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Qualitative activity recognition of weight lifting exercises

Published: 07 March 2013 Publication History

Abstract

Research on activity recognition has traditionally focused on discriminating between different activities, i.e. to predict which activity was performed at a specific point in time. The quality of executing an activity, the how (well), has only received little attention so far, even though it potentially provides useful information for a large variety of applications. In this work we define quality of execution and investigate three aspects that pertain to qualitative activity recognition: specifying correct execution, detecting execution mistakes, providing feedback on the to the user. We illustrate our approach on the example problem of qualitatively assessing and providing feedback on weight lifting exercises. In two user studies we try out a sensor- and a model-based approach to qualitative activity recognition. Our results underline the potential of model-based assessment and the positive impact of real-time user feedback on the quality of execution.

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  • (2024)Learning manifolds from non-stationary streamsJournal of Big Data10.1186/s40537-023-00872-811:1Online publication date: 23-Mar-2024
  • (2024)Convolutional Neural Network with CBAM Module for Fitness Activity Recognition Using Wearable IMU Sensors2024 Joint International Conference on Digital Arts, Media and Technology with ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunications Engineering (ECTI DAMT & NCON)10.1109/ECTIDAMTNCON60518.2024.10480008(567-571)Online publication date: 31-Jan-2024
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cover image ACM Other conferences
AH '13: Proceedings of the 4th Augmented Human International Conference
March 2013
254 pages
ISBN:9781450319041
DOI:10.1145/2459236
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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  • SimTech: SimTech
  • Universität Stuttgart: Universität Stuttgart

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Association for Computing Machinery

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Publication History

Published: 07 March 2013

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Author Tags

  1. qualitative activity recognition
  2. real-time user feedback
  3. weight lifting

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AH'13
Sponsor:
  • SimTech
  • Universität Stuttgart
AH'13: 4th Augmented Human International Conference
March 7 - 8, 2013
Stuttgart, Germany

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AH '13 Paper Acceptance Rate 49 of 69 submissions, 71%;
Overall Acceptance Rate 121 of 306 submissions, 40%

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Cited By

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  • (2025)On the Efficacy and Vulnerabilities of Logic Locking in Tree-Based Machine LearningIEEE Transactions on Circuits and Systems I: Regular Papers10.1109/TCSI.2024.345754172:1(180-191)Online publication date: Jan-2025
  • (2024)Learning manifolds from non-stationary streamsJournal of Big Data10.1186/s40537-023-00872-811:1Online publication date: 23-Mar-2024
  • (2024)Convolutional Neural Network with CBAM Module for Fitness Activity Recognition Using Wearable IMU Sensors2024 Joint International Conference on Digital Arts, Media and Technology with ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunications Engineering (ECTI DAMT & NCON)10.1109/ECTIDAMTNCON60518.2024.10480008(567-571)Online publication date: 31-Jan-2024
  • (2024)Time-Series Data to Refined Insights: A Feature Engineering-Driven Approach to Gym Exercise RecognitionIEEE Access10.1109/ACCESS.2024.342830912(100343-100354)Online publication date: 2024
  • (2024)Optimal Activity Recognition Framework Based on Improvement of Regularized Neighborhood Component Analysis (RNCA)IEEE Access10.1109/ACCESS.2024.341382212(113300-113313)Online publication date: 2024
  • (2024)uLift: Adaptive Workout Tracker Using a Single Wrist-Worn AccelerometerIEEE Access10.1109/ACCESS.2024.336343712(21710-21722)Online publication date: 2024
  • (2023)LEAN: Real-Time Analysis of Resistance Training Using Wearable ComputingSensors10.3390/s2310460223:10(4602)Online publication date: 9-May-2023
  • (2023)ProxiFitProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36109207:3(1-32)Online publication date: 27-Sep-2023
  • (2023)Using Learnable Physics for Real-Time Exercise Form RecommendationsProceedings of the 17th ACM Conference on Recommender Systems10.1145/3604915.3608816(688-695)Online publication date: 14-Sep-2023
  • (2023)[Don't] Let The Bodies HIIT The Floor: Fostering Body Awareness in Fast-Paced Physical Activity Using Body-Worn SensorsProceedings of the ACM on Human-Computer Interaction10.1145/36042507:MHCI(1-27)Online publication date: 13-Sep-2023
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