Advanced Wearable Computing Techniques and Applications

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Electrical, Electronics and Communications Engineering".

Deadline for manuscript submissions: 30 July 2024 | Viewed by 3031

Special Issue Editors

Prof. Dr. Zhan Zhang
E-Mail Website
Guest Editor
School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China
Interests: wearable computing; edge computing; human–computer interaction; fault tolerant computing
Special Issues, Collections and Topics in MDPI journals
Dr. Jie Zhang
E-Mail Website
Guest Editor
School of Electronic and Electrical Engineering, University of Leeds, Leeds LS2 9JT, UK
Interests: intelligent sensing; robot-assisted rehabilitation; AIoT; wearable computing; machine learning
Prof. Dr. Yunlong Zhao
E-Mail Website
Guest Editor
College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
Interests: wearable computing; collective computing; swarm synergy intelligence; pervasive computing; data fusion intelligence; network topology
School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China
Interests: wireless localization and tracking; energy harvesting based network resource management; distributed machine learning for big data; wireless sensor networks; internet of things
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

As an important part of the construction of human–machine–matter harmonious environment, wearable computing has been applied to the enhancement of human sense, intelligence and physical strength, giving birth to a new computing model and scientific paradigm, and profoundly affecting human life and social reform.

The enhanced sense of user and environment status of wearable computing systems has changed from simple data collection and moved to the understanding of current status and prediction of future evolution and development. The intelligent enhancement for complex application environments has been transformed from task planning and node prompt to multi-sensor integrated intelligent service and active perception of user needs. Physical enhancement of human exercise and health has shifted from data monitoring to proactive warning and intervention. With the seamless connection of "AI+AR" technology and wearable technology, many frontier fields such as wearable computing and digital economy, intelligent medical treatment, intelligent fabric and clothing, metaverse and digital twinning are deeply intersected and integrated, and a lot of new technologies and applications to enhance human sense, intelligence and physical strength have been born.

This Special Issue will publish high-quality and original research papers in the following fields, but not limited to them:

  • Wearable hardware and systems;
  • Wearable computing enabled human health monitoring and analysis;
  • Intelligent interactive technology;
  • Wearable health interventions and treatment strategies;
  • Wearable applications;
  • Other cross-cutting studies.

Prof. Dr. Zhan Zhang
Dr. Jie Zhang
Prof. Dr. Yunlong Zhao
Prof. Dr. Wendong Xiao
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • wearable computing
  • human–computer interaction
  • health monitoring
  • metaverse
  • digital twinning
  • digital healthcare

Published Papers (2 papers)

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Research

19 pages, 6623 KiB  
Article
Research on High-Performance Fourier Transform Algorithms Based on the NPU
Appl. Sci. 2024, 14(1), 405; https://doi.org/10.3390/app14010405 - 01 Jan 2024
Viewed by 858
Abstract
Backpack computers require powerful, intelligent computing capabilities for field wearables while taking energy consumption into careful consideration. A recommended solution for this demand is the CPU + NPU-based SoC. In many wearable intelligence applications, the Fourier Transform is an essential, computationally intensive preprocessing [...] Read more.
Backpack computers require powerful, intelligent computing capabilities for field wearables while taking energy consumption into careful consideration. A recommended solution for this demand is the CPU + NPU-based SoC. In many wearable intelligence applications, the Fourier Transform is an essential, computationally intensive preprocessing task. However, due to the unique structure of the NPU, the conventional Fourier Transform algorithms cannot be applied directly to it. This paper proposes two NPU-accelerated Fourier Transform algorithms that leverage the unique hardware structure of the NPU and provides three implementations of those algorithms, namely MM-2DFT, MV-2FFTm, and MV-2FFTv. Then, we benchmarked the speed and energy efficiency of our algorithms for the gray image edge filtering task on the Huawei Atlas200I-DK-A2 development kits against the Cooley-Tukey algorithm running on CPU and GPU platforms. The experiment results reveal MM-2DFT outperforms OpenCL-based FFT on NVIDIA Tegra X2 GPU for small input sizes, with a 4- to 8-time speedup. As the input image resolution exceeds 2048, MV-2FFTv approaches GPU computation speed. Additionally, two scenarios were tested and analyzed for energy efficiency, revealing that cube units of the NPU are more energy efficient. The vector and CPU units are better suited for sparse matrix multiplication and small-scale inputs, respectively. Full article
(This article belongs to the Special Issue Advanced Wearable Computing Techniques and Applications)
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19 pages, 9939 KiB  
Article
Airbag Protection and Alerting System for Elderly People
Appl. Sci. 2023, 13(16), 9354; https://doi.org/10.3390/app13169354 - 17 Aug 2023
Viewed by 1700
Abstract
In the elderly population, falling is an important cause of severe injuries like hip injuries. Injury- or unconsciousness-related immobility implies that the affected are unable to seek assistance. Building fall detection systems is essential since it is usual for senior individuals residing alone [...] Read more.
In the elderly population, falling is an important cause of severe injuries like hip injuries. Injury- or unconsciousness-related immobility implies that the affected are unable to seek assistance. Building fall detection systems is essential since it is usual for senior individuals residing alone to go many hours without being located after a fall, considerably increasing the impact of injuries caused by falls. The primary goal of this paper is to implement an airbag protection and alerting system for elderly people. The system can be installed on a waist belt as an airbag. It is connected wirelessly to the elderly person’s mobile, where an Android mobile application is created to receive alert notifications from the system. The system will detect the fall using a gravity sensor that is connected to an Arduino board. If a fall is detected by the gravity sensor, then the system will activate an air valve and inflate the airbag from the air tank. The system will also send a warning notification to the elderly person’s mobile application via Bluetooth. Then, the elderly person’s GPS location will be determined from their mobile phone and an SMS will be transmitted to a mobile phone belonging to his/her emergency contact of choice. The system is tuned to provide a proper fall detection sensitivity with good accuracy (90%). It has a relatively low cost, very quick response time (only 150 ms delay from the electrical grid), and low power consumption (11.1 Wh). Full article
(This article belongs to the Special Issue Advanced Wearable Computing Techniques and Applications)
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