Human gait is a dynamic biometrical feature that describes the kinematics of human walking. Gait modeling is studied in order to find a pattern of walking that can be used for diagnosis of walking disorder or abnormal walk detection. Difficulty in walking progressively increases with aging and causes unintentional falls, which is a common incident among elderly people. Fall prediction systems can help to prevent unintentional falls that could cause serious injuries, therefore they can reduce the health service costs. This paper presents an algorithm with polynomial classification model of human gait for real-time fall prediction. This approach enables the user to detect the transition from a normal to an abnormal walking pattern. A dataset based on the state-of-the-art techniques in simulating abnormal walks was created by using an accelerometer embedded in a smartphone, which is recognized to be precise enough for fall avoidance systems. The proposed approach improves state-of-the-art fall prediction approaches, by achieving 99.2% of accuracy in abnormal walk detection.

Polynomial classification model for real-time fall prediction system / HEMMATPOUR, MASOUD; KARIMSHOUSHTARI, MILAD; FERRERO, RENATO; MONTRUCCHIO, BARTOLOMEO; REBAUDENGO, Maurizio; NOVARA, Carlo. - ELETTRONICO. - 1:(2017), pp. 973-978. ((Intervento presentato al convegno 2017 IEEE 41st Annual Computer Software and Applications Conference (COMPSAC) tenutosi a Torino nel 4-8 June 2017 [10.1109/COMPSAC.2017.189].

Polynomial classification model for real-time fall prediction system

HEMMATPOUR, MASOUD;KARIMSHOUSHTARI, MILAD;FERRERO, RENATO;MONTRUCCHIO, BARTOLOMEO;REBAUDENGO, Maurizio;NOVARA, Carlo
2017

Abstract

Human gait is a dynamic biometrical feature that describes the kinematics of human walking. Gait modeling is studied in order to find a pattern of walking that can be used for diagnosis of walking disorder or abnormal walk detection. Difficulty in walking progressively increases with aging and causes unintentional falls, which is a common incident among elderly people. Fall prediction systems can help to prevent unintentional falls that could cause serious injuries, therefore they can reduce the health service costs. This paper presents an algorithm with polynomial classification model of human gait for real-time fall prediction. This approach enables the user to detect the transition from a normal to an abnormal walking pattern. A dataset based on the state-of-the-art techniques in simulating abnormal walks was created by using an accelerometer embedded in a smartphone, which is recognized to be precise enough for fall avoidance systems. The proposed approach improves state-of-the-art fall prediction approaches, by achieving 99.2% of accuracy in abnormal walk detection.
File in questo prodotto:
File Dimensione Formato  
FerreroRenato-Polynomial.pdf

non disponibili

Tipologia: 2a Post-print versione editoriale / Version of Record
Licenza: Non Pubblico - Accesso privato/ristretto
Dimensione 459.9 kB
Formato Adobe PDF
459.9 kB Adobe PDF   Visualizza/Apri   Richiedi una copia
Pubblicazioni consigliate

Caricamento pubblicazioni consigliate

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11583/2680726
 Attenzione

Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo