Real-time gait phase detection using wearable sensors

Mazhar, Osama; Bari, Abu Zeeshan; Faudzi, Ahmad Athif Mohd

doi:10.1109/ascc.2015.7244853

Cited by 3 publications

(3 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Rule-based approaches are also popular in the case of IPSs, with several studies using threshold or peak detection based approaches on IPSs, either to distinguish between stance phase and swing phase or between multiple gait phases [ 57 , 67 , 68 , 69 , 70 ]. Lin et al [ 62 ] set a threshold on the first derivative of the pressure sensor data in identifying HS and TO and reports that it makes the detection robust against spurious signals, offset variation between IPSs and between-subject variations.…”

Section: Resultsmentioning

confidence: 99%

Wearable Sensor-Based Real-Time Gait Detection: A Systematic Review

Prasanth

Caban

Keller

et al. 2021

Sensors

152

100

View full text Add to dashboard Cite

Gait analysis has traditionally been carried out in a laboratory environment using expensive equipment, but, recently, reliable, affordable, and wearable sensors have enabled integration into clinical applications as well as use during activities of daily living. Real-time gait analysis is key to the development of gait rehabilitation techniques and assistive devices such as neuroprostheses. This article presents a systematic review of wearable sensors and techniques used in real-time gait analysis, and their application to pathological gait. From four major scientific databases, we identified 1262 articles of which 113 were analyzed in full-text. We found that heel strike and toe off are the most sought-after gait events. Inertial measurement units (IMU) are the most widely used wearable sensors and the shank and foot are the preferred placements. Insole pressure sensors are the most common sensors for ground-truth validation for IMU-based gait detection. Rule-based techniques relying on threshold or peak detection are the most widely used gait detection method. The heterogeneity of evaluation criteria prevented quantitative performance comparison of all methods. Although most studies predicted that the proposed methods would work on pathological gait, less than one third were validated on such data. Clinical applications of gait detection algorithms were considered, and we recommend a combination of IMU and rule-based methods as an optimal solution.

show abstract

Section: Resultsmentioning

confidence: 99%

Wearable Sensor-Based Real-Time Gait Detection: A Systematic Review

Prasanth

Caban

Keller

et al. 2021

Sensors

152

100

View full text Add to dashboard Cite

show abstract

“…The active intelligent gait training systems have the ability to monitor patient's movement in real time [13]. At present, human's body movements are mainly measured through the fixed force platform and optical motion capture system [14][15][16] in the gait laboratory, or multiple movement and force sensors worn on the limb [17][18][19]. The former is highly accurate but limited by the measurement environment, and the latter may interfere with the normal human movement.…”

Section: Gait Movement Measurementmentioning

confidence: 99%

Measurement, Evaluation, and Control of Active Intelligent Gait Training Systems—Analysis of the Current State of the Art

et al. 2022

View full text Add to dashboard Cite

Gait recognition and rehabilitation has been a research hotspot in recent years due to its importance to medical care and elderly care. Active intelligent rehabilitation and assistance systems for lower limbs integrates mechanical design, sensing technology, intelligent control, and robotics technology, and is one of the effective ways to resolve the above problems. In this review, crucial technologies and typical prototypes of active intelligent rehabilitation and assistance systems for gait training are introduced. The limitations, challenges, and future directions in terms of gait measurement and intention recognition, gait rehabilitation evaluation, and gait training control strategies are discussed. To address the core problems of the sensing, evaluation and control technology of the active intelligent gait training systems, the possible future research directions are proposed. Firstly, different sensing methods need to be proposed for the decoding of human movement intention. Secondly, the human walking ability evaluation models will be developed by integrating the clinical knowledge and lower limb movement data. Lastly, the personalized gait training strategy for collaborative control of human–machine systems needs to be implemented in the clinical applications.

show abstract

“…According to a review of wearable gait detection devices [ 42 ], 23 journal papers have used SIMUs on the shank and 25 journal papers have used SIMUs on the foot in an attempt to make SIMUs a popular wearable system for gait analysis. These papers have primarily focused on identifying a user’s gait pattern using artificial intelligence (AI) and machine learning (ML) methods, such as artificial neural networks, extreme learning machines, convolutional neural networks, and long short-term memory [ 43 , 44 , 45 , 46 , 47 ], or rule-based algorithms to detect gait phases such as heel strike, toe-off, and swing phase [ 48 , 49 , 50 , 51 , 52 ].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Control Method for Gait Detection and Classification Devices with Inertial Measurement Unit

Kim

2023

Sensors

View full text Add to dashboard Cite

Cueing and feedback training can be effective in maintaining or improving gait in individuals with Parkinson’s disease. We previously designed a rehabilitation assist device that can detect and classify a user’s gait at only the swing phase of the gait cycle, for the ease of data processing. In this study, we analyzed the impact of various factors in a gait detection algorithm on the gait detection and classification rate (GDCR). We collected acceleration and angular velocity data from 25 participants (1 male and 24 females with an average age of 62 ± 6 years) using our device and analyzed the data using statistical methods. Based on these results, we developed an adaptive GDCR control algorithm using several equations and functions. We tested the algorithm under various virtual exercise scenarios using two control methods, based on acceleration and angular velocity, and found that the acceleration threshold was more effective in controlling the GDCR (average Spearman correlation −0.9996, p < 0.001) than the gyroscopic threshold. Our adaptive control algorithm was more effective in maintaining the target GDCR than the other algorithms (p < 0.001) with an average error of 0.10, while other tested methods showed average errors of 0.16 and 0.28. This algorithm has good scalability and can be adapted for future gait detection and classification applications.

show abstract

Real-time gait phase detection using wearable sensors

Cited by 3 publications

References 13 publications

Wearable Sensor-Based Real-Time Gait Detection: A Systematic Review

Wearable Sensor-Based Real-Time Gait Detection: A Systematic Review

Measurement, Evaluation, and Control of Active Intelligent Gait Training Systems—Analysis of the Current State of the Art

Adaptive Control Method for Gait Detection and Classification Devices with Inertial Measurement Unit

Contact Info

Product

Resources

About