Cargando…
An Activity-Aware Sampling Scheme for Mobile Phones in Activity Recognition
In recent years, sensors in smartphones have been widely used in applications, e.g., human activity recognition (HAR). However, the power of smartphone constrains the applications of HAR due to the computations. To combat it, energy efficiency should be considered in the applications of HAR with sma...
Autores principales: | , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7218853/ https://www.ncbi.nlm.nih.gov/pubmed/32294935 http://dx.doi.org/10.3390/s20082189 |
_version_ | 1783532876222955520 |
---|---|
author | Chen, Zhimin Chen, Jianxin Huang, Xiangjun |
author_facet | Chen, Zhimin Chen, Jianxin Huang, Xiangjun |
author_sort | Chen, Zhimin |
collection | PubMed |
description | In recent years, sensors in smartphones have been widely used in applications, e.g., human activity recognition (HAR). However, the power of smartphone constrains the applications of HAR due to the computations. To combat it, energy efficiency should be considered in the applications of HAR with smartphones. In this paper, we improve energy efficiency for smartphones by adaptively controlling the sampling rate of the sensors during HAR. We collect the sensor samples, depending on the activity changing, based on the magnitude of acceleration. Besides that, we use linear discriminant analysis (LDA) to select the feature and machine learning methods for activity classification. Our method is verified on the UCI (University of California, Irvine) dataset; and it achieves an overall 56.39% of energy saving and the recognition accuracy of 99.58% during the HAR applications with smartphone. |
format | Online Article Text |
id | pubmed-7218853 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-72188532020-05-22 An Activity-Aware Sampling Scheme for Mobile Phones in Activity Recognition Chen, Zhimin Chen, Jianxin Huang, Xiangjun Sensors (Basel) Article In recent years, sensors in smartphones have been widely used in applications, e.g., human activity recognition (HAR). However, the power of smartphone constrains the applications of HAR due to the computations. To combat it, energy efficiency should be considered in the applications of HAR with smartphones. In this paper, we improve energy efficiency for smartphones by adaptively controlling the sampling rate of the sensors during HAR. We collect the sensor samples, depending on the activity changing, based on the magnitude of acceleration. Besides that, we use linear discriminant analysis (LDA) to select the feature and machine learning methods for activity classification. Our method is verified on the UCI (University of California, Irvine) dataset; and it achieves an overall 56.39% of energy saving and the recognition accuracy of 99.58% during the HAR applications with smartphone. MDPI 2020-04-13 /pmc/articles/PMC7218853/ /pubmed/32294935 http://dx.doi.org/10.3390/s20082189 Text en © 2020 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 (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Chen, Zhimin Chen, Jianxin Huang, Xiangjun An Activity-Aware Sampling Scheme for Mobile Phones in Activity Recognition |
title | An Activity-Aware Sampling Scheme for Mobile Phones in Activity Recognition |
title_full | An Activity-Aware Sampling Scheme for Mobile Phones in Activity Recognition |
title_fullStr | An Activity-Aware Sampling Scheme for Mobile Phones in Activity Recognition |
title_full_unstemmed | An Activity-Aware Sampling Scheme for Mobile Phones in Activity Recognition |
title_short | An Activity-Aware Sampling Scheme for Mobile Phones in Activity Recognition |
title_sort | activity-aware sampling scheme for mobile phones in activity recognition |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7218853/ https://www.ncbi.nlm.nih.gov/pubmed/32294935 http://dx.doi.org/10.3390/s20082189 |
work_keys_str_mv | AT chenzhimin anactivityawaresamplingschemeformobilephonesinactivityrecognition AT chenjianxin anactivityawaresamplingschemeformobilephonesinactivityrecognition AT huangxiangjun anactivityawaresamplingschemeformobilephonesinactivityrecognition AT chenzhimin activityawaresamplingschemeformobilephonesinactivityrecognition AT chenjianxin activityawaresamplingschemeformobilephonesinactivityrecognition AT huangxiangjun activityawaresamplingschemeformobilephonesinactivityrecognition |