ABSTRACT
As markerless motion capture is increasingly used to measure 3-dimensional human pose, it is important to understand how markerless results can be interpreted alongside historical marker-based data and how they are impacted by clothing. We compared concurrent running kinematics and kinetics between marker-based and markerless motion capture, and between 2 markerless clothing conditions. Thirty adults ran on an instrumented treadmill wearing motion capture clothing while concurrent marker-based and markerless data were recorded, and ran a second time wearing athletic clothing (shorts and t-shirt) while markerless data were recorded. Differences calculated between the concurrent signals from both systems, and also between each participant's mean signals from both asynchronous clothing conditions were summarized across all participants using root mean square differences. Most kinematic and kinetic signals were visually consistent between systems and markerless clothing conditions. Between systems, joint center positions differed by 3 cm or less, sagittal plane joint angles differed by 5° or less, and frontal and transverse plane angles differed by 5° to 10°. Joint moments differed by 0.3 N·m/kg or less between systems. Differences were sensitive to segment coordinate system definitions, highlighting the effects of these definitions when comparing against historical data or other motion capture modalities.
Subject(s)
Motion Capture , Running , Adult , Humans , Biomechanical Phenomena , Knee Joint , Clothing , MotionABSTRACT
Markerless motion capture allows whole-body movements to be captured without the need for physical markers to be placed on the body. This enables motion capture analyses to be conducted in more ecologically valid environments. However, the influences of varied clothing on video-based markerless motion capture assessments remain largely unexplored. This study investigated two types of clothing conditions, "Sport" (gym shirt and shorts) and "Street" (unrestricted casual clothing), on gait parameters during overground walking by 29 participants at self-selected speeds using markerless motion capture. Segment lengths, gait spatiotemporal parameters, and lower-limb kinematics were compared between the two clothing conditions. Mean differences in segment length for the forearm, upper arm, thigh, and shank between clothing conditions ranged from 0.2 cm for the forearm to 0.9 cm for the thigh (p < 0.05 for thigh and shank) but below typical marker placement errors (1 - 2 cm). Seven out of 9 gait spatiotemporal parameters demonstrated statistically significant differences between clothing conditions (p < 0.05), however, these differences were approximately ten times smaller than minimal detectable changes in movement-related pathologies including multiple sclerosis and cerebral palsy. Hip, knee, and ankle joint angle root-mean-square deviation values averaged 2.6° and were comparable to previously reported average inter-session variability for this markerless system (2.8°). The results indicate that clothing, a potential limiting factor in markerless motion capture performance, would negligibly alter meaningful clinical interpretations under the conditions investigated.
Subject(s)
Gait , Walking , Biomechanical Phenomena , Clothing , Humans , MotionABSTRACT
Total knee arthroplasty (TKA) surgery improves knee joint kinematics and kinetics during gait for most patients, but a lack of evidence exists for the level and incidence of improvement that is achieved. The objective of this study was to quantify patient-specific improvements in knee biomechanics relative to osteoarthritis (OA) severity levels. Seventy-two patients underwent 3-dimensional (3D) gait analysis before and 1 year after TKA surgery, as well as 72 asymptomatic adults and 72 with moderate knee OA. A combination of principal component analysis and discriminant analyses were used to categorize knee joint biomechanics for patients before and after surgery relative to asymptomatic, moderate, and severe OA. Post-TKA, 63% were categorized with knee biomechanics consistent with moderate OA, 29% with severe OA, and 8% asymptomatic. The magnitude and pattern of the knee adduction moment and angle (frontal plane features) were the most significant contributors in discriminating between pre-TKA and post-TKA knee biomechanics. Standard of care TKA improves knee biomechanics during gait to levels most consistent with moderate knee OA and predominately targets frontal plane features. These results provide evidence for the level of improvement in knee biomechanics that can be expected following surgery and highlight the biomechanics most targeted by surgery.
Subject(s)
Arthroplasty, Replacement, Knee , Osteoarthritis, Knee , Adult , Biomechanical Phenomena , Gait , Humans , Knee Joint/surgery , Osteoarthritis, Knee/surgeryABSTRACT
There is little information on the reliability of inertial measurement units for capturing impact load metrics during sport-specific movements. The purpose of this study is to determine the reliability of the Blue Trident IMU sensors in measuring impact load, step count and cumulative bone stimulus during a series of soccer-related tasks. Ten healthy recreational soccer players (age: 27.9 ± 2.18; height: 1.77 ± 0.10 m; mass: 79.02 ± 13.07 kg) volunteered for a 3-visit study and performed 4 tasks. Bilateral impact load, total number of steps and cumulative bone stimulus during the tasks were collected. Data were sampled using a dual-g sensor. Intraclass correlation coefficients (ICC3,1) with 95% confidence intervals assessed between-day reliability. Impact load (0.58-0.89) and cumulative bone stimulus (0.90-0.97) had good to excellent reliability across tasks. ICC values for right/left step count were good to excellent during acceleration-deceleration (0.728-0.837), change direction (0.734-0.955) and plant/cut manoeuvres (0.701-0.866) and fair to good during the ball kick (0.588-0.683). This suggests that wearable sensors can reliably measure the cumulative impact load during outdoor functional movements; however, kicking manoeuvres are less reliable. Measuring impact load in the field expands the ability to capture more ecologically valid data.