• About
  • Policies
  • What is open access
  • Library
  • Contact
Advanced search
      View Item 
      •   BUIR Home
      • Scholarly Publications
      • Faculty of Engineering
      • Department of Electrical and Electronics Engineering
      • View Item
      •   BUIR Home
      • Scholarly Publications
      • Faculty of Engineering
      • Department of Electrical and Electronics Engineering
      • View Item
      JavaScript is disabled for your browser. Some features of this site may not work without it.

      Investigating the Performance of Wearable Motion Sensors on recognizing falls and daily activities via machine learning

      Thumbnail
      View / Download
      1.2 Mb
      Author(s)
      Kavuncuoğlu, E.
      Uzunhisarcıklı, E.
      Barshan, Billur
      Özdemir, A.T.
      Date
      2022-06-30
      Source Title
      Digital Signal Processing
      Publisher
      Academic Press
      Volume
      126
      Pages
      103365-1 - 103365-17
      Language
      English
      Type
      Article
      Item Usage Stats
      6
      views
      9
      downloads
      Abstract
      With sensor-based wearable technologies, high precision monitoring and recognition of human physical activities in real time is becoming more critical to support the daily living requirements of the elderly. The use of sensor technologies, including accelerometers (A), gyroscopes (G), and magnetometers (M) is mostly encountered in work focused on assistive technology, ambient intelligence, context-aware systems, gait and motion analysis, sports science, and fall detection. The classification performance of four sensor type combinations is investigated through the use of four machine learning algorithms: support vector machines (SVMs), Manhattan k-nearest neighbor classifier (M.k-NN), subspace linear discriminant analysis (SLDA), and ensemble bagged decision tree (EBDT). In this context, a large dataset containing 2520 tests performed by 14 volunteers containing 16 activities of daily living (ADLs) and 20 falls was employed. In binary (fall vs. ADL) and multi-class activity (36 activities) recognition, the highest classification accuracy rate was obtained by the SVM (99.96%) and M.k-NN (95.27%) classifiers, respectively, with the AM sensor type combination in both cases. We also made our dataset publicly available to lay the groundwork for new research.
      Keywords
      Activity recognition
      Fall detection
      Machine learning
      Sensor type combinations
      Wearable sensors
      Permalink
      http://hdl.handle.net/11693/111441
      Published Version (Please cite this version)
      https://dx.doi.org/10.1016/j.dsp.2021.103365
      Collections
      • Department of Electrical and Electronics Engineering 4011
      Show full item record

      Browse

      All of BUIRCommunities & CollectionsTitlesAuthorsAdvisorsBy Issue DateKeywordsTypeDepartmentsCoursesThis CollectionTitlesAuthorsAdvisorsBy Issue DateKeywordsTypeDepartmentsCourses

      My Account

      Login

      Statistics

      View Usage StatisticsView Google Analytics Statistics

      Bilkent University

      If you have trouble accessing this page and need to request an alternate format, contact the site administrator. Phone: (312) 290 2976
      © Bilkent University - Library IT

      Contact Us | Send Feedback | Off-Campus Access | Admin | Privacy