Next Article in Journal
A Game Theoretic Approach for Balancing Energy Consumption in Clustered Wireless Sensor Networks
Next Article in Special Issue
Early Detection of the Initiation of Sit-to-Stand Posture Transitions Using Orthosis-Mounted Sensors
Previous Article in Journal
A High-Performance Portable Transient Electro-Magnetic Sensor for Unexploded Ordnance Detection
Previous Article in Special Issue
The Development of an IMU Integrated Clothes for Postural Monitoring Using Conductive Yarn and Interconnecting Technology
Article Menu
Issue 11 (November) cover image

Export Article

Open AccessArticle
Sensors 2017, 17(11), 2647; doi:10.3390/s17112647

Estimating Stair Running Performance Using Inertial Sensors

1
Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI 48109, USA
2
Department of Orthopedic Surgery, Rush University Medical Center, Chicago, IL 60612, USA
3
Department of Aeronautics and Astronautics, Massachusetts Institute of Technology, Boston, MA 02139, USA
*
Author to whom correspondence should be addressed.
Received: 10 October 2017 / Revised: 11 November 2017 / Accepted: 13 November 2017 / Published: 17 November 2017
(This article belongs to the Special Issue Wearable and Ambient Sensors for Healthcare and Wellness Applications)
View Full-Text   |   Download PDF [2754 KB, uploaded 17 November 2017]   |  

Abstract

Stair running, both ascending and descending, is a challenging aerobic exercise that many athletes, recreational runners, and soldiers perform during training. Studying biomechanics of stair running over multiple steps has been limited by the practical challenges presented while using optical-based motion tracking systems. We propose using foot-mounted inertial measurement units (IMUs) as a solution as they enable unrestricted motion capture in any environment and without need for external references. In particular, this paper presents methods for estimating foot velocity and trajectory during stair running using foot-mounted IMUs. Computational methods leverage the stationary periods occurring during the stance phase and known stair geometry to estimate foot orientation and trajectory, ultimately used to calculate stride metrics. These calculations, applied to human participant stair running data, reveal performance trends through timing, trajectory, energy, and force stride metrics. We present the results of our analysis of experimental data collected on eleven subjects. Overall, we determine that for either ascending or descending, the stance time is the strongest predictor of speed as shown by its high correlation with stride time. View Full-Text
Keywords: wearable sensors; inertial measurement units; motion tracking; human performance; stair running wearable sensors; inertial measurement units; motion tracking; human performance; stair running
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Ojeda, L.V.; Zaferiou, A.M.; Cain, S.M.; Vitali, R.V.; Davidson, S.P.; Stirling, L.A.; Perkins, N.C. Estimating Stair Running Performance Using Inertial Sensors. Sensors 2017, 17, 2647.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top