research-article

A CNN-based Human Activity Recognition System Combining a Laser Feedback Interferometry Eye Movement Sensor and an IMU for Context-aware Smart Glasses

Authors:

Johannes Meyer,

Thomas Schlebusch,

Enkeljeda KasneciAuthors Info & Claims

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, Volume 5, Issue 4

Article No.: 172, Pages 1 - 24

https://doi.org/10.1145/3494998

Published: 30 December 2021 Publication History

Abstract

Smart glasses are considered the next breakthrough in wearables. As the successor of smart watches and smart ear wear, they promise to extend reality by immersive embedding of content in the user's field of view. While advancements in display technology seems to fulfill this promises, interaction concepts are derived from established wearable concepts like touch interaction or voice interaction, preventing full immersion as they require the user to frequently interact with the glasses. To minimize interactions, we propose to add context-awareness to smart glasses through human activity recognition (HAR) by combining head- and eye movement features to recognize a wide range of activities. To measure eye movements in unobtrusive way, we propose laser feedback interferometry (LFI) sensors. These tiny low power sensors are highly robust to ambient light. We combine LFI sensors and an IMU to collect eye and head movement features from 15 participants performing 7 cognitive and physical activities, leading to a unique data set. To recognize activities we propose a 1D-CNN model and apply transfer learning to personalize the classification, leading to an outstanding macro-F1 score of 88.15 % which outperforms state of the art methods. Finally, we discuss the applicability of the proposed system in a smart glasses setup.

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Xia KLi WShao YLu SCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)Vi2ACT:Video-enhanced Cross-modal Co-learning with Representation Conditional Discriminator for Few-shot Human Activity RecognitionProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3681664(1848-1856)Online publication date: 28-Oct-2024
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Yang LAmin OShihada B(2024)Intelligent Wearable Systems: Opportunities and Challenges in Health and SportsACM Computing Surveys10.1145/364846956:7(1-42)Online publication date: 9-Apr-2024
https://dl.acm.org/doi/10.1145/3648469
Xia KLi WGan SLu S(2024)TS2ACTProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36314457:4(1-22)Online publication date: 12-Jan-2024
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Index Terms

A CNN-based Human Activity Recognition System Combining a Laser Feedback Interferometry Eye Movement Sensor and an IMU for Context-aware Smart Glasses
1. Hardware
  1. Communication hardware, interfaces and storage
    1. Sensor devices and platforms
  2. Emerging technologies
    1. Emerging optical and photonic technologies
2. Human-centered computing
  1. Ubiquitous and mobile computing
    1. Ubiquitous and mobile computing systems and tools

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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 5, Issue 4

Dec 2021

1307 pages

EISSN:2474-9567

DOI:10.1145/3508492

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Copyright © 2021 ACM.

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Published: 30 December 2021

Published in IMWUT Volume 5, Issue 4

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Agarwal PAlam M(2023)Knowledge Mapping of Human Activity Recognition Techniques for Assistive LivingInternational Journal of Sensors, Wireless Communications and Control10.2174/221032791366623091111314913:4(203-225)Online publication date: Jul-2023
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