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020 _a9783031573453
_9978-3-031-57345-3
024 7 _a10.1007/978-3-031-57345-3
_2doi
050 4 _aTK5105.5-5105.9
072 7 _aUKN
_2bicssc
072 7 _aCOM043000
_2bisacsh
072 7 _aUKN
_2thema
100 1 _aGuo, Xiaonan.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_984111
245 1 0 _aMobile Technologies for Smart Healthcare System Design
_h[electronic resource] /
_cby Xiaonan Guo, Yan Wang, Jerry Cheng, Yingying (Jennifer) Chen.
250 _a1st ed. 2024.
264 1 _aCham :
_bSpringer Nature Switzerland :
_bImprint: Springer,
_c2024.
300 _aIX, 213 p. 94 illus., 89 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aWireless Networks,
_x2366-1445
505 0 _aChapter.1.Introduction -- Chapter.2.Contactless Activity Identification Using Commodity WiFi -- Chapter.3.Personalized Fitness Assistance using Commodity WiFi -- Chapter.4. Multi-person Fitness Assistance via Millimeter Wave -- Chapter.5.Non-intrusive Eating Habits Monitoring Using Millimeter Wave -- Chapter.6.Fitness Assistance Using Motion Sensor -- Chapter.7.Fine-grained Gesture Recognition and Sign Language Interpretation via Photoplethysmography (PPG) on Smartwatches -- Chapter.8.Continuous User Authentication via PPG -- Chapter.9.Conclusion and Future Directions.
520 _aThis book offers a comprehensive examination of mobile technologies in healthcare. It starts by covering wireless solutions, including WiFi signals and mmWave technology for activity recognition, fitness assistance, and eating habit monitoring. The discussion extends to wearable technologies that focus on personal fitness and injury prevention, highlighting the innovative use of PPG sensors in wearables, which enable gesture recognition and user authentication. Based on thorough analyses on the challenges of designing robust mobile healthcare systems, this book addresses the difficulty of gathering accurate and reliable sensor data amidst the variability of human activities. It explores solutions using advanced sensing modalities, such as WiFi, mmWave, and PPG sensors, and robust algorithms for feature extraction to interpret activities, gestures, and biometrics. It also tackles system robustness across diverse environments and practical issues such as reducing training efforts, handling motion artifacts, and the implementation of these systems using commercially available devices. The primary audience for this book targets computer science students and researchers working in mobile computing, smart healthcare, human-computer interaction and artificial intelligence/machine learning. Professionals and consultants focused on advancing mobile-based healthcare solutions will want to purchase this book as a reference. .
650 0 _aComputer networks .
_960157
650 0 _aWireless communication systems.
_965781
650 0 _aMobile communication systems.
_965782
650 0 _aMedical informatics.
_960693
650 1 4 _aComputer Communication Networks.
_965689
650 2 4 _aWireless and Mobile Communication.
_965786
650 2 4 _aHealth Informatics.
700 1 _aWang, Yan.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_984112
700 1 _aCheng, Jerry.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_984113
700 1 _aChen, Yingying (Jennifer).
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_984114
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031573446
776 0 8 _iPrinted edition:
_z9783031573460
776 0 8 _iPrinted edition:
_z9783031573477
830 0 _aWireless Networks,
_x2366-1445
_967497
856 4 0 _uhttps://doi.org/10.1007/978-3-031-57345-3
912 _aZDB-2-SME
912 _aZDB-2-SXM
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