Application of artificial neural networks in fall prediction

• Autorzy: Walczak Tomasz , Sopa Martyna , Pogorzała Adam M.

Identyfikatory

DOI

10.21008/j.0860-6897.2021.2.10

• Języki publikacji: EN

Abstrakty

EN

The problem of fall is still unsolved even though it is a serious problem, especially in group of elderly. Also, another difficulty is to analyse falls that occur in day-to-day life. Those events are hard to observe by specialists and so it is hard to analyse them. Following work contains a description of experimental process for external force-caused fall observation with the use of motion capture system and dynamometric platforms. Data collected according to this protocol were later used for time series neural networks. Obtained results of analysis were compared to popular model of human stability. Conducted inquiry proves that it is possible to detect fall even before it occurs and while it is external force-caused fall the loss of stability develops earlier than it was assumed.

Słowa kluczowe

EN

fall detection; time series neural networks; motion capture; stability

PL

detekcja upadków; szeregi czasowe; sieci neuronowe; przechwytywanie ruchu; stabilność

• Wydawca: Poznan University of Technology. Institute of Applied Mechanics
• Czasopismo: Vibrations in Physical Systems
• Rocznik: 2021
• Tom: Vol. 32, nr 2
• Strony: art. no. 2021210
• Opis fizyczny: Bibliogr. 13 poz., il. (w tym kolor.), 1 fot., wykr.

Twórcy

autor

Walczak Tomasz

• Institute of Applied Mechanics, Faculty of Mechanical Engineering, Poznan University of Technology, Jana Pawła II 24, 60-965 Poznań, Poland

autor

Sopa Martyna

• Institute of Applied Mechanics, Faculty of Mechanical Engineering, Poznan University of Technology, Jana Pawła II 24, 60-965 Poznań, Poland

autor

Pogorzała Adam M.

• Institute of Applied Mechanics, Faculty of Mechanical Engineering, Poznan University of Technology, Jana Pawła II 24, 60-965 Poznań, Poland

Bibliografia

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).