Embedded force platforms

With Institutional Review Board approval and informed consent, CT arthrograms and whole-body motion capture data during walking were collected from five healthy subjects (Table 1) as reported previously.^{20 (link)} CT images included the pelvis and proximal femurs and have been made publicly available (https://mrl.sci.utah.edu/software/hip-data/). To improve segmentation accuracy, CT arthrogram images were up-sampled with a 0.3-mm slice thickness.^{20 (link)} Three-dimensional reconstructions of the pelvis, and bilateral proximal femurs, as well as the acetabular and femoral cartilage for the hip that received the CT arthrogram (i.e., index hip), were generated using Amira (v5.3, Visage Imaging, San Diego, CA). Segmentation was performed using previously validated semi-automated threshold settings.^{4 (link),8 (link)}As reported previously, each subject was instrumented with 21 reflective markers, based on a modified Helen-Hayes marker set, to obtain whole-body kinematics recorded from 10 infrared cameras during overground walking at a self-selected pace on a 10-m walkway (100 Hz sampling frequency) (Vicon, Oxford, UK).^{21 (link)} Ground reaction forces were simultaneously collected from 4 embedded force platforms (1000 Hz sampling frequency) (AMTI, Watertown, MA). Kinematic and kinetic data were filtered with a 4th-order Butterworth filter (6 and 20 Hz cutoff frequencies, respectively).

Biomechanical data acquisition used in this study is similar to previous work(27 (link)) and is briefly described below. A 10-camera motion capture system (VICON, Oxford Metrics, Oxford, UK) was used to obtain 3D position data at a sampling rate of 250 Hz, while ground reaction forces (GRF) were obtained simultaneously using two embedded force platforms (AMTI, Watertown, MA, USA) at a sampling rate of 1000 Hz. Forty-one retroflective markers were placed at anatomical landmarks and used to track segment position, while subjects walked at a fixed speed (1.35m/s) (27 (link)). The Visual3D (C-Motion, Germantown, MD) software was used to calculate lower extremity joint kinematics and moments by using an unweighted least squares method(28 (link)) and inverse dynamics, respectively. Joint angels were normalized to a static calibration trial obtained prior to the gait trials. External hip and knee joint moments were normalized by body mass and height (Nm/kg*m). Joint moment impulses were computed as the integral of the moment with respect to time (Nm*ms/kg*m). GRF data was normalized by body weight (BW). GRF, hip and knee joint kinematic and kinetic variables (Supplementary Table 1) were computed for each subject during four successful gait trials, as described in Samaan et al (23 (link)) and were used to obtain the subject’s average measures which were used for analysis.