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Paper Number
1857
Paper Type
short
Description
While Augmented Reality (AR) glasses are now instrumental in industries for delivering work-related information, the current one-size-fits-all information provision of AR glasses fails to cater to diverse workers’ needs and environmental conditions. We propose a framework for harnessing Internet of thing (IoT) and wearable technology to improve the adaptability and customization of information provision by AR. As a preliminary exploration, this short paper develops a multi-modal data processing system for work performance classification in the aviation industry. Using machine learning algorithms for multi-modal feature extraction and classifier construction, this framework provides a more objective and consistent evaluation of work performance compared to single-modal approaches. The proposed analytics architecture can provide valuable insights for other industries struggling to implement IoT and mixed reality.
Recommended Citation
Han, Xuewen; Li, Ting; Xu, Sean Xin; Yang, Zherui; Yin, Zhitao; and Zhang, Kunpeng, "IoT and Wearable Devices-Enhanced Information Provision of AR Glasses: A Multi-Modal Analysis in Aviation Industry" (2023). ICIS 2023 Proceedings. 7.
https://aisel.aisnet.org/icis2023/iot_smartcity/iot_smartcity/7
IoT and Wearable Devices-Enhanced Information Provision of AR Glasses: A Multi-Modal Analysis in Aviation Industry
While Augmented Reality (AR) glasses are now instrumental in industries for delivering work-related information, the current one-size-fits-all information provision of AR glasses fails to cater to diverse workers’ needs and environmental conditions. We propose a framework for harnessing Internet of thing (IoT) and wearable technology to improve the adaptability and customization of information provision by AR. As a preliminary exploration, this short paper develops a multi-modal data processing system for work performance classification in the aviation industry. Using machine learning algorithms for multi-modal feature extraction and classifier construction, this framework provides a more objective and consistent evaluation of work performance compared to single-modal approaches. The proposed analytics architecture can provide valuable insights for other industries struggling to implement IoT and mixed reality.
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