Society
• /
• v.18 no.2
• /
• pp.182-188
• /
• 2023

Due to the acceleration of an aging society, the need for lower limb exoskeletons to assist gait is increasing. And for use in daily life, it is essential to have technology that can accurately estimate gait phase even in the walking environment and walking speed of the wearer that changes frequently. In this paper, we implement an LSTM-based gait phase estimation learning model by collecting gait data according to changes in gait speed in outdoor level ground and stair environments. In addition, the results of the gait phase estimation error for each walking environment were compared after learning for both max hip extension (MHE) and max hip flexion (MHF), which are ground truth criteria in gait phase divided in previous studies. As a result, the average error rate of all walking environments using MHF reference data and MHE reference data was 2.97% and 4.36%, respectively, and the result of using MHF reference data was 1.39% lower than the result of using MHE reference data.

• Journal of the Korean Society of Physical Medicine
• /
• v.4 no.4
• /
• pp.249-255
• /
• 2009

Purpose：To investigate of gait component in Parkinson's Disease patient. Methods：participated Parkinson's Disease patient(n=12) and Normal adult(n=13). gait measure used by GaitRite. Results：SPSS for win version 12 was used for statistic analysis and independent t-test used to find between two groups. In the comparison of temporal parameter of gait between groups, the swing phase was significant decreased in Parkinson's groups, in the stance phase was significant increased in Normal groups, in the single support was significant decreased in Parkinson's groups and in the double support was significant increased in Parkinson's groups(p<.05). In the asymmetrical ratio of singele support was significant increased in Parkinson's groups(p<.05), and the swing phase and stance phase was significant increased in Parkinson's groups(p<.05). Conclusion：In the Parkinson's Disease patient gait showed temporal and spatial component variable changes comparison normal adult. therefore, it was seems to very important considerable at gait tranning in clinical intervention.

• Journal of the Korean Society for Precision Engineering
• /
• v.27 no.2
• /
• pp.145-152
• /
• 2010

Gait phase detection is important for evaluating the recovery of gait ability in patients with paralysis, and for determining the stimulation timing in FES walking. In this study, three different motion sensors(tilt sensor, gyrosensor and accelerometer) were used to detect gait events(heel strike, HS; toe off, TO) and they were compared one another to determine the most applicable sensor for gait phase detection. Motion sensors were attached on the shank and heel of subjects. Gait phases determined by the characteristics of each sensor's signal were compared with those from FVA. Gait phase detections using three different motion sensors were valid, since they all have reliabilities more than 95%, when compared with FVA. HS and TO were determined by both FVA and motion sensor signals, and the accuracy of detecting HS and TO with motion sensors were assessed by the time differences between FVA and motion sensors. Results show of that the tilt sensor and the gyrosensor could detect gait phase more accurately in normal subjects. Vertical acceleration from the accelerometer could detect HS most accurately in hemiplegic patient group A. The gyrosensor could detect HS and TO most accurately in hemiplegic patient group A and B. Valid error ranges of HS and TO were determined by 3.9 % and 13.6 % in normal subjects, respectively. The detection of TO from all sensor signals was valid in both patient group A and B. However, the vertical acceleration detected HS validly in patient group A and the gyrosensor detected HS validly in patient group B. We could determine the most applicable motion sensors to detect gait phases in hemiplegic patients. However, since hemiplegic patients have much different gait patterns one another, further experimental studies using various simple motion sensors would be required to determine gait events in pathologic gaits.

• Journal of Institute of Control, Robotics and Systems
• /
• v.18 no.7
• /
• pp.644-649
• /
• 2012

This paper proposes the gait phase classifier using backpropagation neural networks method which uses the angle of lower body's joints and ground reaction force as input signals. The classification of a gait phase is useful to understand the gait characteristics of pathologic gait and to control the gait rehabilitation systems. The classifier categorizes a gait cycle as 7 phases which are commonly used to classify the sub-phases of the gait in the literature. We verify the efficiency of the proposed method through experiments.

• PNF and Movement
• /
• v.20 no.1
• /
• pp.135-145
• /
• 2022

Purpose: The purpose of this study was to investigate the association between cognitive and motor inhibition by comparing muscle activity and ground reaction force during unplanned gait termination according to reaction time measured through the stop-signal task. Methods: Sixteen young adults performed a stop-signal task and an unplanned gait termination separately. The subjects were divided into fast and slow groups based on their stop-signal reaction time (SSRT), as measured by the stop-signal task. Electromyography (EMG) and ground reaction force (GRF) were compared between the groups during unplanned gait termination. The data for gait termination were divided into three phases (Phase 1 to 3). The Mann-Whitney U test was used to compare spatiotemporal gait parameters and EMG and GRF data between groups. Results: The slow group had significantly higher activity of the tibialis anterior in Phase 2 and Phase 3 than the fast group (p <0.05). In Phase 1, the fast group had significantly shorter time to peak amplitude (TPA) of the soleus than the slow group (p <0.05). In Phase 2, the TPA of the tibialis anterior was significantly lower in the fast group than the slow group (p <0.05). In Phase 3, there was no significant difference in the GRF between the two groups (p >0.05). There were no significant difference between the two groups in the spatiotemporal gait parameters (p >0.05). Conclusion: Compared to the slow group, the fast group with cognitive inhibition suppressed muscle activity for unplanned gait termination. The association between SSRT and unplanned gait termination shows that a participant's ability to suppress an incipient finger response is relevant to their ability to construct a corrective gait pattern in a choice-demanding environment.

• Physical Therapy Rehabilitation Science
• /
• v.6 no.3
• /
• pp.106-112
• /
• 2017

Objective: Robot assisted gait training is implemented as part of therapy for the recovery of gait patterns in recent clinical fields, and the scope of implications are continuously increasing. However clear therapy protocols of robot assisted gait training are insufficent. The purpose of this study was to investigate the effects of robot-assisted gait training applied with guidance force on balance and gait performance in persons with hemiparetic stroke. Design: Two group pre-test post-test design. Methods: Nineteen persons were diagnosed with hemiparesis following stroke participated in this study. The participants were randomly assigned to the unilateral guidance group or bilateral guidance group to conduct robot-assisted gait training. All participants underwent robot-assisted gait training for twelve sessions (30 min/d, 3 d/wk for 4 weeks). They were assessed with gait parameters (gait velocity, cadence, step length, stance phase, and swing phase) using Optogait. This study also measured the dynamic gait index (DGI), the Berg balance scale (BBS) score, and timed up and go (TUG). Results: After training, BBS scores were was significantly increased in the bilateral training group than in the unilateral guidance group (p<0.05). Spatiotemporal parameters were significantly changed in the bilateral training group (gait speed, swing phase ratio, and stance phase ratio) compared to the unilateral training group (p<0.05). Conclusions: The results of this study suggest that robot-assisted gait training show feasibility in facilitating improvements in balance and gait performance for subacute hemiparetic stroke patients.

• Journal of Biomedical Engineering Research
• /
• v.26 no.3
• /
• pp.145-150
• /
• 2005

A new gait detection system using both FSR (force sensing resistor) sensors and a gyrosensor was developed to detect various gait patterns. FSR sensors were put in self-designed shoe insoles and a gyrosensor was attached to the heel of a shoe. An algorithm was also developed to determine eight different gait transitions during four gait phases: heel-strike, foot-flat, heel-off and swing. The developed system was evaluated from nine heathy mans and twelve hemiplegic patients. Healthy volunteers were asked to walk in various gait patterns: level walking, fore-foot walking and stair walking. Only the level walking was performed in hemiplegic patients. The gait detection system was compared with a optical motion analysis system and the outputs of the FSR sensors. In healthy subjects, the developed system detected successfully more than $99\%$ for both level walking and fore-foot walking. For stair walking, the successful detection rate of the system was above$97\%$. In hemiplegic patients, the developed system detected approximately 98% of gait transitions. The developed gait phase detection system will be helpful not only to determine pathological gait phases but also to apply prosthetics, orthotics and functional electrical stimulation for patients with various gait disorders.