Description
Title: LIFELOGGING SYSTEM BASED ON HIDDEN MARKOV MODELS ON THE AVERAGE: IDENTIFICATION OF DANGEROUS ACTIVITIES FOR CAREGIVER SUPPORT
Abstract: This paper presents a prototype lifelogging system for elderly people and people with cognitive impairments, as well as a method for the automatic detection of hazardous activities. The system respects the privacy of the users by showing their silhouettes rather than their actual images and enables remote monitoring of observed people via an Internet website. Real-time and historical data can both be viewed using the application. Microsoft Kinect 2.0 is used to collect the lifelogging data (skeleton coordinates) required for posture and activity recognition. Several actions are flagged as potentially hazardous, and when they are, alarms are sent to caregivers. Utilizing averaged hidden markov models with multiple learning iterations, recognition models are created. Action recognition techniques include ways to distinguish between typical behaviors and those that could be dangerous (such as self-aggressive autistic behavior) while still using the same motion trajectory. Examples and findings related to activity recognition are provided.
Keywords: lifelogging, abnormal human activity recognition, Hidden Markov Models, machine vision, Microsoft Kinect
Paper Quality: SCOPUS / Web of Science Level Research Paper
Paper type: Analysis Based Research Paper
Subject: Computer Science
Writer Experience: 20+ Years
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