Research and sustainable design of wearable sensor for clothing based on body area network

IF 1.2 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Cognitive Computation and Systems Pub Date : 2021-04-16 DOI:10.1049/ccs2.12014
Ren Xiangfang, Shen Lei, Liu Miaomiao, Zhang Xiying, Chen Han
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引用次数: 7

Abstract

The body area network (BAN) is composed of every wearable device network on the body to share information and data, which is applied in medical and health, especially in the direction of intelligent clothing. A wearable device is an integrated body of multi-sensor fusion. At the same time, the multi-dimensional needs of users and the unique problems of sensors appear. How to solve the problems of wearable sensors and sustainable design is the research focus. Based on the wearable sensor in the critical factor of wearable device fusion, this paper analyses the classification, technology, and current situation of a wearable sensor, discusses the problems of a wearable sensor for BAN from the aspects of human–computer interaction experience, data accuracy, multiple interaction modes, and battery power supply, and summarizes the direction of multi-sensor fusion, compatible biosensor materials, and low power consumption and high sensitivity. The sustainable design direction of visibility design, identification of use scenarios, short-term human–computer interaction, interaction process reduction, and integration invisibility are introduced. The integration research of wearable sensors is the future trend, and it has been widely used in medical and health, intelligent clothing, wireless communication, military, automobile, and other fields.

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基于体域网络的服装可穿戴传感器的研究与可持续设计
人体区域网络(body area network, BAN)是由人体上的各个可穿戴设备组成的网络,实现信息和数据的共享,在医疗健康,尤其是智能服装方向上的应用。可穿戴设备是多传感器融合的集成体。同时,用户的多维需求和传感器的独特性问题也随之显现。如何解决可穿戴传感器的可持续性设计问题是研究的重点。基于可穿戴传感器在可穿戴设备融合中的关键因素,本文分析了可穿戴传感器的分类、技术和现状,从人机交互体验、数据精度、多种交互模式、电池供电等方面探讨了BAN可穿戴传感器存在的问题,总结了多传感器融合、兼容生物传感器材料、低功耗高灵敏度的发展方向。介绍了可视性设计、使用场景识别、短期人机交互、减少交互过程、集成不可见等可持续设计方向。可穿戴传感器的集成化研究是未来的发展趋势,已广泛应用于医疗健康、智能服装、无线通信、军事、汽车等领域。
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来源期刊
Cognitive Computation and Systems
Cognitive Computation and Systems Computer Science-Computer Science Applications
CiteScore
2.50
自引率
0.00%
发文量
39
审稿时长
10 weeks
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