• ETRI Journal
• /
• v.42 no.1
• /
• pp.46-53
• /
• 2020

Gait analysis is an effective clinical tool across a wide range of applications. Recently, inertial measurement units have been extensively utilized for gait analysis. Effective gait analyses require good estimates of heel-strike and toe-off events. Previous studies have focused on the effective device position and type of triaxis direction to detect gait events. This study proposes an effective heel-strike and toe-off detection algorithm using a smart insole with inertial measurement units. This method detects heel-strike and toe-off events through a time-frequency analysis by limiting the range. To assess its performance, gait data for seven healthy male subjects during walking and running were acquired. The proposed heel-strike and toe-off detection algorithm yielded the largest error of 0.03 seconds for running toe-off events, and an average of 0-0.01 seconds for other gait tests. Novel gait analyses could be conducted without suffering from space limitations because gait parameters such as the cadence, stance phase time, swing phase time, single-support time, and double-support time can all be estimated using the proposed heel-strike and toe-off detection algorithm.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.56 no.5
• /
• pp.990-992
• /
• 2007

The purpose of this study is to develop a practical gait-event detection system which is necessary for the FES (functional electrical stimulation) control of locomotion in paralyzed patients. The system is comprised of a sensor board and an event recognition algorithm. We focused on the practicality improvement of the system through 1) using accelerometer to get the angle of shank and dispensing with the foot-switches having limitation in indoor or barefoot usage and 2) using a rule-base instead of threshold to determine the heel-off/heel-strike events corresponding the stimulation on/off timing. The sensor signals are transmitted through RF communication and gait-events was detected using the peaks in shank angle. The system could detect two critical gait-events in all five paralyzed patients. The standard deviation of the gait events time from the peaks were smaller when 1.5Hz cutoff frequency was used in the derivation of the shank angle from the acceleration signals.

• Journal of the Korean Society for Precision Engineering
• /
• v.22 no.3 s.168
• /
• pp.170-178
• /
• 2005

The purpose of this study is to develop a gait-event detection system, which is necessary for the cycle-to-cycle FES control of locomotion. Proposed gait event detection system consists of a signal measurement part and gait event detection part. The signal measurement was composed of the sensors and the LabVIEW program for the data acquisition and synchronization of the sensor signals. We also used a video camera and a motion capture system to get the reference gait events. Machine learning technique with ANN (artificial neural network) was adopted for automatic detection of gait events. 2 cycles of reference gait events were used as the teacher signals for ANN training and the remnants ($2\sim5$ cycles) were used fur the evaluation of the performance in gait-event detection. 14 combinations of sensor signals were used in the training and evaluation of ANN to examine the relationship between the number of sensors and the gait-event detection performance. The best combinations with minimum errors of event-detection time were 1) goniometer, foot-switch and 2) goniometer, foot-switch, accelerometer x(anterior-posterior) component. It is expected that the result of this study will be useful in the design of cycle-to-cycle FES controller.

• The Transactions of the Korean Institute of Electrical Engineers D
• /
• v.55 no.5
• /
• pp.248-253
• /
• 2006

The purpose of this study is to develop a portable gait-event detection system which is necessary for the cycle-to-cycle FES(functional electrical stimulation) control of locomotion. To make the system portable, we made following modifications in the gait signal measurement system. That is, 1) to make the system wireless using Bluetooth communication, 2) to make the system small-sized and battery-powered by using low power consumption ${\mu}$ P(ATmega8535L). The gait-events were analyzed in off-line at the main computer using ANN(Artificial Neural Network). The Proposed system showed no mis-detection of the gait-events of normal subject and hemiplegia subjects. The performance of the system was better than the previous wired-system.

• 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.

• Korean Journal of Applied Biomechanics
• /
• v.21 no.4
• /
• pp.495-501
• /
• 2011

The purpose of this study was to develop the inertial sensor module system to detect gait event using single angular rate sensor(gyroscope), and evaluate the accuracy of this system. This sensor module is attached at the heel and gait events such as heel strike, foot flat, heel off, toe off are detected by using proposed automatic event detection algorithm. The developed algorithm detect characteristics of pitch data of the gyroscope to find gait event. To evaluate the accuracy of system, 3D motion capture system was used and synchronized with sensor module system for comparison of gait event timings. In experiment, 6 subjects performed 5 trials level walking with 3 different conditions such as slow, preferred and fast. Results showed that gait event timings by sensor module system are similar to that by kinematic data, because maximum absolute errors were under 37.4msec regardless of gait velocity. Therefore, this system can be used to detect gait events. Although this system has advantages of small, light weight, long-term monitoring and high accuracy, it is necessary to improve the system to get other gait information such as gait velocity, stride length, step width and joint angles.

• Physical Therapy Korea
• /
• v.14 no.4
• /
• pp.44-49
• /
• 2007

The purpose of this study was to investigate the effects of auditory cues in the form of a metronome on gait initiation (GI) in Parkinson's disease (PD). 2 patients (mean age: 54 yrs) with idiopathic PD participated in the study. All patients (Hoehn and Yahr disability score of 2.0) were tested in the "on" state approximately 1.5 hours following the administration and fully responding to their PD medications. Subjects first initiated walking at self-initiated speeds to determine their cadences. Then, subjects were asked to initiate gait along the walkway while keeping pace with a metronome. The metronome rate (in beats/min) was set at a cadence 85% (slow condition), 100% (normal condition) and 115% (fast condition) of gait for each subject. Subjects were able to increase the speed of GI with faster cadence, but the speed of GI for the slow condition was similar to that of the normal condition. Swing toe-off was 578.3 ms for the fast condition, 709.4 ms for the normal condition and 736.2 ms for the slow condition. Respective times for swing heel-strike were 894.3 ms, 1110.2 ms and 1119.1 ms, and stance toe-off were 1105.4 ms, 1338.5 ms, and 1343.1 ms. Except for stance unloading ground reaction forces were greatest for the fast condition and smallest for the slow condition. It appears that PD patients were able to modulate GRFs and temporal events in response to auditory cues to achieve the peak acceleration force of the swing and stance limb. The findings from this study provided preliminary data, which could be used to investigate how PD patients modulate GRFs and temporal events during GI in response to tasks.

• Journal of Sensor Science and Technology
• /
• v.18 no.6
• /
• pp.449-455
• /
• 2009

In this paper, we developed a digital gait analyzer using the triaxial accelerometer(TA). An approach for normal gait detection employing decay slope peak detection(DSPD) algorithm was presented. The TA was attached to the center of the waist of a subject. The subject walked a bare floor at 60, 92 and 120 steps/minute. We analyzed vertical axis acceleration signal for gait detection. At 60, 92, 120 steps/minute walking, detection accuracy of gait events were over 99 % accuracy.

• Annals of Rehabilitation Medicine
• /
• v.39 no.6
• /
• pp.897-904
• /
• 2015

Objective To investigate differences in plantar pressure distribution between a normal gait and unpredictable slip events to predict the initiation of the slipping process. Methods Eleven male participants were enrolled. Subjects walked onto a wooden tile, and two layers of oily vinyl sheet were placed on the expected spot of the 4th step to induce a slip. An insole pressure-measuring system was used to monitor plantar pressure distribution. This system measured plantar pressure in four regions (the toes, metatarsal head, arch, and heel) for three events: the step during normal gait; the recovered step, when the subject recovered from a slip; and the uncorrected, harmful slipped step. Four variables were analyzed: peak pressure (PP), contact time (CT), the pressure-time integral (PTI), and the instant of peak pressure (IPP). Results The plantar pressure pattern in the heel was unique, as compared with other parts of the sole. In the heel, PP, CT, and PTI values were high in slipped and recovered steps compared with normal steps. The IPP differed markedly among the three steps. The IPPs in the heel for the three events were, in descending order (from latest to earliest), slipped, recovered, and normal steps, whereas in the other regions the order was normal, recovered, and slipped steps. Finally, the metatarsal head-to-heel IPP ratios for the normal, recovered, and slipped steps were $6.1{\pm}2.9$, $3.1{\pm}3.0$, and $2.2{\pm}2.5$, respectively. Conclusion A distinctive plantar pressure pattern in the heel might be useful for early detection of a slip event to prevent slip-related injuries.

• Journal of the Korean Society of Physical Medicine
• /
• v.14 no.3
• /
• pp.21-27
• /
• 2019

PURPOSE: This study examined the changes in the kinematic variables during walking on a downhill ramp according to the shoe heel height. METHODS: The subjects were 10 adult women with no history of musculoskeletal disorders who agreed to participate in the study. Data were collected using a motion analysis system (VICON) consisting of six infrared cameras. The slope was 120 cm in width, 200 cm in length, and 15 in inclination. To confirm the change in gait parameters (stride length, gait speed) and lower extremity joint angle according to the heel heights of the shoes, flat, 5 cm, and 10 cm heel shoes were prepared and walked alternately. RESULTS: As a result, both the stride length and walking speed showed significant differences according to the heel height between flat and 10 cm (p<.05). In the sagittal plane, there was no significant difference in the hip joint and knee joint, but a significant difference was observed in all events in the ankle joint on all heel heights (p<.05). In particular, the heel strike and mid stance events showed significant differences among all height conditions (p<.05). No significant difference was observed in any of the joint angle changes in the frontal plane (p>.05). CONCLUSION: As the shoe heel height increased, the instability increased and efforts to secure the stability were made, leading to a shortened stride length, walking speed, and angle of the ankle joint.