Proceedings ArticleDOI
Analyzing features for activity recognition
Tâm Huynh,Bernt Schiele +1 more
- pp 159-163
TLDR
This paper presents a systematic analysis of features computed from a real-world data set and shows how the choice of feature and the window length over which the feature is computed affects the recognition rates for different activities.Abstract:
Human activity is one of the most important ingredients of context information. In wearable computing scenarios, activities such as walking, standing and sitting can be inferred from data provided by body-worn acceleration sensors. In such settings, most approaches use a single set of features, regardless of which activity to be recognized. In this paper we show that recognition rates can be improved by careful selection of individual features for each activity. We present a systematic analysis of features computed from a real-world data set and show how the choice of feature and the window length over which the feature is computed affects the recognition rates for different activities. Finally, we give a recommendation of suitable features and window lengths for a set of common activities.read more
Citations
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Journal ArticleDOI
A tutorial on human activity recognition using body-worn inertial sensors
TL;DR: In this paper, the authors provide a comprehensive hands-on introduction for newcomers to the field of human activity recognition using on-body inertial sensors and describe the concept of an Activity Recognition Chain (ARC) as a general-purpose framework for designing and evaluating activity recognition systems.
A Tutorial on Human Activity Recognition Using Body-Worn
Andreas Bulling,Ulf Blanke +1 more
TL;DR: This tutorial aims to provide a comprehensive hands-on introduction for newcomers to the field of human activity recognition using on-body inertial sensors and describes the concept of an Activity Recognition Chain (ARC) as a general-purpose framework for designing and evaluating activity recognition systems.
Proceedings Article
Deep convolutional neural networks on multichannel time series for human activity recognition
TL;DR: This method adopts a deep convolutional neural networks (CNN) to automate feature learning from the raw inputs in a systematic way and makes it outperform other HAR algorithms, as verified in the experiments on the Opportunity Activity Recognition Challenge and other benchmark datasets.
Journal ArticleDOI
Using mobile phones to determine transportation modes
TL;DR: This work creates a convenient (no specific position and orientation setting) classification system that uses a mobile phone with a built-in GPS receiver and an accelerometer to identify the transportation mode of an individual when outside.
Proceedings ArticleDOI
Convolutional Neural Networks for human activity recognition using mobile sensors
TL;DR: An approach to automatically extract discriminative features for activity recognition based on Convolutional Neural Networks, which can capture local dependency and scale invariance of a signal as it has been shown in speech recognition and image recognition domains is proposed.
References
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Book ChapterDOI
Activity recognition from user-annotated acceleration data
Ling Bao,Stephen S. Intille +1 more
TL;DR: This is the first work to investigate performance of recognition algorithms with multiple, wire-free accelerometers on 20 activities using datasets annotated by the subjects themselves, and suggests that multiple accelerometers aid in recognition.
Proceedings Article
Activity recognition from accelerometer data
TL;DR: This paper reports on the efforts to recognize user activity from accelerometer data and performance of base-level and meta-level classifiers, and Plurality Voting is found to perform consistently well across different settings.
Journal ArticleDOI
Activity and location recognition using wearable sensors
Seon-Woo Lee,Kenji Mase +1 more
TL;DR: Using measured acceleration and angular velocity data gathered through inexpensive, wearable sensors, this dead-reckoning method can determine a user's location, detect transitions between preselected locations, and recognize and classify sitting, standing, and walking behaviors.
Proceedings Article
A hybrid discriminative/generative approach for modeling human activities
TL;DR: A hybrid approach to recognizing activities is presented, which combines boosting to discriminatively select useful features and learn an ensemble of static classifiers to recognize different activities, with hidden Markov models (HMMs) to capture the temporal regularities and smoothness of activities.
Proceedings ArticleDOI
Recognizing human motion with multiple acceleration sensors
TL;DR: In this paper experiments with acceleration sensors are described for human activity recognition of a wearable device user and the use of principal component analysis and independent component analysis with a wavelet transform is tested for feature generation.