Hybrid Integration of Wearable Devices for Physiological Monitoring

被引:23
作者
Zhang, Yu [1 ]
Zheng, Xin Ting [2 ]
Zhang, Xiangyu [1 ]
Pan, Jieming [1 ]
Thean, Aaron Voon-Yew [1 ]
机构
[1] Natl Univ Singapore, Dept Elect & Comp Engn, Singapore 117576, Singapore
[2] ASTAR, Inst Mat Res & Engn IMRE, Singapore 138634, Singapore
基金
新加坡国家研究基金会;
关键词
CONVOLUTIONAL NEURAL-NETWORKS; HIGH-PERFORMANCE; THERMAL MANAGEMENT; ECCRINE SWEAT; POTENTIOMETRIC SENSOR; MICROFLUIDIC SYSTEMS; SIGNAL COMPRESSION; CORTISOL-LEVELS; STRAIN SENSORS; SENSING SYSTEM;
D O I
10.1021/acs.chemrev.3c00471
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
Wearable devices can provide timely, user-friendly, non- or minimally invasive, and continuous monitoring of human health. Recently, multidisciplinary scientific communities have made significant progress regarding fully integrated wearable devices such as sweat wearable sensors, saliva sensors, and wound sensors. However, the translation of these wearables into markets has been slow due to several reasons associated with the poor system-level performance of integrated wearables. The wearability consideration for wearable devices compromises many properties of the wearables. Besides, the limited power capacity of wearables hinders continuous monitoring for extended duration. Furthermore, peak-power operations for intensive computations can quickly create thermal issues in the compact form factor that interfere with wearability and sensor operations. Moreover, wearable devices are constantly subjected to environmental, mechanical, chemical, and electrical interferences and variables that can invalidate the collected data. This generates the need for sophisticated data analytics to contextually identify, include, and exclude data points per multisensor fusion to enable accurate data interpretation. This review synthesizes the challenges surrounding the wearable device integration from three aspects in terms of hardware, energy, and data, focuses on a discussion about hybrid integration of wearable devices, and seeks to provide comprehensive guidance for designing fully functional and stable wearable devices.
引用
收藏
页码:10386 / 10434
页数:49
相关论文
共 451 条
[51]   Correlation of Serum and Salivary Cytokines Level With Clinical Parameters in Metabolic Syndrome With Periodontitis [J].
Chauhan, Abhishek ;
Yadav, Suraj Singh ;
Dwivedi, Pradeep ;
Lal, Nand ;
Usman, Kauser ;
Khattri, Sanjay .
JOURNAL OF CLINICAL LABORATORY ANALYSIS, 2016, 30 (05) :649-655
[52]   3D double-faced interlock fabric triboelectric nanogenerator for bio-motion energy harvesting and as self-powered stretching and 3D tactile sensors [J].
Chen, Chaoyu ;
Chen, Lijun ;
Wu, Zhiyi ;
Guo, Hengyu ;
Yu, Weidong ;
Du, Zhaoqun ;
Wang, Zhong Lin .
MATERIALS TODAY, 2020, 32 :84-93
[53]   Application of Microfluidics in Wearable Devices [J].
Chen, Guo ;
Zheng, Jiangen ;
Liu, Liyu ;
Xu, Lei .
SMALL METHODS, 2019, 3 (12)
[54]   Electronic Textiles for Wearable Point-of-Care Systems [J].
Chen, Guorui ;
Xiao, Xiao ;
Zhao, Xun ;
Tat, Trinny ;
Bick, Michael ;
Chen, Jun .
CHEMICAL REVIEWS, 2022, 122 (03) :3259-3291
[55]   Multifunctional Iontronic Sensor Based on Liquid Metal-Filled Ho llow Ionogel Fibers in Detecting Pressure, Temperature, and Proximity [J].
Chen, Jianwen ;
Zhu, Guoxuan ;
Wang, Jing ;
Chang, Xiaohua ;
Zhu, Yutian .
ACS APPLIED MATERIALS & INTERFACES, 2023, 15 (05) :7485-7495
[56]   Thermoelectric Coolers: Progress, Challenges, and Opportunities [J].
Chen, Wen-Yi ;
Shi, Xiao-Lei ;
Zou, Jin ;
Chen, Zhi-Gang .
SMALL METHODS, 2022, 6 (02)
[57]   Wearable fiber-based thermoelectrics from materials to applications [J].
Chen, Wen-Yi ;
Shi, Xiao-Lei ;
Zou, Jin ;
Chen, Zhi-Gang .
NANO ENERGY, 2021, 81
[58]   A Capillary-Evaporation Micropump for Real-Time Sweat Rate Monitoring with an Electrochemical Sensor [J].
Chen, Xiao-Ming ;
Li, Yong-Jiang ;
Han, Dan ;
Zhu, Hui-Chao ;
Xue, Chun-Dong ;
Chui, Hsiang-Chen ;
Cao, Tun ;
Qin, Kai-Rong .
MICROMACHINES, 2019, 10 (07)
[59]   Multifunctional Hybrid Membranes with Enhanced Heat Dissipation and Sweat Transportation for Wearable Applications [J].
Chen, Yongfang ;
Qiu, Fengxian ;
Yang, Dongya ;
Li, Yuqi ;
Liang, Hui ;
Zhang, Tao .
ACS APPLIED ENERGY MATERIALS, 2022, 5 (09) :11892-11899
[60]   Asynchronous Federated Learning for Sensor Data with Concept Drift [J].
Chen, Yujing ;
Chai, Zheng ;
Cheng, Yue ;
Rangwala, Huzefa .
2021 IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA), 2021, :4822-4831