• 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.

• Journal of Electrical Engineering and Technology
• /
• v.9 no.2
• /
• pp.733-738
• /
• 2014

We propose a new approach called as a peak time approach for faster detection of step initiation for the lower limb exoskeleton. As faster detection of step initiation is an important criterion in evaluating the lower limb exoskeleton, many studies have investigated approaches to detect step initiation faster, including using electromyography, the center of pressure, the heel-off time and the toe-off time. In this study, we will utilize vertical ground reaction force events to detect step initiation, and compare our approach with prior approaches. Additionally, we will predict the first step's heel strike time with vertical ground reaction force events from multiple regression equations to support our approach. The lower limb exoskeleton should assist the operator's movement much faster and more reliably with our approach.

• Journal of the Ergonomics Society of Korea
• /
• v.26 no.2
• /
• pp.131-136
• /
• 2007

In this study, we developed an active ankle-foot orthosis(AAFO) which can control dorsi/ plantar flexion of the ankle joint to prevent foot drop and toe drag during walking. 3D gait analyses were performed on five healthy subjects under three different gait conditions: the normal gait without AFO, the SAFO gait with the conventional plastic AFO, and the AAFO gait with the developed AFO. As a result, the developed AAFO preeminently induced the normal gait compared to the SAFO. Additionally, AAFO prevented foot drop by proper plantarflexion during loading response and provided enough plantarflexion moment as a driving force to walk forward by sufficient push-off during pre-swing. AAFO also could prevent toe drag by proper dorsiflexion during swing phase. These results indicate that the developed AAFO may have more clinical benefits to treat foot drop and toe drag, compared to conventional AFOs, and also may be useful in patients with other orthotic devices.

• Korean Journal of Applied Biomechanics
• /
• v.19 no.1
• /
• pp.159-166
• /
• 2009

The purpose of this study was to develop and automatic gait event detection algorithm using single accelerometer which is attached at the top of the shoe. The sinal vector magnitude and anterior-posterior(x-axis) directional component of accelerometer were used to detect heel strike(HS) and toe off(TO), respectively. To evaluate proposed algorithm, gait event timing was compared with that by force plate and kinematic data. In experiment, 7 subjects performed 10 trials level walking with 3 different walking conditions such as fast, preferred & slow walking. An accelerometer, force plate and 3D motion capture system were used during experiment. Gait event by force plate was used as reference timing. Results showed that gait event by accelerometer is similar to that by force plate. The distribution of differences were spread about $22.33{\pm}17.45m$ for HS and $26.82{\pm}14.78m$ for To and most error was existed consistently prior to 20ms. The difference between gait event by kinematic data and developed algorithm was small. Thus it can be concluded that developed algorithm can be used during outdoor walking experiment. Further study is necessary to extract gait spatial variables by removing gravity factor.

• 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.

• Journal of the Ergonomics Society of Korea
• /
• v.35 no.5
• /
• pp.319-341
• /
• 2016

Objective:This research analyzed the lower-limb motion in kinetic and kinematic way while walking on various terrains to develop Foot-Ground Contact Detection (FGCD) algorithm using the Inertial Measurement Unit (IMU). Background: To estimate the location of human in GPS-denied environments, it is well known that the lower-limb kinematics based on IMU sensors, and pressure insoles are very useful. IMU is mainly used to solve the lower-limb kinematics, and pressure insole are mainly used to detect the foot-ground contacts in stance phase. However, the use of multiple sensors are not desirable in most cases. Therefore, only IMU based FGCD can be an efficient method. Method: Orientation and acceleration of lower-limb of 10 participants were measured using IMU while walking on flat ground, ascending and descending slope and stairs. And the inertial information showing significant changes at the Heel strike (HS), Full contact (FC), Heel off (HO) and Toe off (TO) was analyzed. Results: The results confirm that pitch angle, rate of pitch angle of foot and shank, and acceleration in x, z directions of the foot are useful in detecting the four different contacts in five different walking terrain. Conclusion: IMU based FGCD Algorithm considering all walking terrain possible in daily life was successfully developed based on all IMU output signals showing significant changes at the four steps of stance phase. Application: The information of the contact between foot and ground can be used for solving lower-limb kinematics to estimating an individual's location and walking speed.