ActDetector: Detecting Daily Activities Using Smartwatches

Detecting daily activities is helpful for health care and clinical medicine. In this paper, we present ActDetector, a smartwatch based application which detects 8 common daily activities, including sitting, walking, running, going upstairs, going downstairs, eating, driving and sitting in a vehicle. By leveraging the built-in sensors on smartwatch, a multi-level classification system is proposed which considers both detection accuracy and energy efficiency. ActDetector is designed to work unobtrusively, no matter on which wrist the smartwatch is worn. We have implemented ActDetector on Sony Smartwatch 3 and evaluated its performance in real experiments involving 12 users. Experimental results show that ActDetector is energy efficient and can detect the daily activities with high accuracy.

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

[2]  Wen Hu,et al.  Radio-based device-free activity recognition with radio frequency interference , 2015, IPSN.

[3]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[4]  Özlem Durmaz Incel,et al.  User, device and orientation independent human activity recognition on mobile phones: challenges and a proposal , 2013, UbiComp.

[5]  Jie Yang,et al.  E-eyes: device-free location-oriented activity identification using fine-grained WiFi signatures , 2014, MobiCom.

[6]  Xiao Sun,et al.  SymDetector: detecting sound-related respiratory symptoms using smartphones , 2015, UbiComp.

[7]  Mo Li,et al.  IODetector: a generic service for indoor outdoor detection , 2012, SenSys '12.

[8]  Richard A. Davis,et al.  Time Series: Theory and Methods , 2013 .

[9]  Koji Yatani,et al.  BodyScope: a wearable acoustic sensor for activity recognition , 2012, UbiComp.

[10]  Ishwar K. Sethi,et al.  Classification of general audio data for content-based retrieval , 2001, Pattern Recognit. Lett..

[11]  Nirmalya Roy,et al.  RAM: Radar-based activity monitor , 2016, IEEE INFOCOM 2016 - The 35th Annual IEEE International Conference on Computer Communications.

[12]  Guohong Cao,et al.  SleepMonitor: Monitoring Respiratory Rate and Body Position During Sleep Using Smartwatch , 2017, Proc. ACM Interact. Mob. Wearable Ubiquitous Technol..

[13]  Kaishun Wu,et al.  WiFall: Device-free fall detection by wireless networks , 2017, IEEE INFOCOM 2014 - IEEE Conference on Computer Communications.

[14]  Ye Xu,et al.  Enabling large-scale human activity inference on smartphones using community similarity networks (csn) , 2011, UbiComp '11.

[15]  Yujie Dong,et al.  Detecting Periods of Eating During Free-Living by Tracking Wrist Motion , 2014, IEEE Journal of Biomedical and Health Informatics.

[16]  Wei Wang,et al.  Understanding and Modeling of WiFi Signal Based Human Activity Recognition , 2015, MobiCom.

[17]  Jungho Im,et al.  Support vector machines in remote sensing: A review , 2011 .

[18]  Zhigang Liu,et al.  The Jigsaw continuous sensing engine for mobile phone applications , 2010, SenSys '10.

[19]  Martin Wolf,et al.  An Efficient Algorithm for Automatic Peak Detection in Noisy Periodic and Quasi-Periodic Signals , 2012, Algorithms.

[20]  Guoliang Xing,et al.  PBN: towards practical activity recognition using smartphone-based body sensor networks , 2011, SenSys.

[21]  Frank Kurth.  The shift-ACF: Detecting multiply repeated signal components , 2013, 2013 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics.

[22]  Xiaomei Zhang,et al.  Infectious Disease Containment Based on a Wireless Sensor System , 2016, IEEE Access.

[23]  J. Hanley,et al.  A task-orientated intervention enhances walking distance and speed in the first year post stroke: a randomized controlled trial , 2004, Clinical rehabilitation.