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Updated: Oct 9, 2025

Design and Analysis for Fall Detection System Simplification
Published on: April 6, 2020
Descending 13 real world steps: A dataset and analysis of stair descent
Astrini Sie1, Maxim Karrenbach1, Charlie Fisher1
1Department of Electrical and Computer Engineering, University of Washington, Seattle, WA 98195, United States of America.
Background:
Stair descent analysis has been typically limited to laboratory staircases of 4 or 5 steps. To date there has been no report of gait parameters during unconstrained stair descent outside of the laboratory, and few motion capture datasets are publicly available.
Research Question:
We aim to collect a dataset and perform gait analysis for stair descent outside of the laboratory. We aim to measure basic kinematic and kinetic gait parameters and foot placement behavior.
Methods:
We present a public stair descent dataset from 101 unimpaired participants aged 18-35 on an unconstrained 13-step staircase collected using wearable sensors. The dataset consists of kinematics (full-body joint angle and position), kinetics (plantar normal forces, acceleration), and foot placement for 30,609 steps.
Results:
We report the lower limb joint angle ranges (30° and 8° for hip flexion and extension, 85° and -11° for knee flexion and extension, and 31° and 28° for ankle dorsi- and plantar-flexion). The self-selected speed was 0.79 ± 0.16 m/s, with cycle duration of 0.97 ± 0.18 s. Mean foot overhang as a percentage of foot length was 17.07 ± 6.66 %, and we calculate that foot size explains only 6% of heel placement variation, but 79% of toe placement variation. We also find a minor but significant asymmetry between left and right maximum hip flexion angle, though all other measured parameters were symmetrical.
Significance:
This is the first quantitative observation of gait data from a large number (n = 101) of participants descending an unconstrained staircase outside of a laboratory. This study enables analysis of gait characteristics including self-selected walking speed and foot placement to better understand typical stair gait behavior. The dataset is a public resource for understanding typical stair descent.

