• Title/Summary/Keyword: Gait signals

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Gait-Event Detection for FES Locomotion (FES 보행을 위한 보행 이벤트 검출)

  • Heo Ji-Un;Kim Chul-Seung;Eom Gwang-Moon
    • Journal of the Korean Society for Precision Engineering
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    • v.22 no.3 s.168
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    • pp.170-178
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    • 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.

Adaptive Postural Control for Trans-Femoral Prostheses Based on Neural Networks and EMG Signals

  • Lee Ju-Won;Lee Gun-Ki
    • International Journal of Precision Engineering and Manufacturing
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    • v.6 no.3
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    • pp.37-44
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    • 2005
  • Gait control capacity for most trans-femoral prostheses is significantly different from that of a normal person, and training is required for a long period of time in order for a patient to walk properly. People become easily tired when wearing a prosthesis or orthosis for a long period typically because the gait angle cannot be smoothly adjusted during wearing. Therefore, to improve the gait control problems of a trans-femoral prosthesis, the proper gait angle is estimated through surface EMG(electromyogram) signals on a normal leg, then the gait posture which the trans-femoral prosthesis should take is calculated in the neural network, which learns the gait kinetics on the basis of the normal leg's gait angle. Based on this predicted angle, a postural control method is proposed and tested adaptively following the patient's gait habit based on the predicted angle. In this study, the gait angle prediction showed accuracy of over $97\%$, and the posture control capacity of over $90\%$.

An Attention-based Temporal Network for Parkinson's Disease Severity Rating using Gait Signals

  • Huimin Wu;Yongcan Liu;Haozhe Yang;Zhongxiang Xie;Xianchao Chen;Mingzhi Wen;Aite Zhao
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.17 no.10
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    • pp.2627-2642
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    • 2023
  • Parkinson's disease (PD) is a typical, chronic neurodegenerative disease involving the concentration of dopamine, which can disrupt motor activity and cause different degrees of gait disturbance relevant to PD severity in patients. As current clinical PD diagnosis is a complex, time-consuming, and challenging task that relays on physicians' subjective evaluation of visual observations, gait disturbance has been extensively explored to make automatic detection of PD diagnosis and severity rating and provides auxiliary information for physicians' decisions using gait data from various acquisition devices. Among them, wearable sensors have the advantage of flexibility since they do not limit the wearers' activity sphere in this application scenario. In this paper, an attention-based temporal network (ATN) is designed for the time series structure of gait data (vertical ground reaction force signals) from foot sensor systems, to learn the discriminative differences related to PD severity levels hidden in sequential data. The structure of the proposed method is illuminated by Transformer Network for its success in excavating temporal information, containing three modules: a preprocessing module to map intra-moment features, a feature extractor computing complicated gait characteristic of the whole signal sequence in the temporal dimension, and a classifier for the final decision-making about PD severity assessment. The experiment is conducted on the public dataset PDgait of VGRF signals to verify the proposed model's validity and show promising classification performance compared with several existing methods.

Comparison of Motion Sensor Systems for Gait Phase Detection (보행주기 검출용 모션 센서 시스템의 비교)

  • Park, Sun-Woo;Sohn, Ryang-Hee;Ryu, Ki-Hong;Kim, Young-Ho
    • Journal of the Korean Society for Precision Engineering
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    • v.27 no.2
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    • pp.145-152
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    • 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.

Detection of Steps or Gait Assessment of Hemiplegic Patient Based on Accelerometer (가속도계 기반의 편마비 환자 보행 평가를 위한 보 검출)

  • Lee, Hyo-Ki;Kim, Young-Ho;Park, Si-Woon;Lee, Kyoung-Joung
    • The Transactions of the Korean Institute of Electrical Engineers D
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    • v.55 no.10
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    • pp.452-457
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    • 2006
  • In this paper, an algorithm to detect steps in hemiplegic patients using a 3-axis accelerometer a紅ached on the trunk was proposed. The proposed algorithm consisted of the signal pre-processing, the step detector, the classification of steps and the calculation of stride time. Two FIR band-pass filters were designed and steps were measured by the combination of filtered signals in the vertical and the anteroposterior directions. In addition, the classification of steps and the calculation of stride time were computed by using the detected steps and lateral signals. For the experiment, fourteen hemiplegic patients were participated and the linear accelerations of the trunk and foot switch signals were measured synchronously. To evaluate the system performance, the detected steps and initial contacts by the foot switch were compared. The average error between the steps and initial contacts was 0.024ms and the difference of the average stride time was 0.01s. Finally, all gait events were detected exactly. Results showed that the accelerometry could use for the gait evaluation in clinical rehabilitation therapies.

Gait-Event Detection using an Accelerometer for the Paralyzed Patients (가속도계를 이용한 마비환자의 보행이벤트 검출)

  • Kong, Se-Jin;Kim, Chul-Seung;Moon, Ki-Wook;Eom, Gwang-Moon;Tack, Gye-Rae;Kim, Kyeong-Seop;Lee, Jeong-Whan;Lee, Young-Hee
    • The Transactions of The Korean Institute of Electrical Engineers
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    • v.56 no.5
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    • pp.990-992
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    • 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.

Gait Phases Detection and Judgment based Multi Biomedical Signals (다중 생체 신호 기반 보행 단계 감지 및 판단)

  • Kim, S.J.;Jeong, E.C.;Song, Y.R.;Yoon, K.S.;Lee, S.M.
    • Journal of rehabilitation welfare engineering & assistive technology
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    • v.6 no.2
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    • pp.43-48
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    • 2012
  • In this paper, we present the method of gait phases detection using multi biomedical signals during normal gait. Electromyogram(EMG) signals, muscle of thigh angle measurement device and resistive sensors are used for experiments. We implemented a test targeting five adult male and identified the pattern of EMG signal of normal gait. For acquiring the EMG signal, subjects attached surface Ag/AgCl electrodes to quadriceps femoris, biceps femoris, tibialis anterior and gastrocnemius medialis. Resistance sensors are attached to the heel toe and soles of the each feet for measuring attachment state of between feet and ground. Infrared sensors are attached on the thigh and thigh angle measurement device has the range from flection 25 degrees to extension 20 degrees. The results of this paper, The stance and swing phase could be confirmed during the normal gait and be classified in detail the eight steps.

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Gait Phases Classification using Joint angle and Ground Reaction Force: Application of Backpropagation Neural Networks (관절각과 지면반발력을 이용한 보행 단계의 분류: 역전파 신경망 적용)

  • Chae, Min-Gi;Jung, Jun-Young;Park, Chul-Je;Jang, In-Hun;Park, Hyun-Sub
    • Journal of Institute of Control, Robotics and Systems
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    • v.18 no.7
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    • pp.644-649
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    • 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.

Modeling of Normal Gait Acceleration Signal Using a Time Series Analysis Method (시계열 분석을 이용한 정상인의 보행 가속도 신호의 모델링)

  • Lim Ye-Taek;Lee Kyoung-Joung;Ha Eunho;Kim Han-Sung
    • The Transactions of the Korean Institute of Electrical Engineers D
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    • v.54 no.7
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    • pp.462-467
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    • 2005
  • In this paper, we analyzed normal gait acceleration signal by time series analysis methods. Accelerations were measured during walking using a biaxial accelerometer. Acceleration data were acquired from normal subjects(23 men and one woman) walking on a level corridor of 20m in length with three different walking speeds. Acceleration signals were measured at a sampling frequency of 60Hz from a biaxial accelerometer mounted between L3 and L4 intervertebral area. Each step signal was analyzed using Box-Jenkins method. Most of the differenced normal step signals were modeled to AR(3) and the model didn't show difference for model's orders and coefficients with walking speed. But, tile model showed difference with acceleration signal direction - vertical and lateral. The above results suggested the proposed model could be applied to unit analysis.

Gait Angle Prediction for Lower Limb Orthotics and Prostheses Using an EMG Signal and Neural Networks

  • Lee Ju-Won;Lee Gun-Ki
    • International Journal of Control, Automation, and Systems
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    • v.3 no.2
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    • pp.152-158
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    • 2005
  • Commercial lower limb prostheses or orthotics help patients achieve a normal life. However, patients who use such aids need prolonged training to achieve a normal gait, and their fatigability increases. To improve patient comfort, this study proposed a method of predicting gait angle using neural networks and EMG signals. Experimental results using our method show that the absolute average error of the estimated gait angles is $0.25^{\circ}$. This performance data used reference input from a controller for the lower limb orthotic or prosthesis controllers while the patients were walking.