LEADER 04001nam 22006255 450 001 9910373903303321 005 20200701022450.0 010 $a3-030-38748-8 024 7 $a10.1007/978-3-030-38748-8 035 $a(CKB)4100000010122010 035 $a(DE-He213)978-3-030-38748-8 035 $a(MiAaPQ)EBC6031638 035 $a(PPN)242846920 035 $a(EXLCZ)994100000010122010 100 $a20200128d2020 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aChallenges and Trends in Multimodal Fall Detection for Healthcare /$fedited by Hiram Ponce, Lourdes Martínez-Villaseñor, Jorge Brieva, Ernesto Moya-Albor 205 $a1st ed. 2020. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2020. 215 $a1 online resource (XIII, 259 p.) 225 1 $aStudies in Systems, Decision and Control,$x2198-4182 ;$v273 311 $a3-030-38747-X 320 $aIncludes bibliographical references. 327 $aChallenges and Solutions on Human Fall Detection and Classi?cation -- Open Source Implementation for Fall Classi?cation and Fall Detection Systems -- Detecting Human Activities based on a Multimodal Sensor Data Set using a Bidirectional Long Short-Term Memory Model: A Case Study -- Approaching Fall Classi?cation using the UP-Fall Detection Dataset: Analysis and Results from an International Competition -- Reviews and Trends on Multimodal Healthcare -- A Novel Approach for Human Fall Detection and Fall Risk Assessment. 330 $aThis book focuses on novel implementations of sensor technologies, artificial intelligence, machine learning, computer vision and statistics for automated, human fall recognition systems and related topics using data fusion. It includes theory and coding implementations to help readers quickly grasp the concepts and to highlight the applicability of this technology. For convenience, it is divided into two parts. The first part reviews the state of the art in human fall and activity recognition systems, while the second part describes a public dataset especially curated for multimodal fall detection. It also gathers contributions demonstrating the use of this dataset and showing examples. This book is useful for anyone who is interested in fall detection systems, as well as for those interested in solving challenging, signal recognition, vision and machine learning problems. Potential applications include health care, robotics, sports, human?machine interaction, among others. 410 0$aStudies in Systems, Decision and Control,$x2198-4182 ;$v273 606 $aBiomedical engineering 606 $aComputational intelligence 606 $aBiomechanics 606 $aBiomedical Engineering and Bioengineering$3https://scigraph.springernature.com/ontologies/product-market-codes/T2700X 606 $aComputational Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/T11014 606 $aBiomechanics$3https://scigraph.springernature.com/ontologies/product-market-codes/L29020 615 0$aBiomedical engineering. 615 0$aComputational intelligence. 615 0$aBiomechanics. 615 14$aBiomedical Engineering and Bioengineering. 615 24$aComputational Intelligence. 615 24$aBiomechanics. 676 $a610.28 676 $a610.285 702 $aPonce$b Hiram$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aMartínez-Villaseñor$b Lourdes$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aBrieva$b Jorge$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aMoya-Albor$b Ernesto$4edt$4http://id.loc.gov/vocabulary/relators/edt 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910373903303321 996 $aChallenges and Trends in Multimodal Fall Detection for Healthcare$92502500 997 $aUNINA