A Robust Step Detection Algorithm and Walking Distance Estimation Based on Daily Wrist Activity Recognition Using a Smart Band
Human activity recognition and pedestrian dead reckoning are an interesting field because of their importance utilities in daily life healthcare. Currently, these fields are facing many challenges, one of which is the lack of a robust algorithm with high performance. This paper proposes a new method...
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doaj-f469fb6a6e4b405f85085eeac4a405072020-11-25T00:21:00ZengMDPI AGSensors1424-82202018-06-01187203410.3390/s18072034s18072034A Robust Step Detection Algorithm and Walking Distance Estimation Based on Daily Wrist Activity Recognition Using a Smart BandDuong Trong Bui0Nhan Duc Nguyen1Gu-Min Jeong2School of Electrical Engineering, Kookmin University, 861-1 Jeongnung-dong, Seongbuk-gu, Seoul 136-702, KoreaSchool of Electrical Engineering, Kookmin University, 861-1 Jeongnung-dong, Seongbuk-gu, Seoul 136-702, KoreaSchool of Electrical Engineering, Kookmin University, 861-1 Jeongnung-dong, Seongbuk-gu, Seoul 136-702, KoreaHuman activity recognition and pedestrian dead reckoning are an interesting field because of their importance utilities in daily life healthcare. Currently, these fields are facing many challenges, one of which is the lack of a robust algorithm with high performance. This paper proposes a new method to implement a robust step detection and adaptive distance estimation algorithm based on the classification of five daily wrist activities during walking at various speeds using a smart band. The key idea is that the non-parametric adaptive distance estimator is performed after two activity classifiers and a robust step detector. In this study, two classifiers perform two phases of recognizing five wrist activities during walking. Then, a robust step detection algorithm, which is integrated with an adaptive threshold, peak and valley correction algorithm, is applied to the classified activities to detect the walking steps. In addition, the misclassification activities are fed back to the previous layer. Finally, three adaptive distance estimators, which are based on a non-parametric model of the average walking speed, calculate the length of each strike. The experimental results show that the average classification accuracy is about 99%, and the accuracy of the step detection is 98.7%. The error of the estimated distance is 2.2–4.2% depending on the type of wrist activities.http://www.mdpi.com/1424-8220/18/7/2034step detectionwalking distancewrist activitysmart band |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Duong Trong Bui Nhan Duc Nguyen Gu-Min Jeong |
spellingShingle |
Duong Trong Bui Nhan Duc Nguyen Gu-Min Jeong A Robust Step Detection Algorithm and Walking Distance Estimation Based on Daily Wrist Activity Recognition Using a Smart Band Sensors step detection walking distance wrist activity smart band |
author_facet |
Duong Trong Bui Nhan Duc Nguyen Gu-Min Jeong |
author_sort |
Duong Trong Bui |
title |
A Robust Step Detection Algorithm and Walking Distance Estimation Based on Daily Wrist Activity Recognition Using a Smart Band |
title_short |
A Robust Step Detection Algorithm and Walking Distance Estimation Based on Daily Wrist Activity Recognition Using a Smart Band |
title_full |
A Robust Step Detection Algorithm and Walking Distance Estimation Based on Daily Wrist Activity Recognition Using a Smart Band |
title_fullStr |
A Robust Step Detection Algorithm and Walking Distance Estimation Based on Daily Wrist Activity Recognition Using a Smart Band |
title_full_unstemmed |
A Robust Step Detection Algorithm and Walking Distance Estimation Based on Daily Wrist Activity Recognition Using a Smart Band |
title_sort |
robust step detection algorithm and walking distance estimation based on daily wrist activity recognition using a smart band |
publisher |
MDPI AG |
series |
Sensors |
issn |
1424-8220 |
publishDate |
2018-06-01 |
description |
Human activity recognition and pedestrian dead reckoning are an interesting field because of their importance utilities in daily life healthcare. Currently, these fields are facing many challenges, one of which is the lack of a robust algorithm with high performance. This paper proposes a new method to implement a robust step detection and adaptive distance estimation algorithm based on the classification of five daily wrist activities during walking at various speeds using a smart band. The key idea is that the non-parametric adaptive distance estimator is performed after two activity classifiers and a robust step detector. In this study, two classifiers perform two phases of recognizing five wrist activities during walking. Then, a robust step detection algorithm, which is integrated with an adaptive threshold, peak and valley correction algorithm, is applied to the classified activities to detect the walking steps. In addition, the misclassification activities are fed back to the previous layer. Finally, three adaptive distance estimators, which are based on a non-parametric model of the average walking speed, calculate the length of each strike. The experimental results show that the average classification accuracy is about 99%, and the accuracy of the step detection is 98.7%. The error of the estimated distance is 2.2–4.2% depending on the type of wrist activities. |
topic |
step detection walking distance wrist activity smart band |
url |
http://www.mdpi.com/1424-8220/18/7/2034 |
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