LEADER 03824nam 22006135 450 001 9911049187103321 005 20260102122902.0 010 $a3-032-09241-8 024 7 $a10.1007/978-3-032-09241-0 035 $a(CKB)44769875100041 035 $a(MiAaPQ)EBC32470442 035 $a(Au-PeEL)EBL32470442 035 $a(DE-He213)978-3-032-09241-0 035 $a(EXLCZ)9944769875100041 100 $a20260102d2026 u| 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aMachine Learning and Deep Learning in Human Activity Recognition and Fall Detection $eAlgorithms, Frameworks, and Applications for Sustainable Healthcare /$fby Suparna Biswas 205 $a1st ed. 2026. 210 1$aCham :$cSpringer Nature Switzerland :$cImprint: Springer,$d2026. 215 $a1 online resource (181 pages) 225 1 $aSignals and Communication Technology,$x1860-4870 311 08$a3-032-09240-X 327 $aIntroduction -- Introduction to Human Activity Recognition, Fall Monitoring and Detection, Health monitoring -- Fundamental concepts -- Machine Learning in Human Activity Recognition using Smartphone Sensors -- Deep Learning in Human Activity Recognition using Smartphone Sensors -- Machine Learning in Fall Detection System -- Sensor Fusion based HAR for Disease Monitoring and Prediction -- Design, Development, Deployment Strategies -- Conclusion. 330 $aThis book presents research into the domain of Human Activity Recognition (HAR) and Fall Detection (FD), with a focus on the seamless monitoring and support of elderly people. The author shows how current HAR and FD technologies have application in disease monitoring, prediction and identification, as well real-time facilitating early diagnosis of symptom-based disease identification, prediction, and detection. The author discusses existing infrastructure that supports this ecosystem, comprising smartphones, WiFi, 3G/4G Internet connectivity, and low-cost wearable sensors for sustainable health monitoring and care. The book presents smart technologies such as machine learning, deep learning, and Internet of Things that are applied for sensor data analysis and knowledge extraction towards accurate identification of activities and fall events with pre-fall postures in real time. The author also shows how smart and seamless health monitoring and care ecosystem fits with traditional healthcare system for sustainable solutions. Presents smart technologies for sustainable health monitoring and care targeted for the elderly; Discusses techniques for privacy surrounding Human Activity Recognition (HAR) and Fall Detection (FD); Includes case studies, scenario-based studies, sponsored projects, prototypes and successful applications. 410 0$aSignals and Communication Technology,$x1860-4870 606 $aTelecommunication 606 $aMedical informatics 606 $aSignal processing 606 $aMachine learning 606 $aCommunications Engineering, Networks 606 $aHealth Informatics 606 $aSignal, Speech and Image Processing 606 $aMachine Learning 615 0$aTelecommunication. 615 0$aMedical informatics. 615 0$aSignal processing. 615 0$aMachine learning. 615 14$aCommunications Engineering, Networks. 615 24$aHealth Informatics. 615 24$aSignal, Speech and Image Processing. 615 24$aMachine Learning. 676 $a621.382 700 $aBiswas$b Suparna$01886190 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9911049187103321 996 $aMachine Learning and Deep Learning in Human Activity Recognition and Fall Detection$94522304 997 $aUNINA