{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,8]],"date-time":"2026-07-08T16:56:10Z","timestamp":1783529770399,"version":"3.55.0"},"reference-count":132,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2019,6,12]],"date-time":"2019-06-12T00:00:00Z","timestamp":1560297600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["N161608001"],"award-info":[{"award-number":["N161608001"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National Social Science Fund of China (NSSFC) for the Art Major Project","award":["18ZD23"],"award-info":[{"award-number":["18ZD23"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>With the development of the Internet of Battlefield Things (IoBT), soldiers have become key nodes of information collection and resource control on the battlefield. It has become a trend to develop wearable devices with diverse functions for the military. However, although densely deployed wearable sensors provide a platform for comprehensively monitoring the status of soldiers, wearable technology based on multi-source fusion lacks a generalized research system to highlight the advantages of heterogeneous sensor networks and information fusion. Therefore, this paper proposes a multi-level fusion framework (MLFF) based on Body Sensor Networks (BSNs) of soldiers, and describes a model of the deployment of heterogeneous sensor networks. The proposed framework covers multiple types of information at a single node, including behaviors, physiology, emotions, fatigue, environments, and locations, so as to enable Soldier-BSNs to obtain sufficient evidence, decision-making ability, and information resilience under resource constraints. In addition, we systematically discuss the problems and solutions of each unit according to the frame structure to identify research directions for the development of wearable devices for the military.<\/jats:p>","DOI":"10.3390\/s19122651","type":"journal-article","created":{"date-parts":[[2019,6,12]],"date-time":"2019-06-12T10:55:19Z","timestamp":1560336919000},"page":"2651","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":67,"title":["Systematic Analysis of a Military Wearable Device Based on a Multi-Level Fusion Framework: Research Directions"],"prefix":"10.3390","volume":"19","author":[{"given":"Han","family":"Shi","sequence":"first","affiliation":[{"name":"School of Computer Science and Engineering, Northeastern University, Shenyang 110169, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hai","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Northeastern University, Shenyang 110169, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yang","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering, Shenyang University of Technology, Shenyang 110870, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wei","family":"Gao","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Shenyang University of Chemical Technology, Shenyang 110142, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sheng-Chang","family":"Dou","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Northeastern University, Shenyang 110169, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2019,6,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2618","DOI":"10.1109\/TWC.2018.2799860","article-title":"On the Secure and Reconfigurable Multi-Layer Network Design for Critical Information Dissemination in the Internet of Battlefield Things (IoBT)","volume":"17","author":"Farooq","year":"2018","journal-title":"IEEE Trans. Wirel. Commun."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Russell, S., and Abdelzaher, T. (2018, January 29\u201331). The Internet of Battlefield Things: The Next Generation of Command, Control, Communications and Intelligence (C3I) Decision-Making. Proceedings of the MILCOM 2018\u20142018 IEEE Military Communications Conference (MILCOM), Los Angeles, CA, USA.","DOI":"10.1109\/MILCOM.2018.8599853"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"150","DOI":"10.1016\/j.inffus.2018.01.012","article-title":"Advances in multi-sensor fusion for body sensor networks: Algorithms, architectures, and applications","volume":"45","author":"Fortino","year":"2019","journal-title":"Inf. Fusion"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"2751","DOI":"10.1109\/TBME.2018.2815155","article-title":"Heartbeats Based Biometric Random Binary Sequences Generation to Secure Wireless Body Sensor Networks","volume":"65","author":"Pirbhulal","year":"2018","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"162","DOI":"10.1109\/LWC.2017.2762305","article-title":"Quantizer Design for Generalized Locally Optimum Detectors in Wireless Sensor Networks","volume":"7","author":"Ciuonzo","year":"2018","journal-title":"IEEE Wirel. Commun. Lett."},{"key":"ref_6","unstructured":"Ciuonzo, D., Buonanno, A., D\u2019Urso, M., and Palmieri, F. (2011, January 5\u20138). Distributed classification of multiple moving targets with binary wireless sensor networks. Proceedings of the 2011 14th International Conference on Information Fusion (FUSION), Chicago, IL, USA."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Dao, T., Roy-Chowdhury, A., Nasrabadi, N., Krishnamurthy, S.V., Mohapatra, P., and Kaplan, L.M. (2017, January 22\u201325). Accurate and Timely Situation Awareness Retrieval from a Bandwidth Constrained Camera Network. Proceedings of the 2017 IEEE 14th International Conference on Mobile Ad Hoc and Sensor Systems (MASS), Orlando, FL, USA.","DOI":"10.1109\/MASS.2017.29"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"39974","DOI":"10.1109\/ACCESS.2019.2902846","article-title":"Multi-Objective Workflow Scheduling with Deep-Q-Network-Based Multi-Agent Reinforcement Learning","volume":"7","author":"Wang","year":"2019","journal-title":"IEEE Access"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Buonanno, A., D\u2019Urso, M., Prisco, G., Felaco, M., Meliado, E.F., Mattei, M., Palmieri, F., and Ciuonzo, D. (2012, January 12\u201314). Mobile sensor networks based on autonomous platforms for homeland security. Proceedings of the 2012 Tyrrhenian Workshop on Advances in Radar and Remote Sensing (TyWRRS), Naples, Italy.","DOI":"10.1109\/TyWRRS.2012.6381108"},{"key":"ref_10","first-page":"1","article-title":"Spatially Regularized Structural Support Vector Machine for Robust Visual Tracking","volume":"99","author":"Zheng","year":"2018","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_11","unstructured":"Zheng, Y., Wang, X., Zhang, G., Xiao, B., Xiao, F., and Zhang, J. (2019). Multiple Kernel Coupled Projections for Domain Adaptive Dictionary Learning. IEEE Trans. Multimed., 1."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Cerutti, F., Alzantot, M., Xing, T., Harborne, D., Bakdash, J.Z., Braines, D., Chakraborty, S., Kaplan, L., Kimmig, A., and Preece, A. (2018, January 10\u201313). Learning and Reasoning in Complex Coalition Information Environments: A Critical Analysis. Proceedings of the 2018 21st International Conference on Information Fusion (FUSION), Cambridge, UK.","DOI":"10.23919\/ICIF.2018.8455458"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"207","DOI":"10.1007\/978-3-642-37893-5_24","article-title":"Mobile Multi-parametric Sensor System for Diagnosis of Epilepsy and Brain Related Disorders","volume":"Volume 61","author":"Godara","year":"2013","journal-title":"Wireless Mobile Communication and Healthcare"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Zhu, Y., Liu, D., Grosu, R., Wang, X., Duan, H., and Wang, G. (2017). A Multi-Sensor Data Fusion Approach for Atrial Hypertrophy Disease Diagnosis Based on Characterized Support Vector Hyperspheres. Sensors, 17.","DOI":"10.3390\/s17092049"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"178","DOI":"10.1016\/j.inffus.2017.04.009","article-title":"Design of an accurate end-of-arm force display system based on wearable arm gesture sensors and EMG sensors","volume":"39","author":"Xiong","year":"2018","journal-title":"Inf. Fusion"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1016\/j.inffus.2017.05.004","article-title":"Real-time activity monitoring with a wristband and a smartphone","volume":"43","author":"Szeklicki","year":"2018","journal-title":"Inf. Fusion"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1147","DOI":"10.1016\/j.jsams.2018.06.004","article-title":"Military applications of soldier physiological monitoring","volume":"21","author":"Friedl","year":"2018","journal-title":"J. Sci. Med. Sport"},{"key":"ref_18","unstructured":"Murray, J. (2000, January 16\u201317). Wearable computers in battle: Recent advances in the Land Warrior system. Proceedings of the Digest of Papers. Fourth International Symposium on Wearable Computers, Atlanta, GA, USA."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"42177","DOI":"10.1109\/ACCESS.2018.2861704","article-title":"Wearable Ultrawideband Technology\u2014A Review of Ultrawideband Antennas, Propagation Channels, and Applications in Wireless Body Area Networks","volume":"6","author":"Yan","year":"2018","journal-title":"IEEE Access"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Camomilla, V., Bergamini, E., Fantozzi, S., and Vannozzi, G. (2018). Trends Supporting the In-Field Use of Wearable Inertial Sensors for Sport Performance Evaluation: A Systematic Review. Sensors, 18.","DOI":"10.3390\/s18030873"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Mart\u00edn-Vaquero, J., Hern\u00e1ndez Encinas, A., Queiruga-Dios, A., Jos\u00e9 Bull\u00f3n, J., Mart\u00ednez-Nova, A., Torreblanca Gonz\u00e1lez, J., and Bull\u00f3n-Carbajo, C. (2019). Review on Wearables to Monitor Foot Temperature in Diabetic Patients. Sensors, 19.","DOI":"10.3390\/s19040776"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Taj-Eldin, M., Ryan, C., O\u2019Flynn, B., and Galvin, P. (2018). A Review of Wearable Solutions for Physiological and Emotional Monitoring for Use by People with Autism Spectrum Disorder and Their Caregivers. Sensors, 18.","DOI":"10.3390\/s18124271"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"180","DOI":"10.1007\/s10916-017-0828-y","article-title":"A Systematic Review of Wearable Systems for Cancer Detection: Current State and Challenges","volume":"41","author":"Ray","year":"2017","journal-title":"J. Med. Syst."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1521","DOI":"10.1109\/JBHI.2016.2608720","article-title":"Toward Pervasive Gait Analysis with Wearable Sensors: A Systematic Review","volume":"20","author":"Chen","year":"2016","journal-title":"IEEE J. Biomed. Health Inf."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Ma, C., Wong, D., Lam, W., Wan, A., and Lee, W. (2016). Balance Improvement Effects of Biofeedback Systems with State-of-the-Art Wearable Sensors: A Systematic Review. Sensors, 16.","DOI":"10.3390\/s16040434"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"6931789","DOI":"10.1155\/2016\/6931789","article-title":"Challenges and Issues in Multisensor Fusion Approach for Fall Detection: Review Paper","volume":"2016","author":"Koshmak","year":"2016","journal-title":"J. Sens."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Kami\u0161ali\u0107, A., Fister, I., Turkanovi\u0107, M., and Karakati\u010d, S. (2018). Sensors and Functionalities of Non-Invasive Wrist-Wearable Devices: A Review. Sensors, 18.","DOI":"10.3390\/s18061714"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"580","DOI":"10.1109\/TOH.2017.2689006","article-title":"Wearable Haptic Systems for the Fingertip and the Hand: Taxonomy, Review, and Perspectives","volume":"10","author":"Pacchierotti","year":"2017","journal-title":"IEEE Trans. Haptics"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Nesenbergs, K., and Selavo, L. (2015, January 7\u20139). Smart textiles for wearable sensor networks: Review and early lessons. Proceedings of the 2015 IEEE International Symposium on Medical Measurements and Applications (MeMeA) Proceedings, Turin, Italy.","DOI":"10.1109\/MeMeA.2015.7145236"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"11957","DOI":"10.3390\/s140711957","article-title":"Wearable Electronics and Smart Textiles: A Critical Review","volume":"14","author":"Stoppa","year":"2014","journal-title":"Sensors"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"283","DOI":"10.1007\/s41315-018-0064-8","article-title":"Upper limb rehabilitation using robotic exoskeleton systems: A systematic review","volume":"2","author":"Rehmat","year":"2018","journal-title":"Int. J. Intell. Robot. Appl."},{"key":"ref_32","first-page":"5751391","article-title":"A Review on Compliant Joint Mechanisms for Lower Limb Exoskeletons","volume":"2016","year":"2016","journal-title":"J. Robot."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Rucco, R., Sorriso, A., Liparoti, M., Ferraioli, G., Sorrentino, P., Ambrosanio, M., and Baselice, F. (2018). Type and Location of Wearable Sensors for Monitoring Falls during Static and Dynamic Tasks in Healthy Elderly: A Review. Sensors, 18.","DOI":"10.3390\/s18051613"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Wang, Z., Yang, Z., and Dong, T. (2017). A Review of Wearable Technologies for Elderly Care that Can Accurately Track Indoor Position, Recognize Physical Activities and Monitor Vital Signs in Real Time. Sensors, 17.","DOI":"10.3390\/s17020341"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"162","DOI":"10.1007\/978-3-319-69155-8_12","article-title":"Infant Monitoring System Using Wearable Sensors Based on Blood Oxygen Saturation: A Review","volume":"Volume 10618","author":"Traore","year":"2017","journal-title":"Intelligent, Secure, and Dependable Systems in Distributed and Cloud Environments"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Chen, H., Xue, M., Mei, Z., Bambang Oetomo, S., and Chen, W. (2016). A Review of Wearable Sensor Systems for Monitoring Body Movements of Neonates. Sensors, 16.","DOI":"10.3390\/s16122134"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1016\/j.inffus.2018.06.002","article-title":"Data fusion and multiple classifier systems for human activity detection and health monitoring: Review and open research directions","volume":"46","author":"Nweke","year":"2019","journal-title":"Inf. Fusion"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"122","DOI":"10.1016\/j.inffus.2017.03.006","article-title":"Multi-sensor distributed fusion estimation with applications in networked systems: A review paper","volume":"38","author":"Sun","year":"2017","journal-title":"Inf. Fusion"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Wei, P., Ball, J.E., and Anderson, D.T. (2016, January 17). Multi-sensor conflict measurement and information fusion. Proceedings of the SPIE 9842, Signal Processing, Sensor\/Information Fusion, and Target Recognition XXV, Baltimore, MD, USA.","DOI":"10.1117\/12.2222049"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1016\/j.cmpb.2017.10.024","article-title":"Surface electromyography based muscle fatigue detection using high-resolution time-frequency methods and machine learning algorithms","volume":"154","author":"Karthick","year":"2018","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Zhang, G., Morin, E., Zhang, Y., and Etemad, S.A. (2018, January 18\u201321). Non-invasive detection of low-level muscle fatigue using surface EMG with wavelet decomposition. Proceedings of the 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Honolulu, HI, USA.","DOI":"10.1109\/EMBC.2018.8513588"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Hu, W., Li, K., Wei, N., Yue, S., and Yin, C. (2017, January 20\u201322). Influence of exercise-induced local muscle fatigue on the thumb and index finger forces during precision pinch. Proceedings of the 2017 Chinese Automation Congress (CAC), Jinan, China.","DOI":"10.1109\/CAC.2017.8243150"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1076","DOI":"10.1109\/JDT.2015.2451087","article-title":"EEG-Based Assessment of Stereoscopic 3D Visual Fatigue Caused by Vergence-Accommodation Conflict","volume":"11","author":"Zou","year":"2015","journal-title":"J. Disp. Technol."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Ito, K., Kourakata, Y., and Hotta, Y. (2015, January 25\u201329). Muscle fatigue detection during dynamic contraction under blood flow restriction: Improvement of detection sensitivity using multivariable fatigue indices. Proceedings of the 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Milan, Italy.","DOI":"10.1109\/EMBC.2015.7319778"},{"key":"ref_45","unstructured":"Ko, L.W., Lai, W.K., Liang, W.G., Chuang, C.H., Lu, S.W., Lu, Y.C., Hsiung, T.Y., Wu, H.H., and Lin, C.T. (2015, January 12\u201317). Single channel wireless EEG device for real-time fatigue level detection. Proceedings of the 2015 International Joint Conference on Neural Networks (IJCNN), Killarney, Ireland."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Kobayshi, T., Okada, S., Makikawa, M., Shiozawa, N., and Kosaka, M. (2017, January 11\u201315). Development of wearable muscle fatigue detection system using capacitance coupling electrodes. Proceedings of the 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Seogwipo, Korea.","DOI":"10.1109\/EMBC.2017.8036953"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"345","DOI":"10.1109\/TNSRE.2016.2641956","article-title":"A Passive EEG-BCI for Single-Trial Detection of Changes in Mental State","volume":"25","author":"Myrden","year":"2017","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"4257","DOI":"10.1109\/LRA.2018.2864355","article-title":"Soft Wearable Augmented Walking Suit With Pneumatic Gel Muscles and Stance Phase Detection System to Assist Gait","volume":"3","author":"Thakur","year":"2018","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Ma, B., Li, C., Wu, Z., and Huang, Y. (2018, January 3\u20136). A PWM-Based Muscle Fatigue Detection and Recovery System. Proceedings of the 2018 International Conference on Bioinformatics and Biomedicine (BIBM), Madrid, Spain.","DOI":"10.1109\/BIBM.2018.8621418"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"821","DOI":"10.1152\/japplphysiol.00788.2007","article-title":"Multichannel thin-film electrode for intramuscular electromyographic recordings","volume":"104","author":"Farina","year":"2008","journal-title":"J. Appl. Physiol."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"715","DOI":"10.1016\/j.cogsys.2018.08.018","article-title":"Electroencephalography based fatigue detection using a novel feature fusion and extreme learning machine","volume":"52","author":"Chen","year":"2018","journal-title":"Cogn. Syst. Res."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1016\/j.bspc.2017.03.022","article-title":"Evolutionary perspective for optimal selection of EEG electrodes and features","volume":"36","author":"Lahiri","year":"2017","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"323","DOI":"10.1049\/iet-cvi.2015.0215","article-title":"Facial video-based detection of physical fatigue for maximal muscle activity","volume":"10","author":"Haque","year":"2016","journal-title":"IET Comput. Vis."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"461","DOI":"10.1109\/TCE.2018.2872162","article-title":"Design and Implementation of a Drowsiness-Fatigue-Detection System Based on Wearable Smart Glasses to Increase Road Safety","volume":"64","author":"Chang","year":"2018","journal-title":"IEEE Trans. Consum. Electron."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Sampei, K., Ogawa, M., Torres, C., Sato, M., and Miki, N. (2016). Mental Fatigue Monitoring Using a Wearable Transparent Eye Detection System. Micromachines, 7.","DOI":"10.3390\/mi7020020"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1016\/j.ijmedinf.2018.08.010","article-title":"Detection of mental fatigue state with wearable ECG devices","volume":"119","author":"Huang","year":"2018","journal-title":"Int. J. Med. Inform."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Sharma, M.K., and Bundele, M.M. (2015, January 10\u201312). Design & analysis of K-means algorithm for cognitive fatigue detection in vehicular driver using Respiration signal. Proceedings of the 2015 IEEE International Conference on Electrical, Computer and Communication Technologies (ICECCT), Coimbatore, India.","DOI":"10.1109\/ICECCT.2015.7226057"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1016\/j.bspc.2019.02.007","article-title":"Assessment of muscle fatigue during isometric contraction using autonomic nervous system correlates","volume":"51","author":"Greco","year":"2019","journal-title":"Biomed. Signal. Process. Control."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Wang, D., Shen, P., Wang, T., and Xiao, Z. (2017, January 6\u20138). Fatigue detection of vehicular driver through skin conductance, pulse oximetry and respiration: A random forest classifier. Proceedings of the 2017 IEEE 9th International Conference on Communication Software and Networks (ICCSN), GuangZhou, China.","DOI":"10.1109\/ICCSN.2017.8230293"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"3076324","DOI":"10.1155\/2019\/3076324","article-title":"Fusion of Motif- and Spectrum-Related Features for Improved EEG-Based Emotion Recognition","volume":"2019","author":"Tiwari","year":"2019","journal-title":"Comput. Intell. Neurosci."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Shu, L., Xie, J., Yang, M., Li, Z., Li, Z., Liao, D., Xu, X., and Yang, X. (2018). A Review of Emotion Recognition Using Physiological Signals. Sensors, 18.","DOI":"10.3390\/s18072074"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"1317","DOI":"10.1587\/transinf.2017MVP0025","article-title":"Multicultural Facial Expression Recognition Based on Differences of Western-Caucasian and East-Asian Facial Expressions of Emotions","volume":"E101.D","author":"Nakamura","year":"2018","journal-title":"IEICE Trans. Inf. Syst."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"26391","DOI":"10.1109\/ACCESS.2018.2831927","article-title":"Dominant and Complementary Emotion Recognition from Still Images of Faces","volume":"6","author":"Guo","year":"2018","journal-title":"IEEE Access"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"347","DOI":"10.1007\/s00371-015-1183-y","article-title":"Real-time EEG-based emotion monitoring using stable features","volume":"32","author":"Lan","year":"2016","journal-title":"Vis. Comput."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"3119","DOI":"10.3233\/JIFS-169496","article-title":"Mouth and eyebrow segmentation for emotion recognition using interpolated polynomials","volume":"34","year":"2018","journal-title":"J. Intell. Fuzzy Syst."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"5296523","DOI":"10.1155\/2018\/5296523","article-title":"Emotion Recognition Based on Weighted Fusion Strategy of Multichannel Physiological Signals","volume":"2018","author":"Wei","year":"2018","journal-title":"Comput. Intell. Neurosci."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"2107451","DOI":"10.1155\/2017\/2107451","article-title":"Fusion of Facial Expressions and EEG for Multimodal Emotion Recognition","volume":"2017","author":"Huang","year":"2017","journal-title":"Comput. Intell. Neurosci."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"7087","DOI":"10.1109\/JSEN.2015.2470638","article-title":"Assessment of Biofeedback Training for Emotion Management Through Wearable Textile Physiological Monitoring System","volume":"15","author":"Wu","year":"2015","journal-title":"IEEE Sens. J."},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Guo, H.-W., Huang, Y.-S., Lin, C.-H., Chien, J.-C., Haraikawa, K., and Shieh, J.-S. (November, January 31). Heart Rate Variability Signal Features for Emotion Recognition by Using Principal Component Analysis and Support Vectors Machine. Proceedings of the 2016 IEEE 16th International Conference on Bioinformatics and Bioengineering (BIBE), Taichung, Taiwan.","DOI":"10.1109\/BIBE.2016.40"},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Das, P., Khasnobish, A., and Tibarewala, D.N. (2016, January 9\u201311). Emotion recognition employing ECG and GSR signals as markers of ANS. Proceedings of the 2016 Conference on Advances in Signal Processing (CASP), Pune, India.","DOI":"10.1109\/CASP.2016.7746134"},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"126","DOI":"10.1109\/TAFFC.2014.2327617","article-title":"Emotion Recognition Based on Multi-Variant Correlation of Physiological Signals","volume":"5","author":"Wen","year":"2014","journal-title":"IEEE Trans. Affect. Comput."},{"key":"ref_72","unstructured":"Xu, Y., Hubener, I., Seipp, A.-K., Ohly, S., and David, K. (2017, January 13\u201317). From the lab to the real-world: An investigation on the influence of human movement on Emotion Recognition using physiological signals. Proceedings of the 2017 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops), Kona, HI, USA."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1016\/j.neucom.2015.07.112","article-title":"Analysis of physiological for emotion recognition with the IRS model","volume":"178","author":"Li","year":"2016","journal-title":"Neurocomputing"},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1016\/j.cmpb.2016.12.005","article-title":"Recognition of emotions using multimodal physiological signals and an ensemble deep learning model","volume":"140","author":"Yin","year":"2017","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"703","DOI":"10.1109\/JBHI.2018.2832069","article-title":"Quantitative Assessment for Self-Tracking of Acute Stress Based on Triangulation Principle in a Wearable Sensor System","volume":"23","author":"Wu","year":"2018","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1016\/j.cmpb.2015.07.006","article-title":"Reliable emotion recognition system based on dynamic adaptive fusion of forehead biopotentials and physiological signals","volume":"122","author":"Khezri","year":"2015","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"9750904","DOI":"10.1155\/2018\/9750904","article-title":"Recognition of Emotions Using Multichannel EEG Data and DBN-GC-Based Ensemble Deep Learning Framework","volume":"2018","author":"Chao","year":"2018","journal-title":"Comput. Intell. Neurosci."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1016\/j.dsp.2018.07.003","article-title":"Emotion recognition based on time\u2013frequency distribution of EEG signals using multivariate synchrosqueezing transform","volume":"81","author":"Mert","year":"2018","journal-title":"Digit. Signal Process."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1016\/j.eswa.2017.09.062","article-title":"Evolutionary computation algorithms for feature selection of EEG-based emotion recognition using mobile sensors","volume":"93","author":"Nakisa","year":"2018","journal-title":"Expert Syst. Appl."},{"key":"ref_80","doi-asserted-by":"crossref","unstructured":"Purnamasari, P., Ratna, A., and Kusumoputro, B. (2017). Development of Filtered Bispectrum for EEG Signal Feature Extraction in Automatic Emotion Recognition Using Artificial Neural Networks. Algorithms, 10.","DOI":"10.3390\/a10020063"},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"563","DOI":"10.1109\/TAFFC.2017.2667642","article-title":"Towards Reading Hidden Emotions: A Comparative Study of Spontaneous Micro-Expression Spotting and Recognition Methods","volume":"9","author":"Li","year":"2018","journal-title":"IEEE Trans. Affect. Comput."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"889","DOI":"10.1007\/s00500-016-2395-4","article-title":"Edge-enhanced bi-dimensional empirical mode decomposition-based emotion recognition using fusion of feature set","volume":"22","author":"Bhattacharya","year":"2018","journal-title":"Soft Comput."},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"4998","DOI":"10.1038\/srep04998","article-title":"Revealing Real-Time Emotional Responses: A Personalized Assessment based on Heartbeat Dynamics","volume":"4","author":"Valenza","year":"2015","journal-title":"Sci. Rep."},{"key":"ref_84","doi-asserted-by":"crossref","unstructured":"Wu, S., Xu, X., Shu, L., and Hu, B. (2017, January 13\u201316). Estimation of valence of emotion using two frontal EEG channels. Proceedings of the 2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Kansas City, MO, USA.","DOI":"10.1109\/BIBM.2017.8217815"},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"11449","DOI":"10.1007\/s11042-016-4203-7","article-title":"Development of emotion recognition interface using complex EEG\/ECG bio-signal for interactive contents","volume":"76","author":"Shin","year":"2017","journal-title":"Multimed. Tools Appl."},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"18","DOI":"10.4018\/JITR.2019010102","article-title":"Human Action Recognition Based on Inertial Sensors and Complexity Classification","volume":"12","author":"Liu","year":"2019","journal-title":"J. Inf. Technol. Res."},{"key":"ref_87","doi-asserted-by":"crossref","unstructured":"Heldt, T., Oefinger, M.B., Hoshiyama, M., and Mark, R.G. (2003, January 21\u201324). Circulatory response to passive and active changes in posture. Proceedings of the Computers in Cardiology, 2003, Thessaloniki Chalkidiki, Greece.","DOI":"10.1109\/CIC.2003.1291141"},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1007\/s11517-016-1504-y","article-title":"A comparison of accuracy of fall detection algorithms (threshold-based vs. machine learning) using waist-mounted tri-axial accelerometer signals from a comprehensive set of falls and non-fall trials","volume":"55","author":"Aziz","year":"2017","journal-title":"Med. Biol. Eng. Comput."},{"key":"ref_89","doi-asserted-by":"crossref","unstructured":"Teufl, W., Lorenz, M., Miezal, M., Taetz, B., Fr\u00f6hlich, M., and Bleser, G. (2018). Towards Inertial Sensor Based Mobile Gait Analysis: Event-Detection and Spatio-Temporal Parameters. Sensors, 19.","DOI":"10.3390\/s19010038"},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"1705","DOI":"10.1007\/s12652-018-0880-6","article-title":"Gait based biometric personal authentication by using MEMS inertial sensors","volume":"9","author":"Tao","year":"2018","journal-title":"J. Ambient Intell. Humaniz. Comput."},{"key":"ref_91","doi-asserted-by":"crossref","unstructured":"Al-Okby, M.F.R., Neubert, S., Stoll, N., and Thurow, K. (2017, January 14\u201316). Complementary functions for intelligent wheelchair head tilts controller. Proceedings of the 2017 IEEE 15th International Symposium on Intelligent Systems and Informatics (SISY), Subotica, Serbia.","DOI":"10.1109\/SISY.2017.8080536"},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"317","DOI":"10.1007\/s11517-012-0996-3","article-title":"A novel five degree of freedom user command controller in people with spinal cord injury and non-injured for full upper extremity neuroprostheses, wearable powered orthoses and prosthetics","volume":"51","author":"Scott","year":"2013","journal-title":"Med. Biol. Eng. Comput."},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1016\/j.engappai.2017.08.013","article-title":"Gesture recognition system for real-time mobile robot control based on inertial sensors and motion strings","volume":"66","year":"2017","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_94","doi-asserted-by":"crossref","unstructured":"Lin, B.-S., Lee, I.-J., Yang, S.-Y., Lo, Y.-C., Lee, J., and Chen, J.-L. (2018). Design of an Inertial-Sensor-Based Data Glove for Hand Function Evaluation. Sensors, 18.","DOI":"10.3390\/s18051545"},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.robot.2018.05.008","article-title":"Waist-assistive exoskeleton powered by a singular actuation mechanism for prevention of back-injury","volume":"107","author":"Ko","year":"2018","journal-title":"Robot. Auton. Syst."},{"key":"ref_96","doi-asserted-by":"crossref","unstructured":"Inose, H., Mohri, S., Arakawa, H., Okui, M., Koide, K., Yamada, Y., Kikutani, I., and Nakamura, T. (June, January 29). Semi-endoskeleton-type waist assist AB-wear suit equipped with compressive force reduction mechanism. Proceedings of the 2017 IEEE International Conference on Robotics and Automation (ICRA), Singapore.","DOI":"10.1109\/ICRA.2017.7989711"},{"key":"ref_97","doi-asserted-by":"crossref","unstructured":"Kondo, S., Ikeura, R., Tachi, A., Murakami, K., Hayakawa, S., and Yasuda, K. (2017, January 5\u20138). Development and evaluation of waist assist device for shipbuilding tasks. Proceedings of the 2017 IEEE International Conference on Robotics and Biomimetics (ROBIO), Macau, China.","DOI":"10.1109\/ROBIO.2017.8324653"},{"key":"ref_98","doi-asserted-by":"crossref","unstructured":"Ancillao, A., Tedesco, S., Barton, J., and O\u2019Flynn, B. (2018). Indirect Measurement of Ground Reaction Forces and Moments by Means of Wearable Inertial Sensors: A Systematic Review. Sensors, 18.","DOI":"10.3390\/s18082564"},{"key":"ref_99","doi-asserted-by":"crossref","first-page":"9884","DOI":"10.3390\/s120709884","article-title":"Foot Plantar Pressure Measurement System: A Review","volume":"12","author":"Zayegh","year":"2012","journal-title":"Sensors"},{"key":"ref_100","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1007\/s10846-017-0645-z","article-title":"Human Arm Motion Tracking by Inertial\/Magnetic Sensors Using Unscented Kalman Filter and Relative Motion Constraint","volume":"90","author":"Atrsaei","year":"2018","journal-title":"J. Intell. Robot. Syst."},{"key":"ref_101","doi-asserted-by":"crossref","unstructured":"McGrath, T., Fineman, R., and Stirling, L. (2018). An Auto-Calibrating Knee Flexion-Extension Axis Estimator Using Principal Component Analysis with Inertial Sensors. Sensors, 18.","DOI":"10.3390\/s18061882"},{"key":"ref_102","doi-asserted-by":"crossref","unstructured":"Pratt, K., and Sigward, S. (2018). Inertial Sensor Angular Velocities Reflect Dynamic Knee Loading during Single Limb Loading in Individuals Following Anterior Cruciate Ligament Reconstruction. Sensors, 18.","DOI":"10.3390\/s18103460"},{"key":"ref_103","doi-asserted-by":"crossref","first-page":"315","DOI":"10.3233\/AIS-180494","article-title":"Validation of a method for the estimation of energy expenditure during physical activity using a mobile device accelerometer","volume":"10","author":"Pires","year":"2018","journal-title":"J. Ambient Intell. Smart Environ."},{"key":"ref_104","doi-asserted-by":"crossref","unstructured":"Shu, Y., Chen, C., Shu, K.-I., and Zhang, H. (2018, January 8\u201312). Research on Human Motion Recognition Based on Wi-Fi and Inertial Sensor Signal Fusion. Proceedings of the 2018 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld\/SCALCOM\/UIC\/ATC\/CBDCom\/IOP\/SCI), Guangzhou, China.","DOI":"10.1109\/SmartWorld.2018.00110"},{"key":"ref_105","unstructured":"Sousa Lima, W., de Souza Bragan\u00e7a, H., Montero Quispe, K., and Pereira Souto, E. (2018). Human Activity Recognition Based on Symbolic Representation Algorithms for Inertial Sensors. Sensors, 18."},{"key":"ref_106","doi-asserted-by":"crossref","first-page":"5597","DOI":"10.1109\/ACCESS.2018.2886362","article-title":"Combining Adaptive Hierarchical Depth Motion Maps with Skeletal Joints for Human Action Recognition","volume":"7","author":"Ding","year":"2019","journal-title":"IEEE Access"},{"key":"ref_107","doi-asserted-by":"crossref","first-page":"745","DOI":"10.1016\/j.future.2018.02.013","article-title":"Accurate detection of sitting posture activities in a secure IoT based assisted living environment","volume":"92","author":"Tariq","year":"2019","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_108","first-page":"577","article-title":"Convolutional Recurrent Neural Networks for Posture Analysis in Fall Detection","volume":"34","author":"Lin","year":"2018","journal-title":"J. Inf. Sci. Eng."},{"key":"ref_109","doi-asserted-by":"crossref","first-page":"31715","DOI":"10.1109\/ACCESS.2018.2839766","article-title":"Human Daily and Sport Activity Recognition Using a Wearable Inertial Sensor Network","volume":"6","author":"Hsu","year":"2018","journal-title":"IEEE Access"},{"key":"ref_110","doi-asserted-by":"crossref","first-page":"7203","DOI":"10.1016\/j.eswa.2013.07.028","article-title":"SVM-based posture identification with a single waist-located triaxial accelerometer","volume":"40","author":"Cabestany","year":"2013","journal-title":"Expert Syst. Appl."},{"key":"ref_111","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1109\/MCE.2018.2880829","article-title":"A Wearable System That Detects Posture and Heart Rate: Designing an Integrated Device with Multiparameter Measurements for Better Health Care","volume":"8","author":"Prawiro","year":"2019","journal-title":"IEEE Consum. Electron. Mag."},{"key":"ref_112","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1016\/j.eswa.2018.01.047","article-title":"Supervised machine learning scheme for electromyography-based pre-fall detection system","volume":"100","author":"Rescio","year":"2018","journal-title":"Expert Syst. Appl."},{"key":"ref_113","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1016\/j.bspc.2018.04.010","article-title":"Discrimination of four class simple limb motor imagery movements for brain\u2013computer interface","volume":"44","author":"Abdalsalam","year":"2018","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_114","doi-asserted-by":"crossref","first-page":"10150","DOI":"10.1109\/ACCESS.2019.2891350","article-title":"Intelligent EMG Pattern Recognition Control Method for Upper-Limb Multifunctional Prostheses: Advances, Current Challenges, and Future Prospects","volume":"7","author":"Samuel","year":"2019","journal-title":"IEEE Access"},{"key":"ref_115","doi-asserted-by":"crossref","first-page":"8940373","DOI":"10.1155\/2018\/8940373","article-title":"Optical Fiber Force Myography Sensor for Identification of Hand Postures","volume":"2018","author":"Fujiwara","year":"2018","journal-title":"J. Sens."},{"key":"ref_116","doi-asserted-by":"crossref","unstructured":"Parrington, L., Jehu, D.A., Fino, P.C., Pearson, S., El-Gohary, M., and King, L.A. (2018). Validation of an Inertial Sensor Algorithm to Quantify Head and Trunk Movement in Healthy Young Adults and Individuals with Mild Traumatic Brain Injury. Sensors, 18.","DOI":"10.3390\/s18124501"},{"key":"ref_117","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1109\/LRA.2018.2874379","article-title":"A Soft Pneumatic Actuator as a Haptic Wearable Device for Upper Limb Amputees: Toward a Soft Robotic Liner","volume":"4","author":"Huaroto","year":"2019","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_118","doi-asserted-by":"crossref","first-page":"2848","DOI":"10.1016\/j.jbiomech.2010.06.005","article-title":"Determination of joint moments with instrumented force shoes in a variety of tasks","volume":"43","author":"Faber","year":"2010","journal-title":"J. Biomech."},{"key":"ref_119","doi-asserted-by":"crossref","unstructured":"Zhao, Y., Zhang, P., Guo, J., Li, X., Wang, J., Yang, F., and Wang, X. (2018). A New Method of High-Precision Positioning for an Indoor Pseudolite without Using the Known Point Initialization. Sensors, 18.","DOI":"10.3390\/s18061977"},{"key":"ref_120","doi-asserted-by":"crossref","first-page":"11563","DOI":"10.1109\/TVT.2018.2871468","article-title":"Investigation of Vehicle Positioning by Infrared Signal-Direction Discrimination for Short-Range Vehicle-to-Vehicle Communications","volume":"67","author":"Shieh","year":"2018","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_121","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1109\/MCE.2018.2797741","article-title":"Improving Mobility for the Visually Impaired: A Wearable Indoor Positioning System Based on Visual Markers","volume":"7","author":"Lee","year":"2018","journal-title":"IEEE Consum. Electron. Mag."},{"key":"ref_122","doi-asserted-by":"crossref","first-page":"845","DOI":"10.1109\/TIM.2018.2857045","article-title":"A SAR-Based Measurement Method for Passive-Tag Positioning with a Flying UHF-RFID Reader","volume":"68","author":"Buffi","year":"2019","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_123","doi-asserted-by":"crossref","first-page":"279","DOI":"10.1109\/JSYST.2017.2766690","article-title":"WUB-IP: A High-Precision UWB Positioning Scheme for Indoor Multiuser Applications","volume":"13","author":"Yin","year":"2019","journal-title":"IEEE Syst. J."},{"key":"ref_124","doi-asserted-by":"crossref","unstructured":"Lu, C., Uchiyama, H., Thomas, D., Shimada, A., and Taniguchi, R. (2019). Indoor Positioning System Based on Chest-Mounted IMU. Sensors, 19.","DOI":"10.3390\/s19020420"},{"key":"ref_125","doi-asserted-by":"crossref","first-page":"557","DOI":"10.1016\/j.compeleceng.2018.05.001","article-title":"Development of mobile platform for indoor positioning reference map using geomagnetic field data","volume":"68","author":"Huang","year":"2018","journal-title":"Comput. Electr. Eng."},{"key":"ref_126","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1109\/JSAC.2017.2774435","article-title":"Three-Dimensional VLC Positioning Based on Angle Difference of Arrival with Arbitrary Tilting Angle of Receiver","volume":"36","author":"Zhu","year":"2018","journal-title":"IEEE J. Sel. Areas Commun."},{"key":"ref_127","doi-asserted-by":"crossref","unstructured":"Lopes, S.I., Vieira, J.M.N., and Albuquerque, D. (2012, January 25\u201327). High Accuracy 3D Indoor Positioning Using Broadband Ultrasonic Signals. Proceedings of the 2012 IEEE 11th International Conference on Trust, Security and Privacy in Computing and Communications, Liverpool, UK.","DOI":"10.1109\/TrustCom.2012.172"},{"key":"ref_128","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.inffus.2017.05.005","article-title":"Towards unravelling the relationship between on-body, environmental and emotion data using sensor information fusion approach","volume":"40","author":"Kanjo","year":"2018","journal-title":"Inf. Fusion"},{"key":"ref_129","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1109\/MC.2018.2876048","article-title":"Toward an Internet of Battlefield Things: A Resilience Perspective","volume":"51","author":"Abdelzaher","year":"2018","journal-title":"Computer"},{"key":"ref_130","doi-asserted-by":"crossref","unstructured":"Kutilek, P., Volf, P., Viteckova, S., Smrcka, P., Krivanek, V., Lhotska, L., Hana, K., Doskocil, R., Navratil, L., and Hon, Z. (June, January 31). Wearable systems for monitoring the health condition of soldiers: Review and application. Proceedings of the 2017 International Conference on Military Technologies (ICMT), Brno, Czech Republic.","DOI":"10.1109\/MILTECHS.2017.7988856"},{"key":"ref_131","doi-asserted-by":"crossref","unstructured":"Moulik, S., Misra, S., and Chakraborty, C. (2015, January 15\u201318). CAPCoS: Context-aware PAN coordinator selection for health monitoring of soldiers in battlefield. Proceedings of the 2015 IEEE International Conference on Advanced Networks and Telecommuncations Systems (ANTS), Kolkata, India.","DOI":"10.1109\/ANTS.2015.7413650"},{"key":"ref_132","doi-asserted-by":"crossref","unstructured":"Rozanowski, K., Sondej, T., and Lewandowski, J. (2015, January 25\u201327). First approach for design of an autonomous measurement system to aid determination of the psychological profile of soldiers. Proceedings of the 2015 22nd International Conference Mixed Design of Integrated Circuits & Systems (MIXDES), Torun, Poland.","DOI":"10.1109\/MIXDES.2015.7208480"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/12\/2651\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T12:57:43Z","timestamp":1760187463000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/12\/2651"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,6,12]]},"references-count":132,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2019,6]]}},"alternative-id":["s19122651"],"URL":"https:\/\/doi.org\/10.3390\/s19122651","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,6,12]]}}}