Energy-efficient activity recognition via multiple time-scale analysis

In this work, we propose a novel power-efficient strategy for supervised human activity recognition using a multiple time-scale approach, which takes into account various window sizes. We assess the proposed methodology on our new multimodal dataset for activities of daily life (ADL), which combines the use of physiological and inertial sensors from multiple wearable devices. We aim to develop techniques that can run efficiently in wearable devices for real-time activity recognition. Our analysis shows that the proposed approach Sequential Maximum-Likelihood (SML) achieves high F1 score across all activities while providing lower power consumption than the standard Maximum-Likelihood (ML) approach.

[1]  Tatsuo Nakajima,et al.  Feature Selection and Activity Recognition from Wearable Sensors , 2006, UCS.

[2]  Diane J. Cook,et al.  Activity recognition on streaming sensor data , 2014, Pervasive Mob. Comput..

[3]  Tina L Hurst,et al.  Physical activity classification using the GENEA wrist-worn accelerometer. , 2012, Medicine and science in sports and exercise.

[4]  Ling Bao,et al.  Activity Recognition from User-Annotated Acceleration Data , 2004, Pervasive.

[5]  Héctor Pomares,et al.  Window Size Impact in Human Activity Recognition , 2014, Sensors.

[6]  Antonio Fernández-Caballero,et al.  A survey of video datasets for human action and activity recognition , 2013, Comput. Vis. Image Underst..

[7]  Gary M. Weiss,et al.  Activity recognition using cell phone accelerometers , 2011, SKDD.

[8]  Ricardo Chavarriaga,et al.  The Opportunity challenge: A benchmark database for on-body sensor-based activity recognition , 2013, Pattern Recognit. Lett..

[9]  Hassan Ghasemzadeh,et al.  Power-Aware Computing in Wearable Sensor Networks: An Optimal Feature Selection , 2015, IEEE Transactions on Mobile Computing.

[10]  Lin Sun,et al.  Activity Recognition on an Accelerometer Embedded Mobile Phone with Varying Positions and Orientations , 2010, UIC.

[11]  Andreas Krause,et al.  Trading off prediction accuracy and power consumption for context-aware wearable computing , 2005, Ninth IEEE International Symposium on Wearable Computers (ISWC'05).

[12]  Miguel A. Labrador,et al.  A Survey on Human Activity Recognition using Wearable Sensors , 2013, IEEE Communications Surveys & Tutorials.

[13]  Moritz Tenorth,et al.  The TUM Kitchen Data Set of everyday manipulation activities for motion tracking and action recognition , 2009, 2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops.

[14]  Daniel Borrajo,et al.  A dynamic sliding window approach for activity recognition , 2011, UMAP'11.

[15]  Jessica K. Hodgins,et al.  Detailed Human Data Acquisition of Kitchen Activities: the CMU-Multimodal Activity Database (CMU-MMAC) , 2008 .

[16]  Noel E. O'Connor,et al.  Classification of Sporting Activities Using Smartphone Accelerometers , 2013, Sensors.

[17]  Michael L. Littman,et al.  Activity Recognition from Accelerometer Data , 2005, AAAI.

[18]  Bernt Schiele,et al.  Analyzing features for activity recognition , 2005, sOc-EUSAI '05.

[19]  Diogo R. Ferreira,et al.  Preprocessing techniques for context recognition from accelerometer data , 2010, Personal and Ubiquitous Computing.

[20]  Kent Larson,et al.  Using a Live-In Laboratory for Ubiquitous Computing Research , 2006, Pervasive.

[21]  Davide Anguita,et al.  A Public Domain Dataset for Human Activity Recognition using Smartphones , 2013, ESANN.

[22]  Michael J. Black,et al.  HumanEva: Synchronized Video and Motion Capture Dataset and Baseline Algorithm for Evaluation of Articulated Human Motion , 2010, International Journal of Computer Vision.

[23]  Silvia Conforto,et al.  Varying behavior of different window sizes on the classification of static and dynamic physical activities from a single accelerometer. , 2015, Medical engineering & physics.

[24]  David Howard,et al.  A Comparison of Feature Extraction Methods for the Classification of Dynamic Activities From Accelerometer Data , 2009, IEEE Transactions on Biomedical Engineering.

[25]  Paul Lukowicz,et al.  Collecting complex activity datasets in highly rich networked sensor environments , 2010, 2010 Seventh International Conference on Networked Sensing Systems (INSS).

[26]  Wanmin Wu,et al.  Classification Accuracies of Physical Activities Using Smartphone Motion Sensors , 2012, Journal of medical Internet research.

[27]  Luis Miguel Soria Morillo,et al.  Low Energy Physical Activity Recognition System on Smartphones , 2015, Sensors.

[28]  Jungsun Kim,et al.  Energy-Efficient Real-Time Human Activity Recognition on Smart Mobile Devices , 2016, Mob. Inf. Syst..