Instrumented treadmill

Whole body kinematics and ground reaction forces (GRF) were collected and processed using a previously described protocol (Stiffler-Joachim et al., 2019 (link)). Briefly, 42 reflective markers were placed on each runner, with 23 being placed on anatomical landmarks. Gait assessments were completed on a treadmill using a standardized protocol. Each runner first walked on the treadmill to acclimate to the testing setup, then speed was gradually increased to 4.47 m·s⁻¹. Data were recorded for 15 seconds at 4.47 m·s⁻¹ after the runner had acclimated for 30 seconds. Kinematic data were recorded at 200 Hz using a passive motion capture system (Motion Analysis Corporation, Santa Rosa, CA) and GRF were recorded synchronously at 2000 Hz using an instrumented treadmill (Bertec Corporation, Columbus, OH). The body was modeled in SIMM 4.0 (MusculoGraphics, Inc., Santa Rosa, CA) as a 14-segment, 31 degree-of-freedom articulated linkage, with body segments scaled individually using the runner’s height, mass, and segment lengths. Standard inverse kinematics and dynamics analyses were used to estimate joint kinematics and kinetics (Stiffler-Joachim et al., 2021 (link)). Kinetics were calculated using kinematic and GRF data filtered with a cutoff frequency of 12 Hz, while GRF metrics were derived from GRF data that were low-pass filtered with a cutoff frequency of 50 Hz.

Ten healthy subjects (age: 23.3 ± 2.5 years; mass: 75.2 ± 18.2; five male and five female) were recruited to participate in this study approved by the University of Delaware Institutional Review Board (approval number 943311). Each subject performed calibration trials and walking trials on an instrumented treadmill (Bertec Corp, Worthington, OH). Two calibration trials were recorded, one per sensor, in which the subject repeated three “steps.” Each “step” involved slowly transitioning one’s weight onto only the hindfoot or forefoot and then back off, thus loading the sensor from 0 to BW and back to 0. Six 30-s walking trials were recorded, with two repetitions at three speeds (0.5, 1.0, and 1.5 m/s). For each of the walking trials, we instructed subjects to “stomp” on the sensor after the trial began. This practice provided a detectable event in both the IPS and FP allowed us to synchronize them. During all trials, force data were recorded by the FP at 2,000 Hz and resistance data were recorded by the Arduino at a frequency of 28 ± 1.2 Hz.

We used an established marker-set (Kwon et al., 2012 (link)) and a 10-camera motion capture system (Vicon) to record kinematic data as participants walked on an instrumented treadmill (Bertec). A wireless electromyography system (Trigno, Delsys) recorded activity of the vastus lateralis and lateral hamstring. Muscles were selected on the basis of being a major contributor to knee flexion-extension and EMG electrodes were placed based on SENIAM guidelines. Semitendinosus was targeted for knee flexion but in some cases electrode placement was shifted slightly due to device interference and may have reflected semimembranosus or biceps femoris activity. All EMG signals were verified by manual muscle testing before data collection. Participants walked at their preferred treadmill speed for a minimum of 30 seconds under each condition before data were collected. Kinematic and kinetic data were low-pass filtered at 6 and 12 Hz, respectively. Electromyography data were band-pass filtered (15-380 Hz), full-wave rectified, and low-pass filtered (7 Hz) to create a linear envelope (Lerner et al., 2016 (link)).

We collected 3-dimensional (3D) kinematics and ground reaction forces while subjects walked on an instrumented treadmill (1200 Hz, Bertec Corp., Columbus, OH, USA). The 3D kinematic data were recorded using an 8-camera video system (120 Hz, Motion Analysis Corporation, Santa Rosa, CA, USA) with 46 reflective markers attached on the lower body, trunk and over the C7 vertebra. We used commercial software (Visual3D, C-Motion Inc., Germantown, MD, USA) for initial data processing.