Feature learning for activity recognition in ubiquitous computing
Thomas Plötz,Nils Y. Hammerla,Patrick Olivier +2 more
- pp 1729-1734
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TLDR
The potential of recent machine learning methods for discovering universal features for context-aware applications of activity recognition is investigated and an alternative data representation based on the empirical cumulative distribution function of the raw data, which effectively abstracts from absolute values is described.Abstract:
Feature extraction for activity recognition in context-aware ubiquitous computing applications is usually a heuristic process, informed by underlying domain knowledge. Relying on such explicit knowledge is problematic when aiming to generalize across different application domains. We investigate the potential of recent machine learning methods for discovering universal features for context-aware applications of activity recognition. We also describe an alternative data representation based on the empirical cumulative distribution function of the raw data, which effectively abstracts from absolute values. Experiments on accelerometer data from four publicly available activity recognition datasets demonstrate the significant potential of our approach to address both contemporary activity recognition tasks and next generation problems such as skill assessment and the detection of novel activities.read more
Citations
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Journal ArticleDOI
Deep Convolutional and LSTM Recurrent Neural Networks for Multimodal Wearable Activity Recognition
TL;DR: A generic deep framework for activity recognition based on convolutional and LSTM recurrent units, which is suitable for multimodal wearable sensors, does not require expert knowledge in designing features, and explicitly models the temporal dynamics of feature activations is proposed.
Journal ArticleDOI
Deep learning for sensor-based activity recognition: A survey
TL;DR: The recent advance of deep learning based sensor-based activity recognition is surveyed from three aspects: sensor modality, deep model, and application and detailed insights on existing work are presented and grand challenges for future research are proposed.
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.
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Proceedings ArticleDOI
Collecting complex activity datasets in highly rich networked sensor environments
Daniel Roggen,Alberto Calatroni,Mirco Rossi,Thomas Holleczek,Kilian Förster,Gerhard Tröster,Paul Lukowicz,David Bannach,Gerald Pirkl,Alois Ferscha,Jakob Doppler,Clemens Holzmann,Marc Kurz,Gerald Holl,Ricardo Chavarriaga,Hesam Sagha,Hamidreza Bayati,Marco Creatura,José del R. Millán +18 more
TL;DR: The networked sensor setup and the methodology for data acquisition, synchronization and curation, and the use of the dataset to develop new sensor network self-organization principles and machine learning techniques for activity recognition in opportunistic sensor configurations are described.