Abnormal Gait Behavior Detection for Elderly Based on Enhanced Wigner-Ville Analysis and Cloud Incremental SVM Learning

Luo, Jian; Tang, Jin; Xiao, Xiaoming

doi:10.1155/2016/5869238

Cited by 6 publications

(3 citation statements)

References 34 publications

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“…Recent advancements in technology have led to reliable, affordable, and wearable sensors for gait analysis that enable its use outside of a laboratory environment and during activities of daily living. One of its primary uses has been in diagnosing walking impairment in people with gait disabilities [ 1 , 2 , 3 , 4 ] and inspires control mechanisms of exoskeletons [ 5 , 6 ] and prostheses [ 7 ], among other applications [ 8 , 9 , 10 , 11 , 12 ]. More specifically, real-time gait analysis has proven essential in applications necessitating real-time control such as exoskeletons and prostheses, as well as gait rehabilitation involving Functional Electrical Stimulation (FES) [ 13 , 14 ] or Epidural Electrical Stimulation (EES) [ 15 ].…”

Section: Introductionmentioning

confidence: 99%

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

Prasanth

Caban

Keller

et al. 2021

Sensors

170

100

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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.

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Section: Introductionmentioning

confidence: 99%

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

Prasanth

Caban

Keller

et al. 2021

Sensors

170

100

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“…SVM Classification. SVM classifier with linear kernel has been one of classifiers, which can categorize the feature vector with the help of a hyper plane [25]. The separation between the data is coined as decision hyperplane, and the separation from the hyper-plane is termed margin.…”

Section: Feature Selection Using Genetic Algorithmmentioning

confidence: 99%

Design and Development of Polymer-Based Optical Fiber Sensor for GAIT Analysis

Mamidipaka¹,

Suman²,

Kumar

et al. 2023

International Journal of Polymer Science

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In the present scenario like COVID-19 pandemic, to maintain physical distance, the gait-based biometric is a must. Human gait identification is a very difficult process, but it is a suitable distance biometric that also gives good results at low resolution conditions even with face features that are not clear. This study describes the construction of a smart carpet that measures ground response force (GRF) and spatio-temporal gait parameters (STGP) using a polymer optical fiber sensor (POFS). The suggested carpet contains two light detection units for acquiring signals. Each unit obtains response from 10 nearby sensors. There are 20 intensity deviation sensors on a fiber. Light-emitting diodes (LED) are triggered successively, using the multiplexing approach that is being employed. Multiplexing is dependent on coupling among the LED and POFS sections. Results of walking experiments performed on the smart carpet suggested that certain parameters, including step length, stride length, cadence, and stance time, might be used to estimate the GRF and STGP. The results enable the detection of gait, including the swing phase, stance, stance length, and double supporting periods. The suggested carpet is dependable, reasonably priced equipment for gait acquisition in a variety of applications. Using the sensor data, gait recognition is performed using genetic algorithm (GA) and particle swarm optimization (PSO) technique. GA- and PSO-based gait template analyses are performed to extract the features with respect to the gait signals obtained from polymer optical gait sensors (POGS). The techniques used for classification of the obtained signals are random forest (RF) and support vector machine (SVM). The accuracy, sensitivity, and specificity results are obtained using SVM classifier and RF classifier. The results obtained using both classifiers are compared.

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“…Using incorporating software and algorithms, Inertial Measurement Units (IMUs) allow to measure spatiotemporal and kinematic parameters of each segment on which they are positioned [6]. Currently, IMUs are the most used sensors devices in multiple fields as diagnosing gait disabilities [7][8][9],…”

Section: Introductionmentioning

confidence: 99%

The Use of Embedded IMU Insoles to Assess Gait Parameters: A Validation and Test-Retest Reliability Study

Riglet,

Nicol,

Leonard

et al. 2023

Sensors

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Wireless wearable insoles are interesting tools to collect gait parameters during daily life activities. However, studies have to be performed specifically for each type of insoles on a big data set to validate the measurement in ecological situations. This study aims to assess the criterion validity and test-retest reliability of gait parameters from wearable insoles compared to motion capture system. Gait of 30 healthy participants was recorded using DSPro® insoles and a motion capture system during overground and treadmill walking at three different speeds. Criterion validity and test-retest reliability of spatio-temporal parameters were estimated with an intraclass correlation coefficient (ICC). For both systems, reliability was found higher than 0.70 for all variables (p < 0.001) except for minimum toe clearance (ICC < 0.50) with motion capture system during overground walking. Regardless of speed and condition of walking, Speed, Cadence, Stride Length, Stride Time and Stance Time variables were validated (ICC > 0.90; p < 0.001). During walking on treadmill, loading time was not validated during slow speed (ICC < 0.70). This study highlights good criterion validity and test-retest reliability of spatiotemporal gait parameters measurement using wearable insoles and opens a new possibility to improve care management of patients using clinical gait analysis in daily life activities.

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Abnormal Gait Behavior Detection for Elderly Based on Enhanced Wigner-Ville Analysis and Cloud Incremental SVM Learning

Cited by 6 publications

References 34 publications

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

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

Design and Development of Polymer-Based Optical Fiber Sensor for GAIT Analysis

The Use of Embedded IMU Insoles to Assess Gait Parameters: A Validation and Test-Retest Reliability Study

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