DOI: 10.6084/m9.figshare.25343671.v4
title: Dataset for: Evidence of sensory error threshold in triggering locomotor adaptations in humans

description: <b>Task:</b> 23 healthy adults (25.0 ± 6.2 y.o., 12 males, 11 females) with no known neurological disease or persistent musculoskeletal injury were divided into two groups: (i) Group 1 (N=11) to perform the walking task with only a kinematic constraint, and (ii) Group 2 (N=12) to perform the walking task with both a kinematic constraint and asymmetric limb loading. The participants walked on the instrumented treadmill with ground reaction force sensors (Bertec, Columbus, OH) with each session consisting of three trial types: control, adaptation, and washout. The control trials documented baseline gait pattern for five minutes of walking at 1 m/s. During the asymmetric trials, participants walked with the device on the right leg constraining the stride length of the left limb. Participants were instructed to not touch the device with the left leg while walking for ten minutes. Specifically for Group 2, participants were explicitly instructed to unload their constrained leg, and verbal feedback was given during the task execution when the unloading was not evident from the real-time vertical component of ground reaction force. The washout period allowed the observation of aftereffects after the removal of the constraints.<b>Dataset: </b>The dataset consists of two analyses: (i) comparing the cumulative sum of ground reaction forces (GRFs) for each leg during the stance phase between Groups 1 and 2, and (ii) comparing the asymmetry index between Groups 1 and 2. For the first analysis, the data can be found in the file <i>nTable_CS.csv</i>, see below for more details. A supporting figure displaying example GRFs for each group can be made using the data found in file <i>nTable_ex_GRF.csv</i>, see below for more details. For the second analysis, the data can be found in the file <i>nTable_AI.csv</i>, see below for more details. To calculate the asymmetry index (AI), use the following equation: AI = (nDS_Left_Leading_s - nDS_Left_Trailing_s) / (nDS_Left_Leading_s + nDS_Left_Trailing_s). All data is in csv files, so you can easily import them into your software of choice. Scripts are included for our analysis done in MATLAB, see below for more details.<br><i>nTable_CS.csv</i>Columns:idGroup -- group identifier (either 1 or 2)sSession -- session identifier (S + #)sCondition -- condition identifier (either control, asymL (adaptation), or washout)sLeg -- leg identifier (either L (left) or R (right)nCumulSum -- cumulative sum of GRFs during the stance phase, normalized to the participant's weight and the number of steps in each condition<br><i>nTable_ex_GRF.csv</i>Columns:idGroup -- group identifier (either 1 or 2)sSignal -- signal name (either LFz (GRFs for the left leg) or RFz (GRFs for the right leg))nData_avg -- data for the average values across the period, normalized to the participant's weightnData_sd -- data for the standard deviation across the period<br><i>nTable_AI.csv</i>Columns:idGroup -- group identifier (either 1 or 2)sSession -- session identifier (S + #)sCondition -- condition identifier (either Control, Adapt (i.e., adaptation), or Post (i.e., washout))nDS_Left_Leading_s -- left leading double stance (i.e., the phase of the gait cycle where both leg are in contact with the ground and the left leg is in the front) durations in secondsnDS_Left_Trailing_s -- left trailing double stance (i.e., the phase of the gait cycle where both leg are in contact with the ground and the left leg is in the back) durations in seconds<br>MATLAB Scripts:main_v9_figshare.m -- the main script to run the entire analysisplotExampleGRF.m -- function to plot an example GRF profile for a given groupplotLoading_v5.m -- function to plot the box plots and perform the stats for the loading analysistestHypothesis_v6.m -- function to plot the box plots and perform the stats for the aftereffects analysisgetAvgBehavior_v5.m -- function to plot the average behavior regarding asymmetry for a given groupplotAvgSignalwSD.m -- function to plot an average signal with standard deviation lines above and below it<br>