Proceedings ArticleDOI
On preserving statistical characteristics of accelerometry data using their empirical cumulative distribution
Nils Y. Hammerla,Reuben Kirkham,Peter Andras,Thomas Ploetz +3 more
- pp 65-68
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TLDR
The ECDF representation is presented, a novel approach to preserve characteristics of arbitrary distributions for feature extraction, which is particularly suitable for embedded applications and outperforms common approaches to feature extraction across a wide variety of tasks.Abstract:
The majority of activity recognition systems in wearable computing rely on a set of statistical measures, such as means and moments, extracted from short frames of continuous sensor measurements to perform recognition. These features implicitly quantify the distribution of data observed in each frame. However, feature selection remains challenging and labour intensive, rendering a more generic method to quantify distributions in accelerometer data much desired. In this paper we present the ECDF representation, a novel approach to preserve characteristics of arbitrary distributions for feature extraction, which is particularly suitable for embedded applications. In extensive experiments on six publicly available datasets we demonstrate that it outperforms common approaches to feature extraction across a wide variety of tasks.read more
Citations
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Journal ArticleDOI
Large Scale Population Assessment of Physical Activity Using Wrist Worn Accelerometers: The UK Biobank Study.
Aiden R. Doherty,Dan Jackson,Nils Y. Hammerla,Thomas Plötz,Patrick Olivier,Malcolm H. Granat,Thomas E. White,Vincent T. van Hees,Michael I. Trenell,Christoper G. Owen,Stephen J. Preece,Rob Gillions,S.M. Sheard,Tim Peakman,Soren Brage,Nicholas J. Wareham +15 more
TL;DR: The summary measure of overall physical activity is lower in older participants and age-related differences in activity are most prominent in the afternoon and evening, which lays the foundation for studies of physical activity and its health consequences.
Proceedings ArticleDOI
Smart Devices are Different: Assessing and MitigatingMobile Sensing Heterogeneities for Activity Recognition
Allan Stisen,Henrik Blunck,Sourav Bhattacharya,Thor Siiger Prentow,Mikkel Baun Kjærgaard,Anind K. Dey,Tobias Sonne,Mads Møller Jensen +7 more
TL;DR: It is indicated that on-device sensor and sensor handling heterogeneities impair HAR performances significantly and a novel clustering-based mitigation technique suitable for large-scale deployment of HAR is proposed, where heterogeneity of devices and their usage scenarios are intrinsic.
Proceedings ArticleDOI
DeepSense: A Unified Deep Learning Framework for Time-Series Mobile Sensing Data Processing
TL;DR: DeepSense as discussed by the authors integrates convolutional and recurrent neural networks to exploit local interactions among similar mobile sensors, merge local interactions of different sensory modalities into global interactions, and extract temporal relationships to model signal dynamics.
Journal ArticleDOI
Ensembles of Deep LSTM Learners for Activity Recognition using Wearables
Yu Guan,Thomas Plötz +1 more
TL;DR: It is demonstrated that Ensembles of deep L STM learners outperform individual LSTM networks and thus push the state-of-the-art in human activity recognition using wearables.
Journal ArticleDOI
Deep Recurrent Neural Networks for Human Activity Recognition.
Abdulmajid Murad,Jae-Young Pyun +1 more
TL;DR: Experimental results show that the proposed deep recurrent neural networks (DRNNs) used for building recognition models that are capable of capturing long-range dependencies in variable-length input sequences yield better performance than other deep learning techniques, such as deep believe networks (DBNs) and CNNs.
References
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Analysis of Survival Data
David Cox,D. Oakes +1 more
TL;DR: In this article, the authors give a concise account of the analysis of survival data, focusing on new theory on the relationship between survival factors and identified explanatory variables and conclude with bibliographic notes and further results that can be used for student exercises.
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Proceedings ArticleDOI
Introducing a New Benchmarked Dataset for Activity Monitoring
Attila Reiss,Didier Stricker +1 more
TL;DR: A new dataset - recorded from 18 activities performed by 9 subjects, wearing 3 IMUs and a HR-monitor - is created and made publicly available, showing the difficulty of the classification tasks and exposes new challenges for physical activity monitoring.
Journal ArticleDOI
The Opportunity challenge: A benchmark database for on-body sensor-based activity recognition
Ricardo Chavarriaga,Hesam Sagha,Alberto Calatroni,Sundara Tejaswi Digumarti,Gerhard Tröster,José del R. Millán,Daniel Roggen +6 more
TL;DR: This work introduces a versatile human activity dataset recorded in a sensor-rich environment and expects this benchmarking database will motivate other researchers to replicate and outperform the presented results, thus contributing to further advances in the state-of-the-art of activity recognition methods.
Journal ArticleDOI
Wearable Assistant for Parkinson’s Disease Patients With the Freezing of Gait Symptom
M. Bachlin,Meir Plotnik,Daniel Roggen,Inbal Maidan,Jeffrey M. Hausdorff,Nir Giladi,Gerhard Tröster +6 more
TL;DR: A wearable assistant for Parkinson's disease (PD) patients with the freezing of gait (FOG) symptom that uses on-body acceleration sensors to measure the patients' movements and provides online assistive feedback for PD patients when they experienced FOG.