DOI: 10.5061/dryad.3j9kd51v9
title: Data from: James-Stein estimator improves accuracy and sample efficiency in human kinematic and metabolic data

description: In the associated manuscript, we show that James-Stein estimator helps
 improve statistical estimates for kinematic and metabolic problems by
 pooling data from multiple subjects. We considered three types of
 estimation problems: foot placement control, metabolic cost of walking in
 circles and metabolic cost of resting. In this dataset, we have shared the
 individual statistical estimates for each subject for each type of data,
 for each trial duration, along with their standard errors. The data in
 MATLAB's .mat format and can be opened with MATLAB, or free software
 such as octave and python.
description: The estimates and standard errors were computed as
 follows: Foot placement control
 sensitivities was estimated from linear regression over motion capture
 data, using methods identical to those in Yang and Srinivasan [1], Perry
 and Srinivasan [2], Joshi and Srinivasan [3], and Seethapathi and
 Srinivasan [4]. The estimates and standard errors were obtained from the
 linear regression software fitlm in MATLAB. The
 resting metabolic data just involves simple averages and are from Hanford
 and Srinivasan [5], Seethapathi and Srinivasan [6], and Brown,
 Seethapathi, and Srinivasan [7]. The estimates and standard errors were
 obtained by elementary formulas for mean and standard error.
 The exponential fit to the walking metabolic rate is performed
 using fminunc in MATLAB to minimize a mean squared error between an
 exponential a0 + a1*exp(-lambda t) and the data [7].
 <strong>References</strong>
 [1] Wang, Yang, and Manoj Srinivasan. "Stepping in the
 direction of the fall: the next foot placement can be predicted from
 current upper body state in steady-state walking." <em>Biology
 letters</em> 10, no. 9 (2014): 20140405. [2]
 Perry, Jennifer A., and Manoj Srinivasan. "Walking with wider steps
 changes foot placement control, increases kinematic variability and does
 not improve linear stability." <em>Royal Society open
 science</em> 4, no. 9 (2017): 160627. [3] Joshi,
 Varun, and Manoj Srinivasan. "A controller for walking derived from
 how humans recover from perturbations." <em>Journal of The
 Royal Society Interface</em> 16, no. 157 (2019): 20190027.
 [4] Seethapathi, Nidhi, and Manoj Srinivasan. "Step-to-step
 variations in human running reveal how humans run without falling."
 <em>Elife</em> 8 (2019): e38371. [5]
 Handford, Matthew L., and Manoj Srinivasan. "Sideways walking:
 preferred is slow, slow is optimal, and optimal is expensive."
 <em>Biology letters</em> 10, no. 1 (2014): 20131006.
 [6] Seethapathi, Nidhi, and Manoj Srinivasan. "The metabolic
 cost of changing walking speeds is significant, implies lower optimal
 speeds for shorter distances, and increases daily energy estimates."
 <em>Biology letters</em> 11, no. 9 (2015): 20150486.
 [7] Brown, Geoffrey L., Nidhi Seethapathi, and Manoj Srinivasan.
 "A unified energy-optimality criterion predicts human navigation
 paths and speeds." <em>Proceedings of the National Academy of
 Sciences</em> 118, no. 29 (2021): e2020327118.
description: # Data from: James-Stein estimator improves accuracy and sample efficiency
 in human kinematic and metabolic data
 [https://doi.org/10.5061/dryad.3j9kd51v9](https://doi.org/10.5061/dryad.3j9kd51v9) ## Description of the data and file structure The repository has three subfolders, one for each type of data and estimation problem. 1\) **foot placement control** - the sensitivity of foot placement to sideways perturbations 2\) **resting metabolics** - the metabolic cost of sitting/resting 3\) **walking metabolics** - the metabolic cost of walking in a circle Both the statistical estimates and their standard errors are provided for all the subjects, all these different estimation problems, and for different trial durations, as provided in the associated manuscript. The file naming makes it clear what duration each dataset is for and what quantity is being stored in the file. **Software:** All data files are .mat files, namely MATLAB data files, and can be loaded with the 'load' command in MATLAB. Free software such as Python and Octave have appropriate corresponding commands for opening such files. We now describe the variables and the format for the data in each folder in more detail. **Foot placement control** The file names in this folder all have a name of the form 'xFootByxHip_LtoR_N_Steps.mat', where N ranges from 15 to 100 in steps of 5. e.g., xFootByxHip_LtoR_15_Steps.mat, xFootByxHip_LtoR_20_Steps.mat, etc. The files in this folder contains the sensitivity of foot placement relative to hip (pelvis) state. As the file names above indicate, the quantity reported is the sensitivity of xFoot (foot position in sideways direction) to xHip (hip position relative to previous stance foot in the sideways direction). The LtoR in the file name refers to the fact that the mappings are from hip position during left stance (L) to foot position during the next right stance (R). The number N refers to the number of left to right transitions used to infer the sensitivity. Each file can be loaded in MATLAB using the command, where N = 15, ... 100: load('xFootByxHip_LtoR_N_Steps.mat') Two 8 x 1 array variables will be loaded into the MATLAB workspace.  xFootByxHip_LtoR_Estimate - the value of the sensitivity of xFoot to xHip for 8 human participants xFootByxHip_LtoR_SE - the standard error of the xFootByxHip_LtoR_Estimate for the 8 subjects The motion capture data was obtained using Vicon T20 system with Nexus 1.x. **Resting metabolics**  The file names in this folder all have a name of the form 'metabolicCost_resting_N_points', where N ranges from 5 to 40 in steps of 5. e.g., metabolicCost_resting_5_points.mat, metabolicCost_resting_10_points.mat, etc. The files in this folder contains the mean resting metabolic rate for 27 subjects. The number N refers to the number of individual metabolic rate samples used to infer the average. Each file can be loaded in MATLAB using the command, where N = 5, ... 40: load('metabolicCost_resting_N_points.mat') Two 27 x 1 array variables will be loaded into the MATLAB workspace.  metCost_Estimate - the value of the mean metabolic rate for 27 human participants metCost_SE - the standard error of the metCost_Estimate for the 27 subjects The metabolic data was obtained using Oxycon Mobile indirect calorimetry system. **Walking metabolics** The file names in this folder all have a name of the form 'metabolicCostWithExponentialFit_CircleWalking_N_points.mat', where N ranges from 3 to 54 in steps of 3. e.g., metabolicCostWithExponentialFit_CircleWalking_3_points.mat, metabolicCostWithExponentialFit_CircleWalking_6_points.mat, etc. The files in this folder contains the estimated steady state metabolic rate when the participant is walking in a circle. The metabolic data was obtained using Oxycon Mobile indirect calorimetry system. The number N refers to the number of metabolic rate samples used to fit an exponential to estimate the steady state value of the metabolic rates.  Each file can be loaded in MATLAB using the command, where N =  3, ... 54: load('metabolicCostWithExponentialFit_CircleWalking_N_points.mat') Two 11 x 1 array variables will be loaded into the MATLAB workspace.  metCost_Estimate - the value of the steady state metabolic rate for 11 human participants metCost_SE - the standard error of the metCost_Estimate for the 11 subjects