Objectives: Rehabilitation progression and return-to-play (RTP) guidelines vary greatly and are predominantly based on expert opinion and subjective criteria. Evaluating joint acceleration and forces in a biomechanical laboratory setting require substantial resources and are not feasible within a clinical setting for a multitude of patients. Video motion capture systems on smartphone devices offer a modern and implementable biomechanical assessment capability. Our purpose is to (1) show the in-clinic practicality of an AI-driven smartphone motion capture system for analyzing human motion, (2) identify movements and biomechanical parameters best correlating with clinical evaluation, and lastly predicting rehabilitation progression and return-to-play. We hypothesize that the smartphone motion capture system OpenCap is easy implementable in clinic and can identify biomechanical movement parameters which will streamline rehabilitation progression and RTP following various sports injuries and surgeries. This study is part of the first phase of the financially supported AOSSM Playmaker Grant research project. Methods: Healthy controls and participants with various musculoskeletal conditions (e.g. nonoperative treated anterior knee pain, post ACL reconstruction, knee osteoarthritis) were recruited. Participants performed defined movements that were captured with the smartphone-based 3-camera video motion capture system OpenCap in the outpatient clinical setting. OpenCap is a smartphone-based open-source software for estimating the kinematics and kinetics of human movement. Patients were grouped into three categories, easy, intermediate, and hard, based on the quality of performed movements, as deemed by the research team, the patients’ self-assessments, and restrictions set by the treating physician. The advanced groups completed quantitatively more and qualitatively higher joint loading movements. All movements were performed three times and during analysis averaged. The easy testbattery consisted of the timed up-and-go test, five-second slow sit-to-stand test and a 10 second one-leg lift for balance. The intermediate group additionally performed 2-legged squats and 1-legged pistol squats. The hard group performed on top also single leg Romanian deadlifts, vertical hop tests for height, step-down tests and drop jumps. OpenCap recorded the movement and computed positions of anatomic landmarks, joint angles, and joint forces during the movements (Figure 1). The time of motion capture system setup and movement completion were recorded. Various biomechanical parameters were explored and compared between groups to identify the best biomechanical parameters and movements allowing group distinguishment. Results: 100 participants visiting a tertiary orthopaedic surgery clinic were included, consisting of 20 healthy controls and 80 patients with various nonoperative and surgical treated pathologies. The cohort consists of 51% females and has an average age of 41.4 years. All participants completed the easy test battery, 53% the intermediate test battery, and 22% the hard test battery. The average setup time was 5.3 minutes, including acquiring patients’ age, height, and weight necessary for the musculoskeletal model and calibration, but excluding obtaining an informed consent. The movement capturing time differed between testbattery groups and ranged from 3 to 17 minutes (average easy 3.6 min, intermediate 5.2 min, hard 11.3 min). Setup time was reduced during the continuation of the study from 9.2 min in the first 20 participants to 3.4min in the last 20 participants. No injuries occurred. In our exploratory analysis, hip flexion, hip adduction, and hip rotation most clearly displayed differences between the easy and intermediate classified groups from the hard motion participants. Comparing the minimum hip flexion between the groups during the slow sit-to-stand test, the easy and intermediate group showed a 5° increased hip flexion (easy p=0.000, intermediate p=0.004) compared to the hard group. During the single leg squat the maximum hip flexion was 15° less in the intermediate compared to the hard group (p=0.009). During the lateral step-down test, the intermediate group showed a minimum 17° decreased hip internal rotation in comparison to the hard group (p=0.015). The slow sit to stand test, timed up and go test, one leg lift, single leg squat and the lateral step-down test demonstrated best discrimination between the groups. Conclusions: The video motion capture system OpenCap can be easily implemented into a clinical setting due to the easy setup and expeditious manner in which the measurements are taken. Testing time is mostly dependent on the extent of measured movements. Moreover, biomechanical parameters as assessed by OpenCap allow differentiation between physical capability groups, compared to clinically assessed. The single-legged movements, the slow sit-to-stand test and the timed-up-and-go-test are most effective in revealing these biomechanical differences. The biomechanical parameters of the hip, including its flexion, adduction, and rotation, reveal these differences most clearly. The ultimate goal is to provide a clinically friendly, rapid, inexpensive, portable, and objective assessment tool to facilitate rehabilitation and return to sport following various injuries and surgeries.
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Oct 23, 2024
Karim Samir + 6
Open Access