Markerless vs. Marker-Based Gait Analysis: A Proof of Concept Study

Abstract

The analysis of human gait is an important tool in medicine and rehabilitation to evaluate the effects and the progression of neurological diseases resulting in neuromotor disorders. In these fields, the gold standard techniques adopted to perform gait analysis rely on motion capture systems and markers. However, these systems present drawbacks: they are expensive, time consuming and they can affect the naturalness of the motion. For these reasons, in the last few years, considerable effort has been spent to study and implement markerless systems based on videography for gait analysis. Unfortunately, only few studies quantitatively compare the differences between markerless and marker-based systems in 3D settings. This work presented a new RGB video-based markerless system leveraging computer vision and deep learning to perform 3D gait analysis. These results were compared with those obtained by a marker-based motion capture system. To this end, we acquired simultaneously with the two systems a multimodal dataset of 16 people repeatedly walking in an indoor environment. With the two methods we obtained similar spatio-temporal parameters. The joint angles were comparable, except for a slight underestimation of the maximum flexion for ankle and knee angles. Taking together these results highlighted the possibility to adopt markerless technique for gait analysis.

Automatic extraction of upper-limb kinematic activity using deep learning-based markerless tracking during deep brain stimulation implantation for Parkinson’s disease: A proof of concept study

“…For example, neurologists have employed DeepLabCut in post-stroke patients to conduct gait analyses with promising accuracy [ 60 ]. Our contributions could elaborate on this use by categorizing aspects of gait based on Euclidean distance movement epochs, thereby presenting an entirely objective option that performs well even in the absence of strictly-controlled room conditions required for videotaped observational gait analysis [ 25 ]. In pediatric patients, rehabilitation scientists have used a similar methodology to evaluate dyskinetic cerebral palsy symptoms, which could also be expanded upon by our findings [ 61 ].…”

“…This markerless approach exhibits acceptable or better effectiveness at motion tracking in human-based studies when compared to inertial and electromagnetic sensors [ 23 ] and infrared physical markers [ 24 – 27 ]. Several recent studies have employed DLC in clinical settings to track joints of the body, with evidence of reliability [ 25 , 28 ], further bolstering its clinical applicability and utility. DLC has repeatedly demonstrated better performance and greater versatility in markerless label placement compared to other markerless methods like OpenPose and LEAP, which do not permit network retraining and comparatively have shallower, less robust networks [ 26 , 27 , 29 , 30 ].…”

“…Additionally, it is adaptable to low camera resolutions and variable light conditions [20][21][22]. This markerless approach exhibits acceptable or better effectiveness at motion tracking in human-based studies when compared to inertial and electromagnetic sensors [23] and infrared physical markers [24][25][26][27]. Several recent studies have employed DLC in clinical settings to track joints of the body, with evidence of reliability [25,28], further bolstering its clinical applicability and utility.…”

Towards Accurate Computer Vision-Based Marker Less Human Joint Localization for Rehabilitation Purposes

“…The potential for providing cost-effective and easy-to-use solutions for home environments, marker-less CV solutions for rehabilitations applications have been of interest in the field of TR [9]. Recently, a comparison of marker-less vs. marker-based solutions for Gait analysis through a proof of concept study has been presented in [10]. The authors propose a multi RGB camera neural network based system for detecting and localizing key points of the human body.…”

Intelligent Sensors for Human Motion Analysis

“…In turn, Moro et al [5] presented an approach for markerless gait analysis based on RGB video data and deep learning algorithms. To detect 2D feature points in the image, the AdaFuse algorithm was used.…”