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- W4295510511 abstract "The aim of this study was to estimate the temporal gait parameters using a wrist-worn Inertial Measurement Unit (IMU) during an outdoor run. While it is easier to compute running gait parameters using foot IMUs, a wrist IMU is more convenient and less obtrusive when it comes to data acquisition. During a track run of 12 minutes, we equipped 14 highly-trained male runners with one IMU on the wrist and one on each foot. We trained machine learning models based on CNN, GPR, and Lasso regression using wrist IMU signals and validated them with a foot-worn IMU reference system. Lasso model performed the best, with the accuracy for cycle time, swing time, flight time, and contact time being <tex xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>$0.27, % pm 0.1, %, 2.6, %pm 1.7, %, 7.3, % pm 4.9, {%}$</tex> , and <tex xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>$10.6, % pm 5.5, {%}$</tex> , respectively." @default.
- W4295510511 created "2022-09-14" @default.
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- W4295510511 date "2022-07-11" @default.
- W4295510511 modified "2023-10-02" @default.
- W4295510511 title "Estimation of Temporal Parameters During Running with a Wrist-worn Inertial Sensor: an In-field Validation" @default.
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- W4295510511 doi "https://doi.org/10.1109/embc48229.2022.9871063" @default.
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