Real time feedback system for walking gait analysis

Author(s):  
N. D. Deshmukh ◽  
M. N. Kakatkar
Author(s):  
Schahrazad Soltane ◽  
Shahad Al-Mutabeq ◽  
Mona Masood ◽  
Rawan Al-Otaibi ◽  
Safiah Abdul Raouf ◽  
...  

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2727
Author(s):  
Hari Prasanth ◽  
Miroslav Caban ◽  
Urs Keller ◽  
Grégoire Courtine ◽  
Auke Ijspeert ◽  
...  

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.


Author(s):  
Vincent Berardi ◽  
John Bellettiere ◽  
Benjamin Nguyen ◽  
Neil E Klepeis ◽  
Suzanne C Hughes ◽  
...  

Abstract Few studies have examined the relative effectiveness of reinforcing versus aversive consequences at changing behavior in real-world environments. Real-time sensing devices makes it easier to investigate such questions, offering the potential to improve both intervention outcomes and theory. This research aims to describe the development of a real-time, operant theory-based secondhand smoke (SHS) intervention and compare the efficacy of aversive versus aversive plus reinforcement contingency systems. Indoor air particle monitors were placed in the households of 253 smokers for approximately three months. Participants were assigned to a measurement-only control group (N = 129) or one of the following groups: 1.) aversive only (AO, N = 71), with aversive audio/visual consequences triggered by the detection of elevated air particle measurements, or 2.) aversive plus reinforcement (AP, N = 53), with reinforcing consequences contingent on the absence of SHS added to the AO intervention. Residualized change ANCOVA analysis compared particle concentrations over time and across groups. Post-hoc pairwise comparisons were also performed. After controlling for Baseline, Post-Baseline daily particle counts (F = 6.42, p = 0.002), % of time >15,000 counts (F = 7.72, p < 0.001), and daily particle events (F = 4.04, p = 0.02) significantly differed by study group. Nearly all control versus AO/AP pair-wise comparisons were statistically significant. No significant differences were found for AO versus AP groups. The aversive feedback system reduced SHS, but adding reinforcing consequences did not further improve outcomes. The complexity of real-world environments requires the nuances of these two contingency systems continue to be explored, with this study demonstrating that real-time sensing technology can serve as a platform for such research.


Author(s):  
Alexandre de Queiroz Burle ◽  
Thiago Buarque de Gusmao Lafayette ◽  
Jose Roberto Fonseca ◽  
Veronica Teichrieb ◽  
Alana Elza Fontes Da Gama

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Saaveethya Sivakumar ◽  
Alpha Agape Gopalai ◽  
King Hann Lim ◽  
Darwin Gouwanda ◽  
Sunita Chauhan

AbstractThis paper presents a wavelet neural network (WNN) based method to reduce reliance on wearable kinematic sensors in gait analysis. Wearable kinematic sensors hinder real-time outdoor gait monitoring applications due to drawbacks caused by multiple sensor placements and sensor offset errors. The proposed WNN method uses vertical Ground Reaction Forces (vGRFs) measured from foot kinetic sensors as inputs to estimate ankle, knee, and hip joint angles. Salient vGRF inputs are extracted from primary gait event intervals. These selected gait inputs facilitate future integration with smart insoles for real-time outdoor gait studies. The proposed concept potentially reduces the number of body-mounted kinematics sensors used in gait analysis applications, hence leading to a simplified sensor placement and control circuitry without deteriorating the overall performance.


Sensors ◽  
2015 ◽  
Vol 15 (1) ◽  
pp. 1417-1434 ◽  
Author(s):  
Enea Cippitelli ◽  
Samuele Gasparrini ◽  
Susanna Spinsante ◽  
Ennio Gambi

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