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Novel Sensing Technologies and Artificial Intelligence for Human–Computer Interaction

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".

Deadline for manuscript submissions: closed (31 August 2023) | Viewed by 2613

Special Issue Editors


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Guest Editor
Department of Computing, Xi’an Jiaotong-Liverpool University, SD447 (Science Building), 111 Ren’ai Road, Dushu Lake Science and Education Innovation District, Suzhou 215123, China
Interests: human–computer interaction; virtual and augmented reality; gaming technologies
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Digital Business Center, School of Management Fribourg, University of Applied Sciences and Arts Western Switzerland (HES-SO), Fribourg, Switzerland
Interests: human-computer interaction; eHealth; digital innovation

Special Issue Information

Dear Colleagues,

Two technological developments are offering new possibilities for people to interact with computing devices. On the one hand, there has been a rapid growth in new sensing technologies. The new generation of sensors is small and can be integrated into a variety of interactive devices, from mobile phones to extended reality systems (such as virtual and augmented reality head-mounted displays). In addition, despite their smaller size, these sensors can capture more varieties of data with high precision. For example, we can now capture hand and body motions and different types of physiological data, including heart rate, eye gaze, respiration, and brain activity, in a non-invasible and unobtrusive way. These data can be measured, manipulated, and, to some extent, interpreted using AI-based approaches.

On the other hand, advances in artificial intelligence, particularly from the subfields of machine learning and deep learning, are helping to make interactions more refined and adapted to the needs of single users or groups of users. Machine learning techniques allow computing devices to learn from and adapt to new data captured via sensors without direct intervention from users. Deep learning techniques enable this automatic learning by absorbing vast amounts of unstructured data such as text, images, or video, which new sensing technologies capture and feed into these AI-based algorithms.

This Special Issue aims to bring state-of-the-art developments that integrate novel sensing technologies with artificial intelligence for augmenting the interaction between people and interactive devices around them.

Prof. Dr. Hai-Ning Liang
Prof. Dr. Maurizio Caon
Guest Editors

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Keywords

  • human–computer interaction
  • artificial intelligence
  • machine learning and deep learning
  • sensing and tracking devices
  • pervasive computing
  • virtual reality
  • augmented reality
  • natural interfaces
  • wearables and IoT
  • brain–computer interfaces
  • affective computing
  • haptic interfaces

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Published Papers (1 paper)

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Research

16 pages, 3889 KiB  
Article
SD-HRNet: Slimming and Distilling High-Resolution Network for Efficient Face Alignment
by Xuxin Lin, Haowen Zheng, Penghui Zhao and Yanyan Liang
Sensors 2023, 23(3), 1532; https://doi.org/10.3390/s23031532 - 30 Jan 2023
Cited by 4 | Viewed by 2181
Abstract
Face alignment is widely used in high-level face analysis applications, such as human activity recognition and human–computer interaction. However, most existing models involve a large number of parameters and are computationally inefficient in practical applications. In this paper, we aim to build a [...] Read more.
Face alignment is widely used in high-level face analysis applications, such as human activity recognition and human–computer interaction. However, most existing models involve a large number of parameters and are computationally inefficient in practical applications. In this paper, we aim to build a lightweight facial landmark detector by proposing a network-level architecture-slimming method. Concretely, we introduce a selective feature fusion mechanism to quantify and prune redundant transformation and aggregation operations in a high-resolution supernetwork. Moreover, we develop a triple knowledge distillation scheme to further refine a slimmed network, where two peer student networks could learn the implicit landmark distributions from each other while absorbing the knowledge from a teacher network. Extensive experiments on challenging benchmarks, including 300W, COFW, and WFLW, demonstrate that our approach achieves competitive performance with a better trade-off between the number of parameters (0.98 M–1.32 M) and the number of floating-point operations (0.59 G–0.6 G) when compared to recent state-of-the-art methods. Full article
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