This dataset is associated with the manuscript entitled "Data-efficient Human Walking Speed Intent Inference". The data represent the measurements taken from 15 able-bodied human subjects as the made speed changes while walking on a treadmill. Each subject is associated with a .mat file that contains 8 variables. Four variables are associated with the training dataset while four are associated with the experimental testing protocol.

DOI Link: 
10.21227/961z-jn75
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Dataset Files

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[1] Taylor Higgins, Kaitlyn Bresingham, James Schmiedeler, Patrick Wensing, "Data-efficient Human Walking Speed Intent Identification", IEEE Dataport, 2023. [Online]. Available: http://dx.doi.org/10.21227/961z-jn75. Accessed: Jan. 14, 2025.
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doi = {10.21227/961z-jn75},
url = {http://dx.doi.org/10.21227/961z-jn75},
author = {Taylor Higgins; Kaitlyn Bresingham; James Schmiedeler; Patrick Wensing },
publisher = {IEEE Dataport},
title = {Data-efficient Human Walking Speed Intent Identification},
year = {2023} }
TY - DATA
T1 - Data-efficient Human Walking Speed Intent Identification
AU - Taylor Higgins; Kaitlyn Bresingham; James Schmiedeler; Patrick Wensing
PY - 2023
PB - IEEE Dataport
UR - 10.21227/961z-jn75
ER -
Taylor Higgins, Kaitlyn Bresingham, James Schmiedeler, Patrick Wensing. (2023). Data-efficient Human Walking Speed Intent Identification. IEEE Dataport. http://dx.doi.org/10.21227/961z-jn75
Taylor Higgins, Kaitlyn Bresingham, James Schmiedeler, Patrick Wensing, 2023. Data-efficient Human Walking Speed Intent Identification. Available at: http://dx.doi.org/10.21227/961z-jn75.
Taylor Higgins, Kaitlyn Bresingham, James Schmiedeler, Patrick Wensing. (2023). "Data-efficient Human Walking Speed Intent Identification." Web.
1. Taylor Higgins, Kaitlyn Bresingham, James Schmiedeler, Patrick Wensing. Data-efficient Human Walking Speed Intent Identification [Internet]. IEEE Dataport; 2023. Available from : http://dx.doi.org/10.21227/961z-jn75
Taylor Higgins, Kaitlyn Bresingham, James Schmiedeler, Patrick Wensing. "Data-efficient Human Walking Speed Intent Identification." doi: 10.21227/961z-jn75