Development of a Robust, Simple, and Affordable Human Gait Analysis System Using Bottom-Up Pose Estimation With a Smartphone Camera

Viswakumar, Aditya; Rajagopalan, Venkateswaran; Ray, Tathagata; Gottipati, Pranitha; Parimi, Chandu

doi:10.3389/fphys.2021.784865

Cited by 22 publications

(21 citation statements)

References 28 publications

(34 reference statements)

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“…These studies consistently conclude that, despite having a higher average error with respect to marker based approaches, markerless HPE can be effectively used in gait and posture analysis. Similar results are obtained by Viswakumar et al (2022) using OpenPose to estimate knee angles. The main issue with single camera setups is represented by self-occlusions.…”

Section: Gait and Posture Analysissupporting

confidence: 84%

Markerless human pose estimation for biomedical applications: a survey

Avogaro

Cunico

Rosenhahn

et al. 2023

Front. Comput. Sci.

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Markerless Human Pose Estimation (HPE) proved its potential to support decision making and assessment in many fields of application. HPE is often preferred to traditional marker-based Motion Capture systems due to the ease of setup, portability, and affordable cost of the technology. However, the exploitation of HPE in biomedical applications is still under investigation. This review aims to provide an overview of current biomedical applications of HPE. In this paper, we examine the main features of HPE approaches and discuss whether or not those features are of interest to biomedical applications. We also identify those areas where HPE is already in use and present peculiarities and trends followed by researchers and practitioners. We include here 25 approaches to HPE and more than 40 studies of HPE applied to motor development assessment, neuromuscolar rehabilitation, and gait & posture analysis. We conclude that markerless HPE offers great potential for extending diagnosis and rehabilitation outside hospitals and clinics, toward the paradigm of remote medical care.

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Section: Gait and Posture Analysissupporting

confidence: 84%

Markerless human pose estimation for biomedical applications: a survey

Avogaro

Cunico

Rosenhahn

et al. 2023

Front. Comput. Sci.

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“…This finding suggests that interventions that target to increase knee flexion angles may have a higher impact on improving knee joint loading than interventions that target to increase vGRF. Knee flexion angles are also easier to target in a clinic because of the increasing availability of low-cost camera systems that can provide real-time feedback of knee joint angles (31)(32)(33). However, further research is warranted to determine if targeting greater knee flexion angle during gait can improve knee joint loading and cartilage health after ACL reconstruction.…”

Section: Discussionmentioning

confidence: 99%

Mechanical Factors Contributing to Altered Knee Extension Moment during Gait after ACL Reconstruction: A Longitudinal Analysis

Krishnan

Johnson

Palmieri-Smith

2022

Medicine &Amp; Science in Sports &Amp; Exercise

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PurposeThis study aimed to comprehensively examine the extent to which knee flexion angle at initial contact, peak knee flexion angle, and vertical ground reaction force (vGRF) contribute to knee extension moments during gait in individuals with anterior cruciate ligament (ACL) reconstruction.MethodsOverground gait biomechanics were evaluated in 26 participants with ACL reconstruction at three time points (about 2, 4, and 6 months after the surgery). Knee flexion angle at initial contact, peak knee flexion angle, peak vGRF, and peak knee extension moment were calculated for each limb during the early stance phase of gait for all three time points. A change score from baseline (time point 2 − time point 1 and time point 3 − time point 1) along with limb symmetry values (ACL − non-ACL limb values) was also calculated for these variables. Multiple linear regressions utilizing classical and Bayesian interference methods were used to determine the contribution of knee flexion angle and vGRF to knee extension moment during gait.ResultsPeak knee flexion angle and peak vGRF positively contributed to knee extension moment during gait in both the reconstructed (R2 = 0.767, P < 0.001) and nonreconstructed limbs (R2 = 0.815, P < 0.001). Similar results were observed for the symmetry values (R2 = 0.673, P < 0.001) and change scores (R2 = 0.731–0.883; all P < 0.001), except that the changes in knee flexion angle at initial contact also contributed to the model using the change scores in the nonreconstructed limb (time point 2 − time point 1: R2 = 0.844, P < 0.001; time point 3 − time point 1: R2 = 0.883, P < 0.001). Bayesian regression evaluating the likelihood of these prediction models showed that there was decisive evidence favoring the alternative model over the null model (all Bayes factors >1000). Standardized β coefficients indicated that changes in knee flexion angle had a greater impact (>2×) on knee extension moments than vGRF at both time points in both limbs (βvGRF = 0.204–0.309; βkneeflexion = 0.703–0.831).ConclusionsThe findings indicate that both knee flexion angle and peak vGRF positively contribute to altered knee extension moments during gait, but the contribution of knee flexion angle is much greater than vGRF. Therefore, treatment strategies targeting these variables may improve knee loading after ACL reconstruction.

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“…Among instrumented analysis methods, photogrammetric motion capture systems have been widely used due to their ability to provide detailed kinematic and spatiotemporal features of human movement. However, several issues like marker displacement, limited field of view, lighting conditions, expensive equipment, and time‐consuming data processing are associated with photogrammetric systems for gait analysis (Viswakumar et al, 2022). While they offer valuable information, these systems present limitations requiring careful consideration for research or clinical purposes (Wade et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Robotics‐driven gait analysis: Assessing Azure Kinect's performance in in‐lab versus in‐corridor environments

Guffanti,

Brunete,

Hernando

et al. 2024

Journal of Field Robotics

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Gait analysis offers vital insights into human movement, aiding in the diagnosis, treatment, and rehabilitation of various conditions. Analyzing gait in corridors, rather than in lab, provides unique advantages for a more comprehensive understanding of human locomotion. However, limited dedicated technologies constrain gait data analysis in this context. In this study, a markerless gait analysis system using an Azure Kinect sensor mounted on a mobile robot is proposed and validated as a potential solution for gait analysis in corridors. Ten healthy participants (4 males and 6 females) underwent two tests. The first test (5 trials per participant) took place in the laboratory. Here, Azure Kinect performance was validated against a Vicon system, assessing eight gait signals and 22 gait parameters. The second test (2 trials per participant) was performed in the corridors over a 32‐m walking distance to compare this gait pattern with the one developed within the laboratory. The intrasession Intraclass Correlation Coefficient (ICC) reliability for in‐lab experiments was assessed by calculating the ICC between gait cycles captured in each session per participant. Notably, knee flexion/extension (ICC‐0.95), hip flexion/extension (ICC‐0.96), pelvis rotation (ICC‐0.88), and interankle distance (ICC‐0.98) demonstrated excellent reliability with high confidence. Similarly, hip adduction/abduction showed good reliability (ICC‐0.79), while trunk rotation exhibited moderate reliability (ICC‐0.72). In contrast, both trunk tilt (ICC‐0.24) and pelvis tilt (ICC‐0.41) consistently displayed lower reliability. This was observed for both the Vicon and the Azure systems, highlighting the intricate nature of capturing precise data for these specific signals in both systems. Validity outcomes indicated comparable error rates to literature standards ( knee flexion/extension, hip flexion/extension, and hip adduction/abduction), with 11 parameters having no significant differences from Vicon. Comparison of in‐lab and in‐corridor experiments show that individuals exhibit significantly longer stride time (1.10 s vs. 1.05 s), lower pelvis tilt ( vs. ), and lower minimum pelvis rotation ( vs. ) when walking in the laboratory. This study demonstrates promising outcomes in outdoor gait analysis with a robot‐mounted camera, revealing significant distinctions from controlled laboratory evaluations

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Development of a Robust, Simple, and Affordable Human Gait Analysis System Using Bottom-Up Pose Estimation With a Smartphone Camera

Cited by 22 publications

References 28 publications

Markerless human pose estimation for biomedical applications: a survey

Markerless human pose estimation for biomedical applications: a survey

Mechanical Factors Contributing to Altered Knee Extension Moment during Gait after ACL Reconstruction: A Longitudinal Analysis

Robotics‐driven gait analysis: Assessing Azure Kinect's performance in in‐lab versus in‐corridor environments

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