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191 protocols using visual3d

1

Barbell Lift Kinematic Analysis

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Barbell displacement-time data were exported to Visual 3D (V6.01.22, C-Motion, Rockville, USA), and barbell velocity was calculated using the finite difference method in Visual 3D. Displacement data were filtered using a fourth order, zero-lag, Butterworth low-pass filter with a cut-off frequency of 12 Hz. Data were visually inspected to assess the effect that different cut-off frequencies (6-20 Hz) had on vertical velocity and 12 Hz was selected because lower cut-off frequencies attenuated peak values. The start of the concentric phase of each repetition was determined as the first frame in which the marker displayed a positive vertical velocity following the eccentric phase (bar lowering), and the end of the concentric phase was identified as the first frame in which the marker displayed a negative vertical velocity after the end of the concentric lifting phase. Peak vertical velocity and mean vertical velocity were subsequently determined from the highest values in the concentric phase and by averaging data over the concentric phase, respectively.
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2

Biomechanics of Golf Swing Kinematics

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Static, ROM and RVJ trials were reconstructed and labelled, and gaps filled in VICON Nexus (v2.10.2, VICON, Oxford, UK) with C3D files exported for processing in Visual3D (v6.0, c-motion, MD, USA). Functional joint centres (hip and knee) were calculated using the ROM trials in Visual3D (C-Motion, 2020). A fourth order zero-lag low-pass Butterworth filter with 15 Hz cut-off frequency was used to smooth marker trajectory data. GRF (Fx, Fy, Fz), segment angular kinematics (thorax and pelvis) and joint angular kinematics (ankle, knee and hip) and joint kinetics (moment and power for the ankle, knee and hip) for the three axes of rotation (X ¼ medial-lateral; Y ¼ anterior-posterior; Z ¼ longitudinal) were calculated. The thorax segment was defined as the axial rotation component of thorax motion relative to pelvis motion, otherwise known as 'X-Factor' in golf (Hume et al., 2005) . Two key events were determined; initial contact (INC), defined as vertical GRF >10 N, and initial impact (INI), defined as 100 ms after INC (Norcross et al., 2013) . Data was collected at these two time points for statistical analysis. Peak GRF metric for all three axes was exported for each trial. The data for the GRF, segment and joint angular kinematics and joint kinetics across the five trials were averaged for each session.
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3

Center of Pressure Trajectory Analysis

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The COP trajectory was resolved into a virtual foot local coordinate system from contact with the force platform (Visual3D, C-Motion Inc., USA). Specifically, the forward progression COP was normalised (arbitrary unit, a.u) by the distance along the anteriorposterior (A-P) axis from the proximal end of the foot segment (ankle joint) to the 2 nd metatarsal head (distal joint centre) (O'Connell et al., 1998) . This meant that A-P COP range of motion was quantified on the order of -1 to 2, where a negative value indicates that COP is behind the ankle joint centre and a value > 1 reflects COP ahead of the metatarsals (O'Connell et al., 1998) . Similarly, the medio-lateral (M-L) COP was normalised (a.u) by its distance along the distal radius of the foot segment (1 st to 5 th metatarsal head) with respect to the longitudinal axis of the foot segment. An M-L COP equal to zero reflects a position located on the A-P axis, whereas a positive value indicates a laterally-directed trajectory (Visual3D, C-Motion Inc., USA). The data were expressed relative to subdivisions of stance phase, representing early (0-33%; COP33), mid-(34-66%; COP66) and late stance (67-100%; COP100) regions (Chang et al., 2008) .
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4

Kinematic Analysis of Gait Variability

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Data from at least three trials were averaged (five where possible, average number of trials = 4.7 ± 0.6) for the consistent dimension condition and the first trial was used for the inconsistent higher rise condition, trials with incomplete force data or long periods of occluded markers were not included in the analysis. Kinetic and kinematic data were filtered using a low-pass fourth order Butterworth filter with a cut-off frequency of 6 Hz in Visual 3D (version 6.01.043 Visual3D, C-Motion, Germantown, USA). Foot centre of mass (CoM) was calculated according to Dempster's regression equations (Dempster, 1955) and were individualised to a participants height and mass as described by Hanavan (Hanavan, 1964) .
Foot clearance was chosen as the outcome measure to quantify the risk of toe or heel catch. Foot contact length was chosen as the outcome measure to quantify the risk of over or under stepping.
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5

Barefoot Gait Kinematics and Kinetics

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Participants conducted a series of barefoot walking trials at a self-selected speed and without the use of assistive devices along the length of a raised wooden walkway (9.9 x 1.8 m, expanded to 9.9 x 4.2 m in 2010) under the supervision of a physiotherapist. First, reflective spherical markers were affixed to the lower extremities and trunk of the participants using a modified Helen-Hayes marker set. Gait kinematics were then recorded using an 8-camera optical motion analysis system (Motion Analysis, USA) that was expanded to 12-cameras in 2010. Ground reaction forces were collected synchronously (1200 Hz) with gait kinematics (120 Hz) using two OR6-6 force plates (AMTI, USA), expanded to four OR6-6 force plates in 2010, embedded in the walkway. Five successful gait repetitions were identified for each participant. Successful gait repetitions consisted of a completely visible marker set, contact of one foot only with the center of one of the force plates, and maintenance of expected gait dynamics, verified using visual inspection. Marker data were tracked using EVART (Motion Analysis, USA). Stance phase hip, knee and ankle joint angles were computed using Visual-3D (C-Motion, USA) and mean walking speeds were determined in Visual-3D as the mean stride length over stride time across five gait repetitions.
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6

Entropy Analysis of Lower Limb Kinematics

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Sagittal hip, knee and ankle joint angles were extracted from the kinematic data using Visual 3D (c-Motion, Inc., Germantown, MD). The joint angles from 400, 300, 200, 100 and 50 consecutive strides were extracted. For each data length, the original time series with a sampling frequency of 480Hz were additionally down sampled to 240, 120 and 60Hz. SaEn was calculated from the joint angle time series based on the algorithm presented by Richman and Moorman [14 ] and defined as the negative logarithm for the conditional probability that a series of data points within a certain distance, m, would be repeated within the distance m+1 (equation 1).
Where N is the number of data points in the time series, A is the number of similar vector lengths (m+1) falling within a relative tolerance limit (r times standard deviation of the time series) and B is the number of similar vector lengths (m) falling within the tolerance limit [24 (link)]. To investigate the effect of parameter choice, SaEn calculations were performed with m = 2 and 3 and r = 0.1, 0.15, 0.2, 0.25 and 0.3. The results from the analysis using m=2 and r=0.2 is presented below and remaining results are presented in a supplementary material available at the University of Nebraska, Omaha Digital Commons (https://digitalcommons.unomaha.edu).
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7

Gait Kinematics and Perturbation Analysis

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Data were initially processed using Visual3D (Version 6.03.6 C-Motion, Germantown, Maryland, USA). Marker positions were filtered using a fourth order dual-pass zero phase lag Butterworth filter with a cut-off frequency of 6 Hz. Left and right heel marker velocities were used to determine timing of heel strikes. Heel strike was defined as the time after toe-off when heel marker velocity was first <0.1 m/s. SL and SW were defined as the absolute anterior-posterior (AP) and ML distances, respectively, between the heel markers at heel strike. ST was defined as the absolute difference between the time of consecutive heel strikes. Custom MATLAB routines (R2014a, The Mathworks, Inc., Natick, Massachusetts, United States) were used for the remainder of data processing. Perturbation onset time was defined as the time when the motion base acceleration exceeded 0.1m/s 2 .
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8

Kinematic and EMG Analysis of Dominant Limb

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The dominant (preferred kicking) limb was selected for data collection. Prior to electrode placement, the skin was shaved abraded and cleaned with isopropyl alcohol. Parallel-bar EMG Sensors (DE-2.1, DELSYS, USA) were then placed over the BF and ST in accordance to SENIAM guidelines (Hermens et al. 2000). EMG signals were amplified (1 k gain) via a Delsys Bagnoli system (Delsys Inc. Boston, MA, USA) with a bandwidth of 20–450 Hz. The common mode rejection rate and input impedance were -92 dB and >1015Ω, respectively. Data was collected at 1000 Hz synchronously with the kinematic data.
Lower extremity planar kinematics was monitored using a 10-camera retroreflective system at 200 Hz (Oqus 3, Qualisys Gothenburg, Sweden). Four retroreflective soft markers (19 mm) were placed over the lateral malleolus, lateral knee joint, greater trochanter and acromion process of the dominant limb. Following tracking, kinematic and sEMG data were exported for analysis to Visual 3D (C-Motion Inc. USA).
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9

Kinematic Analysis of Upper Limb Reaching Task

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The kinematic data was processed through Qualisys Track Manager (Qualisys AB, Gothenburg, Sweden) and Visual3D (C-Motion, Inc., Germantown, USA) software following the International Society of Biomechanics recommendations and the methods of previous studies52 (link). The movement trajectory and force plate data were low-pass Butterworth filtered with a cut of frequency of 6 Hz and 20 Hz, respectively.
The “onset” of the task, designated by T0, was defined as the time when the tangential velocity of the hand exceed 2% of the maximum velocity in the reaching phase53 (link). The “reaching” phase end (beginning of the return to start position) was defined as the instant when the linear velocity of the hand crossed the zero value downwards in the sagittal plane.
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10

Analyzing Stance Phase Dynamics

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The stance phase of running inclusive of the right heel strike to toe-off was analyzed in this study. A customized function in Visual 3D (c-motion Inc., Germantown, MD, USA) was applied to process and quantify kinematic and kinetic variables in the stance phase of the ankle, knee and hip joints using C3D files generated by Vicon Nexus Software. The data of kinematics and kinetics were filtered by 10 Hz and 20 Hz fourth-order zero-phase low pass Butterworth filter for the de-noising process of marker trajectories [28 (link)]. The standard inverse dynamic method was used to calculate the internal joint moments and joint powers. The joint kinetic data were normalized for the participant’s body mass. Joint kinematic and kinetic data were time normalized to the stance phase (101 data points per stance phase) by Matlab version 2019b (The Math Works, Natick, MA, USA).
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