Smokesense: Online activity recognition framework on smartwatches

Muhammad Shoaib, Ozlem Durmaz Incel*, Hans Scholten, Paul Havinga

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

11 Citations (Scopus)

Abstract

In most cases, human activity recognition (AR) with smartphones and smartwatches has been done offline due to the limited resources of these devices. Initially, these devices were used for logging sensor data which was later on processed in machine learning tools on a desktop or laptop. However, current versions of these devices are more capable of running an activity recognition system. Therefore, in this paper, we present SmokeSense, an online activity recognition (AR) framework developed for both smartphones and smartwatches on Android platform. This framework can log data from various sensors and can run an AR process in real-time locally on these devices. Any classifier or feature can easily be added on demand. As a case study, we evaluate the recognition performance of smoking with four classifiers, four features, and two sensors on a smartwatch. The activity set includes variants of smoking such as smoking while sitting, standing, walking, biking, as well as other similar activities. Our analysis shows that, similar recognition performance can be achieved in an online recognition as in an offline analysis, even if no training data is available for some smoking postures. We also propose a smoking session detection algorithm to count the number of cigarettes smoked and evaluate its performance.

Original languageEnglish
Title of host publicationMobile Computing, Applications, and Services - 9th International Conference, MobiCASE 2018, Proceedings
EditorsRen Ohmura, Kazuya Murao, Sozo Inoue, Yusuke Gotoh
PublisherSpringer Verlag
Pages106-124
Number of pages19
ISBN (Print)9783319907390
DOIs
Publication statusPublished - 2018
Event9th EAI International Conference on Mobile Computing, Applications, and Services, MobiCASE 2018 - Osaka, Japan
Duration: 28 Feb 20182 Mar 2018
Conference number: 9

Publication series

NameLecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST
Volume240
ISSN (Print)1867-8211

Conference

Conference9th EAI International Conference on Mobile Computing, Applications, and Services, MobiCASE 2018
Abbreviated titleMobiCASE 2018
CountryJapan
CityOsaka
Period28/02/182/03/18

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