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research-article

Physical activity recognition using multiple sensors embedded in a wearable device

Published: 22 February 2013 Publication History

Abstract

In this article, we present a wearable intelligence device for activity monitoring applications. We developed and evaluated algorithms to recognize physical activities from data acquired using a 3-axis accelerometer with a single camera worn on a body. The recognition process is performed in two steps: at first the features for defining a human activity are measured by the 3-axis accelerometer sensor and the image sensor embedded in a wearable device. Then, the physical activity corresponding to the measured features is determined by applying the SVM classifier. The 3-axis accelerometer sensor computes the correlation between axes and the magnitude of the FFT for other features of an activity. Acceleration data is classified into nine activity labels. Through the image sensor, multiple optical flow vectors computed on each grid image patch are extracted as features for defining an activity. In the experiments, we showed that an overall accuracy rate of activity recognition based our method was 92.78%.

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Information

Published In

cover image ACM Transactions on Embedded Computing Systems
ACM Transactions on Embedded Computing Systems  Volume 12, Issue 2
Special issue on embedded systems for interactive multimedia services (ES-IMS)
February 2013
209 pages
ISSN:1539-9087
EISSN:1558-3465
DOI:10.1145/2423636
Issue’s Table of Contents
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

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Publication History

Published: 22 February 2013
Accepted: 01 March 2011
Revised: 01 March 2011
Received: 01 November 2010
Published in TECS Volume 12, Issue 2

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Author Tags

  1. Accelerometer
  2. SVM
  3. human activity recognition
  4. ubiquitous
  5. wearable computing

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Cited By

View all
  • (2024)Artificial Intelligence and Its Revolutionary Role in Physical and Mental Rehabilitation: A Review of Recent AdvancementsBioMed Research International10.1155/bmri/95545902024:1Online publication date: 17-Dec-2024
  • (2024)State-of-the-art in human activity recognition based on inertial measurement unit sensors: survey and applicationsInternational Journal of Computers and Applications10.1080/1206212X.2024.2426501(1-16)Online publication date: 18-Nov-2024
  • (2022)Internet of Things (IoT) Based Activity Recognition Strategies in Smart Homes: A ReviewIEEE Sensors Journal10.1109/JSEN.2022.316179722:9(8327-8336)Online publication date: 1-May-2022
  • (2021)State-of-the-art survey on activity recognition and classification using smartphones and wearable sensorsMultimedia Tools and Applications10.1007/s11042-021-11410-0Online publication date: 22-Sep-2021
  • (2020)Analysis of Machine Learning-Based Assessment for Elbow Spasticity Using Inertial SensorsSensors10.3390/s2006162220:6(1622)Online publication date: 14-Mar-2020
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  • (2020)Multivariate Analysis of Joint Motion Data by Kinect: Application to Parkinson’s DiseaseIEEE Transactions on Neural Systems and Rehabilitation Engineering10.1109/TNSRE.2019.295370728:1(181-190)Online publication date: Jan-2020
  • (2020)A Review on the Artificial Intelligence Algorithms for the Recognition of Activities of Daily Living Using Sensors in Mobile DevicesHandbook of Wireless Sensor Networks: Issues and Challenges in Current Scenario's10.1007/978-3-030-40305-8_33(685-713)Online publication date: 9-Feb-2020
  • (2019)Fusing Object Information and Inertial Data for Activity RecognitionSensors10.3390/s1919411919:19(4119)Online publication date: 23-Sep-2019
  • (2019)Wireless non-invasive motion tracking of functional behaviorPervasive and Mobile Computing10.1016/j.pmcj.2019.01.006Online publication date: Jan-2019
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