03179nam 22003613 450 991086916460332120240703080304.09783658446888(CKB)32609760400041(MiAaPQ)EBC31508247(Au-PeEL)EBL31508247(EXLCZ)993260976040004120240703d2024 uy 0engur|||||||||||txtrdacontentcrdamediacrrdacarrierEmpowering Independent Living Using the ICF An Unobtrusive Home Monitoring Sensor System for Older Adults1st ed.Wiesbaden :Springer Vieweg. in Springer Fachmedien Wiesbaden GmbH,2024.©2024.1 online resource (141 pages)9783658446871 Intro -- Acknowledgements -- Abstract -- Kurzzusammenfassung -- Contents -- List of Figures -- List of Tables -- 1 Introduction -- 2 A Deep Learning Approach for TUG and SPPB Score Prediction of (Pre-) Frail Older Adults on Real-Life IMU Data -- 2.1 Introduction -- 2.2 State of the Art -- 2.3 Methods and Materials -- Data Acquisition -- Preprocessing -- Network Architecture -- 2.4 Results -- 2.5 Discussion -- 2.6 Conclusions and Future Work -- References -- 3 Detecting Impending Malnutrition of (Pre-) Frail Older Adults in Domestic Smart Home Environments -- 3.1 Introduction -- 3.2 State of the Art -- Nutritional Intake and Body Weight -- Activities of Daily Living -- 3.3 Materials and Methods -- Data Acquisition -- Data Preprocessing -- Meal Preparation Time Estimation -- Minimal Clinically Important Difference -- Statistical Analysis -- 3.4 Results -- 3.5 Discussion -- 3.6 Conclusions -- References -- 4 Using Sensor Graphs for Monitoring the Effect on the Performance of the OTAGO Exercise Program in Older Adults -- 4.1 Introduction -- 4.2 State of the Art -- 4.3 Materials and Methods -- Study Design -- Data Acquisition -- Sensor Graph -- Difference Function -- Preprocessing -- Statistical and Computational Methods -- 4.4 Results -- 4.5 Discussion -- 4.6 Conclusions -- References -- 5 Unsupervised Statistical Concept Drift Detection for Behaviour Abnormality Detection -- 5.1 Introduction -- 5.2 State of the Art -- 5.3 Materials and Methods -- Data Acquisition -- Data Preprocessing -- Activity Probability Maps -- Feature Engineering -- Unsupervised Statistical Concept Drift Detection -- Central Limit Theorem -- Variational Autoencoder -- Method Validation -- 5.4 Results -- Underlying Distribution Approximation -- Artificial dataset -- Real-world Dataset -- 5.5 Discussion -- 5.6 Conclusions -- References.6 A System for Monitoring the Functional Status of Older Adults in Daily Life -- 6.1 Introduction -- 6.2 State of the Art -- 6.3 Methods -- 6.4 Results -- 6.5 Discussion -- 6.6 Conclusion -- References -- 7 General Discussion -- Bibliography.Friedrich Bjö1743761MiAaPQMiAaPQMiAaPQ9910869164603321Empowering Independent Living Using the ICF4171969UNINA