Evaluating Optimal Weight Design Parameters of a Hip Exoskeleton Designed for Rehabilitation.
Phase: NA
Conditions: Healthy, Gait, Frontal
Sponsor: University of Texas at Austin

 
 
 
 
 
 
 
 
Study Title: Optimization of Hip-exoskeleton Weight Attributes 
 
NCT number: NCT05120115 
 
Document date: 12/2/2021 
 
 

Participants 
Twenty-one healthy participants (12 males, 9 females, age 26.8 ±5.57 years, body height 172.8 ± 
7.58 cm, body weight 65.9 ± 8.53 kg) were recruited for this study. Exclusion criteria included 
relevant musculoskeletal injuries, abnormal gait deviations, and weight bearing restrictions. Prior 
to the experiment, participants had their footedness evaluated to ensure that they were right 
footed. The University of Texas Institutional Review Board approved the experimental protocol 
and subjects were provided informed written consent. 
Experimental setup and protocol 
Participants were tasked with traversing a treadmill while bearing scuba weights secured with a 
diving belt (Scuba Choice, Los Angeles CA). Weights of 4 kg, 6 kg, and 8 kg were suspended on 
the pelvis in three configurations: weight evenly distributed between both anterior iliac 
crests (Bilateral, BI), weight on the 4th lumbar vertebrae (Lumbar, L), and weight on the non-
dominant left anterior iliac crest (Unilateral, UNI). Fig. 1 illustrates these placements. 
We collected Motion capture marker data with a 13-camera motion capture system and 36 active 
markers attached to the lower body and torso segment (Phase Space, San Leandro, CA). Ground 
reaction forces (GRF) were measured through force plates in an instrumented split-belt treadmill 
(Bertec, Columbus, OH). Surface electromyography data (EMG) (Bortec, Calgary, AL) were 
collected from the rectus femoris (RF), medial hamstring (MH), tibialis anterior (TA), and 
lateral gastrocnemius (GAS) of each leg. 

 
Fig. 1. Weight placements during an experiment. Also shown are the motion capture LEDs, 
EMG electrodes, and split belt treadmill. 
 
Participants walked for 1m30s at a speed of 1.1 m/s for every combination of weight (4, 6 and 8 
kg), placement (BI, UNI, L), and no weight (NW) in pseudo-randomized order. The participants 
were exposed to each condition two times, for 20 total trials. After every five trials subjects were 
given a break of 2 min. 
Data processing 
Using GRFs, we defined heel strike and toe off events, which were then used to identify gait 
phases. Starting from heel strike, the gait phases were defined as initial stance, mid stance, late 
stance, and swing, ending the gait cycle with the proceeding heel strike. For each trial, we 

ignored the first 30 s to account for familiarization to the condition. For outlier detection, we 
removed an individual stride if the waveform exceeded 2 inter-quartile-ranges from the median 
waveform for more than 40% of the gait cycle. The mean waveform from the last 30 strides was 
evaluated. 
We recorded all biometric data at 960 Hz. First, all these signals were downsampled to 480 Hz. 
Force plate data were low-pass filtered with 4th order Butterworth filter at 20 Hz. Motion capture 
data were low-pass filtered with 4th order Butterworth filter at 6 Hz. Surface EMG signals were 
processed with a high-pass filter of 40 Hz, demeaned, rectified, and low-pass filtered at 4 Hz. 
We used motion capture data and GRFs with an open-source musculoskeletal simulation 
software, OpenSim 4.3. We scaled a musculoskeletal model to match the anthropometry of each 
subject, and then performed inverse kinematics and dynamics for joint angles and moments, 
respectively. 
Outcome measures 
We contextualized the joint motion for the hips and knees as Range-of-Motion (ROM), the 
difference between the maximum and minimum joint angles within a given period. We analyzed 
the effect of weight on the hip and knee sagittal plane motion during initial stance phase. We 
quantified pelvic tilt and obliquity as the average position within a gait phase. 
 
EMG signals were normalized via the mean-dynamic method centering the EMG signal around 
1. For data analysis, the EMG signal was integrated along each gait phase to calculate the 
integrated EMG (iEMG) values. We analyzed the RF muscle activation during initial stance and 
the GAS muscle activation during late stance. 
 

Statistical analysis 
R 4.1.1 (2021 The R Foundation for Statistical Computing) was used for statistical analysis. One 
subject was removed from the dataset due to technical errors with recording the data. 
We used a linear mixed regression model (lme4 1.1.27.1  and lmerTest 3.1.3) with two fixed 
effects (weight and placement) along with a no weight condition and one random effect (subject) 
and α<0.05. A Tukey Honestly Significant Difference post-hoc test was performed to determine 
pairwise differences between weights, placements, and their interactive effects.