The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
센서의 활동 인식은 시계열 데이터에 대한 분류 문제입니다. 이 분야의 일부 연구에서는 데이터 세트마다 다른 시간 및 주파수 영역에서 직접 제작한 기능을 활용합니다. 완전히 다른 또 다른 접근 방식은 기능 학습에 딥 러닝 방법을 사용하는 것입니다. 이 문서에서는 상용 기능 추출기를 사용하여 다수의 후보 시간 영역 기능을 생성한 다음 특정 분류 기술에 대한 편향을 줄이기 위해 설계된 기능 선택기를 사용하는 중간 지점을 탐구합니다. 또한 이 논문에서는 센서 방향에 거의 영향을 받지 않는 기능의 사용을 옹호하고 활동 인식 문제에 대한 적용 가능성을 보여줍니다. 제안된 접근 방식은 다양한 실험 프로토콜을 사용하여 다양한 조건에서 수집된 6개의 공개적으로 사용 가능한 데이터 세트를 사용하여 평가되었으며 대부분의 데이터 세트에서 최첨단 방법과 비슷하거나 더 높은 정확도를 나타내지만 일반적으로 훨씬 적은 수의 기능을 사용합니다.
Yasser MOHAMMAD
AIST,Assiut University
Kazunori MATSUMOTO
KDDI Research Inc.
Keiichiro HOASHI
KDDI Research Inc.
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부
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
부
@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},}
부
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 -