MARKERLESS MOTION CAPTURE IS SENSITIVE TO BIOMECHANICAL CHANGES IN GAIT KINEMATICS FOR ORTHOPAEDIC PATIENTS

The use of biomechanical data to inform clinical decision making in orthopaedics has been rare, largely due to practical limitations in obtaining motion capture data. Traditional marker-based motion capture systems are resource- and time-intensive with significant data collection burden for patients. Markerless motion capture technology using computer vision and machine learning approaches to obtain biomechanical data from standard video images offers significant advantages for ease of data collection, but must demonstrate the ability to produce relevant outcome metrics in a clinical population. The objectives of our pilot study were to (1) determine the feasibility of using markerless motion capture technology to record kinematics during a variety of tasks in an orthopaedic population by examining the task completion by participants and ability of the markerless system to track body segments and (2) to compare gait waveform data collected on a severe knee osteoarthritis (OA) population to a control group, to replaced knee joints, and to historical marker-based data for similar groups. Orthopaedic patients with knee OA were recruited directly from an orthopaedic assessment clinic after referral to an orthopaedic surgeon for total knee arthroplasty. Participants wore the clothes and shoes they had worn that day. Severe OA patients (n=77) performed functional tasks during markerless motion capture: timed up-and-go (TUG), stair ascent and descent, quiet standing (balance), walking at self-selected speed, and fast walking. Previous knee replacements in the contralateral leg were analyzed separately (n=17). The control group consisted of members of the community over 50 years of age (n=29). Markerless motion capture was performed using 8 commercially available video cameras (Sony RX0-II) recorded at 60 Hz and processed using Theia3D (Theia Markerless Inc.). Historical data using a marker-based motion capture system for severe OA post-knee replacement, and control subjects were obtained from a previous publication. The severe OA group was 56% female with mean age 69 years (SD 8). Kinematic data from the markerless system was calculated for all participants for all completed tasks with no discernable tracking issues. The control group (mean age 58 years, 59% female) had a self-selected walking speed of 1.3 m/s compared to 0.9 m/s for the severe OA group. Joint angle waveform data captured with the markerless system show similar patterns to historical data collected with marker-based motion capture for severe OA, post-knee replacement, and control groups (Figure 1). This study demonstrated feasibility of using markerless motion capture on an orthopaedic population directly from a clinic visit with no restrictions on clothing. Kinematic data from markerless motion capture exhibited expected kinematic deviations based on historical marker-based gait data on similar populations. The ease of data collection and the standardized calculation of biomechanical metrics have important implications for clinical implementation as well as longitudinal and multi-centre studies. For any figures or tables, please contact the authors directly.