Recent advances in wearable electromechanical sensors-Moving towards machine learning-assisted wearable sensing systems

被引:63
作者
Dai, Nian [1 ,2 ,3 ,4 ]
Lei, Iek Man [2 ]
Li, Zhaoyang [2 ]
Li, Yi [5 ]
Fang, Peng [1 ,3 ,4 ]
Zhong, Junwen [2 ]
机构
[1] Univ Macau, Dept Electromech Engn, Macau 999078, Peoples R China
[2] Univ Macau, Ctr Artificial Intelligence & Robot, Macau 999078, Peoples R China
[3] Shenzhen Inst Adv Technol, CAS, Key Lab Human Machine Intelligent Synergy Syst, Shenzhen 518055, Peoples R China
[4] Shenzhen Engn Lab Neral Rehabil Technol, Shenzhen 518055, Peoples R China
[5] Univ Macau, Dept Sociol, Macau, Peoples R China
基金
中国国家自然科学基金;
关键词
Wearable electronics; Electromechanical; Sensors; Machine learning; Human-machine interface; ARTIFICIAL-INTELLIGENCE; PRESSURE SENSOR; TRIBOELECTRIC NANOGENERATOR; DIMENSIONALITY REDUCTION; PIEZORESISTIVE SENSOR; COMPOSITE; IMPLEMENTATION; NANOPARTICLE; ELECTRODES; ARRAYS;
D O I
10.1016/j.nanoen.2022.108041
中图分类号
O64 [物理化学(理论化学)、化学物理学];
学科分类号
070304 ; 081704 ;
摘要
With the assistance of powerful machine learning algorithms, data collecting and processing efficiency of wearable electromechanical sensors are highly improved. Meanwhile, the functions and applications of these intelligent sensing systems are widely enhanced and expanded. In this review, wearable electromechanical sensors with various working mechanisms and their typical usage for monitoring human physiological signals are outlined. The recent advances of machine learning-assisted wearable electromechanical sensing systems in specific applications of tactile perception, gesture/gait recognition, and health care are then summarized and discussed. Finally, current existing limitations and future perspectives are discussed. The progress of intelligent wearable electromechanical sensing systems will promote the development in the domains of human-machine interface (HMI), soft robotics, metaverse, etc.
引用
收藏
页数:23
相关论文
共 160 条
[1]   A State-of-the-Art Survey on Deep Learning Theory and Architectures [J].
Alom, Md Zahangir ;
Taha, Tarek M. ;
Yakopcic, Chris ;
Westberg, Stefan ;
Sidike, Paheding ;
Nasrin, Mst Shamima ;
Hasan, Mahmudul ;
Van Essen, Brian C. ;
Awwal, Abdul A. S. ;
Asari, Vijayan K. .
ELECTRONICS, 2019, 8 (03)
[2]   Conceptual and empirical comparison of dimensionality reduction algorithms (PCA, KPCA, LDA, MDS, SVD, LLE, ISOMAP, LE, ICA, t-SNE) [J].
Anowar, Farzana ;
Sadaoui, Samira ;
Selim, Bassant .
COMPUTER SCIENCE REVIEW, 2021, 40
[3]   A Highly Sensitive Capacitive-Based Soft Pressure Sensor Based on a Conductive Fabric and a Microporous Dielectric Layer [J].
Atalay, Ozgur ;
Atalay, Asli ;
Gafford, Joshua ;
Walsh, Conor .
ADVANCED MATERIALS TECHNOLOGIES, 2018, 3 (01)
[4]   Graded Interlocks for Iontronic Pressure Sensors with High Sensitivity and High Linearity over a Broad Range [J].
Bai, Ningning ;
Wang, Liu ;
Xue, Yiheng ;
Wang, Yan ;
Hou, Xingyu ;
Li, Gang ;
Zhang, Yuan ;
Cai, Minkun ;
Zhao, Lingyu ;
Guan, Fangyi ;
Wei, Xueyong ;
Guo, Chuan Fei .
ACS NANO, 2022, 16 (03) :4338-4347
[5]   High-Performance Integrated ZnO Nanowire UV Sensors on Rigid and Flexible Substrates [J].
Bai, Suo ;
Wu, Weiwei ;
Qin, Yong ;
Cui, Nuanyang ;
Bayerl, Dylan J. ;
Wang, Xudong .
ADVANCED FUNCTIONAL MATERIALS, 2011, 21 (23) :4464-4469
[6]   Diving beetle-like miniaturized plungers with reversible, rapid biofluid capturing for machine learning-based care of skin disease [J].
Baik, Sangyul ;
Lee, Jihyun ;
Jeon, Eun Je ;
Park, Bo-yong ;
Kim, Da Wan ;
Song, Jin Ho ;
Lee, Heon Joon ;
Han, Seung Yeop ;
Cho, Seung-Woo ;
Pang, Changhyun .
SCIENCE ADVANCES, 2021, 7 (25)
[7]   Wearable Sensors Integrated with Internet of Things for Advancing eHealth Care [J].
Bayo-Monton, Jose-Luis ;
Martinez-Millana, Antonio ;
Han, Weisi ;
Fernandez-Llatas, Carlos ;
Sun, Yan ;
Traver, Vicente .
SENSORS, 2018, 18 (06)
[8]   Deep Learning for AI [J].
Bengio, Yoshua ;
Lecun, Yann ;
Hinton, Geoffrey .
COMMUNICATIONS OF THE ACM, 2021, 64 (07) :58-65
[9]   A hierarchically patterned, bioinspired e-skin able to detect the direction of applied pressure for robotics [J].
Boutry, Clementine M. ;
Negre, Marc ;
Jorda, Mikael ;
Vardoulis, Orestis ;
Chortos, Alex ;
Khatib, Oussama ;
Bao, Zhenan .
SCIENCE ROBOTICS, 2018, 3 (24)
[10]   A comparison of PCA, KPCA and ICA for dimensionality reduction in support vector machine [J].
Cao, LJ ;
Chua, KS ;
Chong, WK ;
Lee, HP ;
Gu, QM .
NEUROCOMPUTING, 2003, 55 (1-2) :321-336