research-article

A tutorial on human activity recognition using body-worn inertial sensors

Authors:

Andreas Bulling,

Bernt SchieleAuthors Info & Claims

ACM Computing Surveys (CSUR), Volume 46, Issue 3

Article No.: 33, Pages 1 - 33

https://doi.org/10.1145/2499621

Published: 01 January 2014 Publication History

Abstract

The last 20 years have seen ever-increasing research activity in the field of human activity recognition. With activity recognition having considerably matured, so has the number of challenges in designing, implementing, and evaluating activity recognition systems. This tutorial aims to provide a comprehensive hands-on introduction for newcomers to the field of human activity recognition. It specifically focuses on activity recognition using on-body inertial sensors. We first discuss the key research challenges that human activity recognition shares with general pattern recognition and identify those challenges that are specific to human activity recognition. We then describe the concept of an Activity Recognition Chain (ARC) as a general-purpose framework for designing and evaluating activity recognition systems. We detail each component of the framework, provide references to related research, and introduce the best practice methods developed by the activity recognition research community. We conclude with the educational example problem of recognizing different hand gestures from inertial sensors attached to the upper and lower arm. We illustrate how each component of this framework can be implemented for this specific activity recognition problem and demonstrate how different implementations compare and how they impact overall recognition performance.

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Cited By

Jeong YKim S(2024)COLDOG: Human Activity Recognition through Collaborative Learning for Domain GeneralizationJournal of the Korean Institute of Industrial Engineers10.7232/JKIIE.2024.50.2.07550:2(75-82)Online publication date: 15-Apr-2024
https://doi.org/10.7232/JKIIE.2024.50.2.075
Roy ADutta HBhuyan ABiswas S(2024)On-Device Semi-Supervised Activity Detection: A New Privacy-Aware Personalized Health Monitoring ApproachSensors10.3390/s2414444424:14(4444)Online publication date: 9-Jul-2024
https://doi.org/10.3390/s24144444
Cherian JRay STaele PKoh JHammond T(2024)Exploring the Impact of the NULL Class on In-the-Wild Human Activity RecognitionSensors10.3390/s2412389824:12(3898)Online publication date: 16-Jun-2024
https://doi.org/10.3390/s24123898
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Index Terms

A tutorial on human activity recognition using body-worn inertial sensors
1. Computer systems organization
  1. Embedded and cyber-physical systems
  2. Real-time systems
2. Computing methodologies
  1. Machine learning

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Published In

cover image ACM Computing Surveys

ACM Computing Surveys Volume 46, Issue 3

January 2014

507 pages

ISSN:0360-0300

EISSN:1557-7341

DOI:10.1145/2578702

Issue’s Table of Contents

Copyright © 2014 ACM.

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 the author(s) 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

New York, NY, United States

Publication History

Published: 01 January 2014

Accepted: 01 June 2013

Revised: 01 April 2013

Received: 01 October 2011

Published in CSUR Volume 46, Issue 3

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Cited By

Jeong YKim S(2024)COLDOG: Human Activity Recognition through Collaborative Learning for Domain GeneralizationJournal of the Korean Institute of Industrial Engineers10.7232/JKIIE.2024.50.2.07550:2(75-82)Online publication date: 15-Apr-2024
https://doi.org/10.7232/JKIIE.2024.50.2.075
Roy ADutta HBhuyan ABiswas S(2024)On-Device Semi-Supervised Activity Detection: A New Privacy-Aware Personalized Health Monitoring ApproachSensors10.3390/s2414444424:14(4444)Online publication date: 9-Jul-2024
https://doi.org/10.3390/s24144444
Cherian JRay STaele PKoh JHammond T(2024)Exploring the Impact of the NULL Class on In-the-Wild Human Activity RecognitionSensors10.3390/s2412389824:12(3898)Online publication date: 16-Jun-2024
https://doi.org/10.3390/s24123898
Fula VMoreno P(2024)Wrist-Based Fall Detection: Towards Generalization across DatasetsSensors10.3390/s2405167924:5(1679)Online publication date: 5-Mar-2024
https://doi.org/10.3390/s24051679
Haresamudram HEssa IPlötz T(2024)Towards Learning Discrete Representations via Self-Supervision for Wearables-Based Human Activity RecognitionSensors10.3390/s2404123824:4(1238)Online publication date: 15-Feb-2024
https://doi.org/10.3390/s24041238
Azadi BHaslgrübler MAnzengruber-Tanase BSopidis GFerscha A(2024)Robust Feature Representation Using Multi-Task Learning for Human Activity RecognitionSensors10.3390/s2402068124:2(681)Online publication date: 21-Jan-2024
https://doi.org/10.3390/s24020681
Alhazmi AAlanazi MAlshehry AAlshahry SJaszek JDjukic CBrown AJackson KChodavarapu V(2024)Intelligent Millimeter-Wave System for Human Activity Monitoring for TelemedicineSensors10.3390/s2401026824:1(268)Online publication date: 2-Jan-2024
https://doi.org/10.3390/s24010268
Cavita-Huerta EReyes-Reyes JRomero-Ugalde HOsorio-Gordillo GEscobar-Jiménez RAlvarado-Martínez V(2024)Human Activity Recognition from Accelerometry, Based on a Radius of Curvature FeatureMathematical and Computational Applications10.3390/mca2905008029:5(80)Online publication date: 13-Sep-2024
https://doi.org/10.3390/mca29050080
Pearce JChang YXia DAbeyesinghe S(2024)Classification of Behaviour in Conventional and Slow-Growing Strains of Broiler Chickens Using Tri-Axial AccelerometersAnimals10.3390/ani1413195714:13(1957)Online publication date: 2-Jul-2024
https://doi.org/10.3390/ani14131957
Lalapura VBhimavarapu VAmudha JSatheesh H(2024)A Systematic Evaluation of Recurrent Neural Network Models for Edge Intelligence and Human Activity Recognition ApplicationsAlgorithms10.3390/a1703010417:3(104)Online publication date: 28-Feb-2024
https://doi.org/10.3390/a17030104
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