Lifelogging system based on averaged Hidden Markov Models: dangerous activities recognition for caregiver support

• Autorzy: Postawka A. , Rudy J.

Identyfikatory

DOI

10.7494/csci.2018.19.3.2855

• Języki publikacji: EN

Abstrakty

EN

In this paper, a prototype lifelogging system for monitoring people with cognitive disabilities and elderly people as well as a method for the automatic detection of dangerous activities are presented. The system allows for the remote monitoring of observed people via an Internet website and respects the privacy of the people by displaying their silhouettes instead of their actual images. The application allows for the viewing of both real-time and historical data. The lifelogging data (skeleton coordinates) needed for posture and activity recognition are acquired using Microsoft Kinect 2.0. Several activities are marked as potentially dangerous and generate alarms sent to caregivers upon detection. Recognition models are developed using Averaged Hidden Markov Models with multiple learning sequences. Action recognition includes methods for dierentiating between normal and potentially dangerous activities (e.g., self-aggressive autistic behavior) using the same motion trajectory. Some activity recognition examples and results are presented.

Słowa kluczowe

EN

lifelogging; abnormal human activity recognition; machine vision; Microsoft Kinect; Hidden Markov Models

• Wydawca: Wydawnictwa AGH
• Czasopismo: Computer Science
• Rocznik: 2018
• Tom: Vol. 19 (3)
• Strony: 257--278
• Opis fizyczny: Bibliogr. 23 poz., rys., wykr., tab.

Twórcy

autor

Postawka A.

• Wroclaw University of Science and Technology, Department of Computer Engineering, Wybrzeże Wyspiańskiego 27, 50-370 Wroclaw, Poland

autor

Rudy J.

• Wroclaw University of Science and Technology, Department of Computer Engineering, Wybrzeże Wyspiańskiego 27, 50-370 Wroclaw, Poland

Bibliografia

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Uwagi

PL

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2018).