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What is That in Your Hand?: Recognizing Grasped Objects via Forearm Electromyography Sensing

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Hongan WangAuthors Info & Claims

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, Volume 2, Issue 4

Article No.: 161, Pages 1 - 24

https://doi.org/10.1145/3287039

Published: 27 December 2018 Publication History

Abstract

Knowing the object in hand can offer essential contextual information revealing a user's fine-grained activities. In this paper, we investigate the feasibility, accuracy, and robustness of recognizing the uninstrumented object in a user's hand by sensing and decoding her forearm muscular activities via off-the-shelf electromyography (EMG) sensors. We present results from three studies to advance our fundamental understanding of the opportunities that EMG brings in object interaction recognition. In the first study, we investigated the influence of physical properties of objects such as shape, size, and weight on EMG signals. We also conducted a thorough exploration of the feature spaces and sensor positions which can provide a solid base to rely on for future designers and practitioners for such interactive technique. In the second study, we assessed the feasibility and accuracy of inferring the types of grasped objects via using forearm muscular activity as a cue. Our results indicate that the types of objects can be recognized with up to 94.2% accuracy by employing user-dependent training. In the third study, we investigated the robustness of this approach in a realistic office setting where users were allowed to interact with objects as they would naturally. Our approach achieved up to 82.5% accuracy in discriminating 15 types of objects, even when training and testing phrases were purposefully performed on different days to incorporate changes in EMG patterns over time. Overall, this work contributes a set of fundamental findings and guidelines on using EMG technologies for object-based activity tracking.

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Cited By

Chen WLin SPeng ZParizi FHeo SPatel SMatusik WZhao WStankovic J(2024)ViObjectProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36435478:1(1-26)Online publication date: 6-Mar-2024
https://dl.acm.org/doi/10.1145/3643547
Lee CZhang RAgarwal DYu TGunda VLopez OKim JYin SDong BLi KSakashita MGuimbretiere FZhang C(2024)EchoWrist: Continuous Hand Pose Tracking and Hand-Object Interaction Recognition Using Low-Power Active Acoustic Sensing On a WristbandProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642910(1-21)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642910
Gupta RKavanal VJoshi A(2024)Deep Learning Models for Recognition of Hand Gestures in Basketball Sports2024 1st International Conference on Cognitive, Green and Ubiquitous Computing (IC-CGU)10.1109/IC-CGU58078.2024.10530848(1-6)Online publication date: 1-Mar-2024
https://doi.org/10.1109/IC-CGU58078.2024.10530848
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Index Terms

What is That in Your Hand?: Recognizing Grasped Objects via Forearm Electromyography Sensing
1. Human-centered computing
  1. Ubiquitous and mobile computing
    1. Empirical studies in ubiquitous and mobile computing

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cover image Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies Volume 2, Issue 4

December 2018

1169 pages

EISSN:2474-9567

DOI:10.1145/3301777

Issue’s Table of Contents

Copyright © 2018 ACM.

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Publication History

Published: 27 December 2018

Accepted: 01 October 2018

Revised: 01 August 2018

Received: 01 May 2018

Published in IMWUT Volume 2, Issue 4

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Cited By

Chen WLin SPeng ZParizi FHeo SPatel SMatusik WZhao WStankovic J(2024)ViObjectProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36435478:1(1-26)Online publication date: 6-Mar-2024
https://dl.acm.org/doi/10.1145/3643547
Lee CZhang RAgarwal DYu TGunda VLopez OKim JYin SDong BLi KSakashita MGuimbretiere FZhang C(2024)EchoWrist: Continuous Hand Pose Tracking and Hand-Object Interaction Recognition Using Low-Power Active Acoustic Sensing On a WristbandProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642910(1-21)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642910
Gupta RKavanal VJoshi A(2024)Deep Learning Models for Recognition of Hand Gestures in Basketball Sports2024 1st International Conference on Cognitive, Green and Ubiquitous Computing (IC-CGU)10.1109/IC-CGU58078.2024.10530848(1-6)Online publication date: 1-Mar-2024
https://doi.org/10.1109/IC-CGU58078.2024.10530848
Caillet AGoguey ANigay L(2024)Microgesture ＋ Grasp: A journey from human capabilities to interaction with microgesturesInternational Journal of Human-Computer Studies10.1016/j.ijhcs.2024.103398(103398)Online publication date: Nov-2024
https://doi.org/10.1016/j.ijhcs.2024.103398
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Yin ZLiyanage MOttun APaul SZuniga ANurmi PFlores H(2023)HIPPOProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/35703446:4(1-30)Online publication date: 11-Jan-2023
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He SMa WDong HXiao LJiang T(2023)C-CubeProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/35703426:4(1-26)Online publication date: 11-Jan-2023
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Sharma ASalchow-Hömmen CMollyn VNittala AHedderich MKoelle MSeel TSteimle J(2023)SparseIMU: Computational Design of Sparse IMU Layouts for Sensing Fine-grained Finger MicrogesturesACM Transactions on Computer-Human Interaction10.1145/356989430:3(1-40)Online publication date: 10-Jun-2023
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Waghmare ABen Taleb YChatterjee INarendra APatel S(2023)Z-Ring: Single-Point Bio-Impedance Sensing for Gesture, Touch, Object and User RecognitionProceedings of the 2023 CHI Conference on Human Factors in Computing Systems10.1145/3544548.3581422(1-18)Online publication date: 19-Apr-2023
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