Internet-of-Things-Enabled Markerless Running Gait Assessment from a Single Smartphone Camera

Abstract

Running gait assessment is essential for the development of technical optimization strategies as well as to inform injury prevention and rehabilitation. Currently, running gait assessment relies on (i) visual assessment, exhibiting subjectivity and limited reliability, or (ii) use of instrumented approaches, which often carry high costs and can be intrusive due to the attachment of equipment to the body. Here, the use of an IoT-enabled markerless computer vision smartphone application based upon Google’s pose estimation model BlazePose was evaluated for running gait assessment for use in low-resource settings. That human pose estimation architecture was used to extract contact time, swing time, step time, knee flexion angle, and foot strike location from a large cohort of runners. The gold-standard Vicon 3D motion capture system was used as a reference. The proposed approach performs robustly, demonstrating good (ICC(2,1) > 0.75) to excellent (ICC(2,1) > 0.90) agreement in all running gait outcomes. Additionally, temporal outcomes exhibit low mean error (0.01–0.014 s) in left foot outcomes. However, there are some discrepancies in right foot outcomes, due to occlusion. This study demonstrates that the proposed low-cost and markerless system provides accurate running gait assessment outcomes. The approach may help routine running gait assessment in low-resource environments.

IoT-Enabled Gait Assessment: The Next Step for Habitual Monitoring

“…Computer vision (CV) is generally considered in two categories: 2D (a regular camera) and 3D (use of camera in conjunction with technologies such as infrared, e.g., Microsoft Kinect) [153]. CV has shown utility in providing insight into a variety of healthcare applications such as remote monitoring of vulnerable cohorts [154], fall detection [155,156], and running [157]. Typically, 3D-based CV is considered the gold standard in person identification due to its understanding of depth perception [158].…”

Community-Based and Everyday Life Gait Analysis: Approach to an Automatic Balance Assessment and Fall Risk Prediction in the Elderly

“…Commonly, the identification of these variables and parameters in clinical contexts is carried out through scales and tests [12,13]. Nowadays, due to the subjectivity of these tools [14], the use of instrumentation and/or technologies to increase their predictive value and objectivity has progressively been associated with their use [15,16].…”

“…It is important to note that the majority of these tools are implemented in safe and controlled clinical contexts [14]. However, it has been observed that falls are influenced by various factors, with the environment being a significant determinant in their performance [16,17].…”

“…While the environment alone is not a cause of falls, there is an interaction between the characteristics of the environment that can be dangerous and the intrinsic risk factors of older people [17]. Therefore, restricting evaluations to clinical contexts does not allow for the identification of real issues that may arise according to the different characteristics of the environment and community spaces [14].…”

A webcam-based machine learning approach for three-dimensional range of motion evaluation

“…Compared to other open-sourced 3D pose estimation libraries, such as OpenPose [19,25], BlazePose is computationally lightweight and can be deployed across various platforms, such as in a web browser as a JavaScript application. As a result of its versatility, BlazePose and other video-based pose estimation libraries have been used by researchers to develop different applications, including in the context of sports for movement abnormality detection [26], gait assessment [27], hypermobility assessment [28], yoga training [29], postural disorder monitoring for Parkinson's patients, [30] and spinal dysfunction risk estimation [31]. However, studies that explicitly examine the effectiveness of applying opensourced 3D pose-estimation library for range of motion evaluation remain scarce [32].…”