Cargando…
Step Detection Robust against the Dynamics of Smartphones
A novel algorithm is proposed for robust step detection irrespective of step mode and device pose in smartphone usage environments. The dynamics of smartphones are decoupled into a peak-valley relationship with adaptive magnitude and temporal thresholds. For extracted peaks and valleys in the magnit...
Autores principales: | , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2015
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4634483/ https://www.ncbi.nlm.nih.gov/pubmed/26516857 http://dx.doi.org/10.3390/s151027230 |
_version_ | 1782399366527451136 |
---|---|
author | Lee, Hwan-hee Choi, Suji Lee, Myeong-jin |
author_facet | Lee, Hwan-hee Choi, Suji Lee, Myeong-jin |
author_sort | Lee, Hwan-hee |
collection | PubMed |
description | A novel algorithm is proposed for robust step detection irrespective of step mode and device pose in smartphone usage environments. The dynamics of smartphones are decoupled into a peak-valley relationship with adaptive magnitude and temporal thresholds. For extracted peaks and valleys in the magnitude of acceleration, a step is defined as consisting of a peak and its adjacent valley. Adaptive magnitude thresholds consisting of step average and step deviation are applied to suppress pseudo peaks or valleys that mostly occur during the transition among step modes or device poses. Adaptive temporal thresholds are applied to time intervals between peaks or valleys to consider the time-varying pace of human walking or running for the correct selection of peaks or valleys. From the experimental results, it can be seen that the proposed step detection algorithm shows more than 98.6% average accuracy for any combination of step mode and device pose and outperforms state-of-the-art algorithms. |
format | Online Article Text |
id | pubmed-4634483 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-46344832015-11-23 Step Detection Robust against the Dynamics of Smartphones Lee, Hwan-hee Choi, Suji Lee, Myeong-jin Sensors (Basel) Article A novel algorithm is proposed for robust step detection irrespective of step mode and device pose in smartphone usage environments. The dynamics of smartphones are decoupled into a peak-valley relationship with adaptive magnitude and temporal thresholds. For extracted peaks and valleys in the magnitude of acceleration, a step is defined as consisting of a peak and its adjacent valley. Adaptive magnitude thresholds consisting of step average and step deviation are applied to suppress pseudo peaks or valleys that mostly occur during the transition among step modes or device poses. Adaptive temporal thresholds are applied to time intervals between peaks or valleys to consider the time-varying pace of human walking or running for the correct selection of peaks or valleys. From the experimental results, it can be seen that the proposed step detection algorithm shows more than 98.6% average accuracy for any combination of step mode and device pose and outperforms state-of-the-art algorithms. MDPI 2015-10-26 /pmc/articles/PMC4634483/ /pubmed/26516857 http://dx.doi.org/10.3390/s151027230 Text en © 2015 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Lee, Hwan-hee Choi, Suji Lee, Myeong-jin Step Detection Robust against the Dynamics of Smartphones |
title | Step Detection Robust against the Dynamics of Smartphones |
title_full | Step Detection Robust against the Dynamics of Smartphones |
title_fullStr | Step Detection Robust against the Dynamics of Smartphones |
title_full_unstemmed | Step Detection Robust against the Dynamics of Smartphones |
title_short | Step Detection Robust against the Dynamics of Smartphones |
title_sort | step detection robust against the dynamics of smartphones |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4634483/ https://www.ncbi.nlm.nih.gov/pubmed/26516857 http://dx.doi.org/10.3390/s151027230 |
work_keys_str_mv | AT leehwanhee stepdetectionrobustagainstthedynamicsofsmartphones AT choisuji stepdetectionrobustagainstthedynamicsofsmartphones AT leemyeongjin stepdetectionrobustagainstthedynamicsofsmartphones |