TY - JOUR

T1 - Emergency detection based on probabilistic modeling in AAL environments

AU - Busch, Björn-Helge

AU - Kujath, Alexander

AU - Welge, Ralph

PY - 2011

Y1 - 2011

N2 - The actual demographic trend predicts a significant increasing percentage of elderly people in the German society until 2050. According to this trend, it must be assumed that the number of elderly persons solitarily living at home will rise as well. In this paper a human centered assistance system is tightly described and proposed as a resolve for the coherent challenges of this changes; the focus of consideration lies thereby on the statistical analysis of process data in order to derive an intelligent emergency detection based on probabilistic modeling. Using room automation events to design and train Hidden Markov Models for position tracking, complemented with the stochastically evaluation of telemedical de-vices and contactless sensor networks, the main issue is to achieve a solid, robust situation recognition mechanism. Due to the knowledge of the hidden user states or i.e. situations this approach offers the opportunity to detect emergency situa-tions and to prevent harmful aftermath for the user through interventions like emergency calls. This paper illustrates the general functionality of the proposed solution and its key features by a vivid example. In addition to security aspects relating to the health status of the patient the recognition of the activities of daily life grants further advantageous options like energy management, comfort by assistance and building security completely adapted to people requirements.

AB - The actual demographic trend predicts a significant increasing percentage of elderly people in the German society until 2050. According to this trend, it must be assumed that the number of elderly persons solitarily living at home will rise as well. In this paper a human centered assistance system is tightly described and proposed as a resolve for the coherent challenges of this changes; the focus of consideration lies thereby on the statistical analysis of process data in order to derive an intelligent emergency detection based on probabilistic modeling. Using room automation events to design and train Hidden Markov Models for position tracking, complemented with the stochastically evaluation of telemedical de-vices and contactless sensor networks, the main issue is to achieve a solid, robust situation recognition mechanism. Due to the knowledge of the hidden user states or i.e. situations this approach offers the opportunity to detect emergency situa-tions and to prevent harmful aftermath for the user through interventions like emergency calls. This paper illustrates the general functionality of the proposed solution and its key features by a vivid example. In addition to security aspects relating to the health status of the patient the recognition of the activities of daily life grants further advantageous options like energy management, comfort by assistance and building security completely adapted to people requirements.

KW - Informatics

M3 - Journal articles

VL - 6

SP - 122

EP - 137

JO - IADIS International Journal on Computer Science and Information Systems

JF - IADIS International Journal on Computer Science and Information Systems

SN - 1646-3692

IS - 1

ER -