IEICE TRANSACTIONS on Information
Selecting Orientation-Insensitive Features for Activity Recognition from Accelerometers
Summary :
Activity recognition from sensors is a classification problem over time-series data. Some research in the area utilize time and frequency domain handcrafted features that differ between datasets. Another categorically different approach is to use deep learning methods for feature learning. This paper explores a middle ground in which an off-the-shelf feature extractor is used to generate a large number of candidate time-domain features followed by a feature selector that was designed to reduce the bias toward specific classification techniques. Moreover, this paper advocates the use of features that are mostly insensitive to sensor orientation and show their applicability to the activity recognition problem. The proposed approach is evaluated using six different publicly available datasets collected under various conditions using different experimental protocols and shows comparable or higher accuracy than state-of-the-art methods on most datasets but usually using an order of magnitude fewer features.
- Publication
- IEICE TRANSACTIONS on Information Vol.E102-D No.1 pp.104-115
- Publication Date
- 2019/01/01
- Publicized
- 2018/10/05
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2018EDP7092
- Type of Manuscript
- PAPER
- Category
- Information Network
Authors
Yasser MOHAMMAD
AIST,Assiut University
Kazunori MATSUMOTO
KDDI Research Inc.
Keiichiro HOASHI
KDDI Research Inc.
Keyword
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Yasser MOHAMMAD, Kazunori MATSUMOTO, Keiichiro HOASHI, "Selecting Orientation-Insensitive Features for Activity Recognition from Accelerometers" in IEICE TRANSACTIONS on Information,
vol. E102-D, no. 1, pp. 104-115, January 2019, doi: 10.1587/transinf.2018EDP7092.
Abstract: Activity recognition from sensors is a classification problem over time-series data. Some research in the area utilize time and frequency domain handcrafted features that differ between datasets. Another categorically different approach is to use deep learning methods for feature learning. This paper explores a middle ground in which an off-the-shelf feature extractor is used to generate a large number of candidate time-domain features followed by a feature selector that was designed to reduce the bias toward specific classification techniques. Moreover, this paper advocates the use of features that are mostly insensitive to sensor orientation and show their applicability to the activity recognition problem. The proposed approach is evaluated using six different publicly available datasets collected under various conditions using different experimental protocols and shows comparable or higher accuracy than state-of-the-art methods on most datasets but usually using an order of magnitude fewer features.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2018EDP7092/_p
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@ARTICLE{e102-d_1_104,
author={Yasser MOHAMMAD, Kazunori MATSUMOTO, Keiichiro HOASHI, },
journal={IEICE TRANSACTIONS on Information},
title={Selecting Orientation-Insensitive Features for Activity Recognition from Accelerometers},
year={2019},
volume={E102-D},
number={1},
pages={104-115},
abstract={Activity recognition from sensors is a classification problem over time-series data. Some research in the area utilize time and frequency domain handcrafted features that differ between datasets. Another categorically different approach is to use deep learning methods for feature learning. This paper explores a middle ground in which an off-the-shelf feature extractor is used to generate a large number of candidate time-domain features followed by a feature selector that was designed to reduce the bias toward specific classification techniques. Moreover, this paper advocates the use of features that are mostly insensitive to sensor orientation and show their applicability to the activity recognition problem. The proposed approach is evaluated using six different publicly available datasets collected under various conditions using different experimental protocols and shows comparable or higher accuracy than state-of-the-art methods on most datasets but usually using an order of magnitude fewer features.},
keywords={},
doi={10.1587/transinf.2018EDP7092},
ISSN={1745-1361},
month={January},}
Copy
TY - JOUR
TI - Selecting Orientation-Insensitive Features for Activity Recognition from Accelerometers
T2 - IEICE TRANSACTIONS on Information
SP - 104
EP - 115
AU - Yasser MOHAMMAD
AU - Kazunori MATSUMOTO
AU - Keiichiro HOASHI
PY - 2019
DO - 10.1587/transinf.2018EDP7092
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E102-D
IS - 1
JA - IEICE TRANSACTIONS on Information
Y1 - January 2019
AB - Activity recognition from sensors is a classification problem over time-series data. Some research in the area utilize time and frequency domain handcrafted features that differ between datasets. Another categorically different approach is to use deep learning methods for feature learning. This paper explores a middle ground in which an off-the-shelf feature extractor is used to generate a large number of candidate time-domain features followed by a feature selector that was designed to reduce the bias toward specific classification techniques. Moreover, this paper advocates the use of features that are mostly insensitive to sensor orientation and show their applicability to the activity recognition problem. The proposed approach is evaluated using six different publicly available datasets collected under various conditions using different experimental protocols and shows comparable or higher accuracy than state-of-the-art methods on most datasets but usually using an order of magnitude fewer features.
ER -
English
