Situation Model for Situation-Aware Assistance of Dementia Patients in Outdoor Mobility

被引:15
|
作者
Yordanova, Kristina [1 ]
Koldrack, Philipp [1 ,2 ]
Heine, Christina [2 ,3 ]
Henkel, Ron [1 ]
Martin, Mike [4 ,5 ]
Teipel, Stefan [2 ,3 ]
Kirste, Thomas [1 ]
机构
[1] Univ Rostock, Dept Comp Sci, Albert Einstein Str 22, D-18059 Rostock, Germany
[2] German Ctr Neurodegenerat Dis DZNE, Rostock, Germany
[3] Univ Rostock, Dept Psychosomat & Psychotherapeut Med, Rostock, Germany
[4] Univ Zurich, Dept Psychol Gerontopsychol & Gerontol, Zurich, Switzerland
[5] Univ Zurich, Univ Res Prior Program Dynam Hlth Aging, Zurich, Switzerland
关键词
Alzheimer's disease; assistance; data collection; dementia; knowledge base; mobility limitation; situation awareness; ALZHEIMERS-DISEASE; ONTOLOGY; PROGRESSION; PATTERNS; VIDEO;
D O I
10.3233/JAD-170105
中图分类号
Q189 [神经科学];
学科分类号
071006 ;
摘要
Background: Dementia impairs spatial orientation and route planning, thus often affecting the patient's ability to move outdoors and maintain social activities. Situation-aware deliberative assistive technology devices (ATD) can substitute impaired cognitive function in order to maintain one's level of social activity. To build such a system, one needs domain knowledge about the patient's situation and needs. We call this collection of knowledge situation model. Objective: To construct a situation model for the outdoor mobility of people with dementia (PwD). The model serves two purposes: 1) as a knowledge base from which to build an ATD describing the mobility of PwD; and 2) as a codebook for the annotation of the recorded behavior. Methods: We perform systematic knowledge elicitation to obtain the relevant knowledge. The OBO Edit tool is used for implementing and validating the situation model. The model is evaluated by using it as a codebook for annotating the behavior of PwD during a mobility study and interrater agreement is computed. In addition, clinical experts perform manual evaluation and curation of the model. Results: The situation model consists of 101 concepts with 11 relation types between them. The results from the annotation showed substantial overlapping between two annotators (Cohen's kappa of 0.61). Conclusion: The situation model is a first attempt to systematically collect and organize information related to the outdoor mobility of PwD for the purposes of situation-aware assistance. The model is the base for building an ATD able to provide situation-aware assistance and to potentially improve the quality of life of PwD.
引用
收藏
页码:1461 / 1476
页数:16
相关论文
共 50 条
  • [1] Situation-aware mobile assistance
    Kirste, T
    [J]. FRONTIERS OF HUMAN-CENTRED COMPUTING, ONLINE COMMUNITIES AND VIRTUAL ENVIRONMENTS, 2001, : 99 - 115
  • [2] An architecture for situation-aware driver assistance systems
    Roeckl, Matthias
    Robertson, Patrick
    Frank, Korbinian
    Strang, Thomas
    [J]. 2007 IEEE 65TH VEHICULAR TECHNOLOGY CONFERENCE, VOLS 1-6, 2007, : 2555 - 2559
  • [3] A situation-aware support model and its implementation
    Cheng, Zixue
    Huang, Tongjun
    [J]. FCST 2006: JAPAN-CHINA JOINT WORKSHOP ON FRONTIER OF COMPUTER SCIENCE AND TECHNOLOGY, PROCEEDINGS, 2006, : 172 - +
  • [4] Situation-aware wireless networks
    Sharma, S
    Nix, AR
    Olafsson, S
    [J]. IEEE COMMUNICATIONS MAGAZINE, 2003, 41 (07) : 44 - 50
  • [5] Situation-Aware Robots and Transporters
    Jamsa, Joni
    Pieska, Sakari
    Luimula, Mika
    Siio, Itiro
    Komatsuzaki, Mizuho
    [J]. 3RD IEEE INTERNATIONAL CONFERENCE ON COGNITIVE INFOCOMMUNICATIONS (COGINFOCOM 2012), 2012, : 345 - 350
  • [6] A Formal Definition of Situation towards Situation-Aware Computing
    Kim, Minsoo
    Kim, Minkoo
    [J]. VISIONING AND ENGINEERING THE KNOWLEDGE SOCIETY: A WEB SCIENCE PERSPECTIVE, PROCEEDINGS, 2009, 5736 : 553 - +
  • [7] An ontological model to support communications of situation-aware vehicles
    Choi, Seong Kyu
    [J]. TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES, 2015, 53 : 112 - 133
  • [8] A Situation-Aware Fear Learning (SAFEL) model for robots
    Rizzi, Caroline
    Johnson, Colin G.
    Fabris, Fabio
    Vargas, Patricia A.
    [J]. NEUROCOMPUTING, 2017, 221 : 32 - 47
  • [9] Situation-Aware Model Refinement for Semantic Image Segmentation
    Habermayr, Lukas
    Hofbauer, Markus
    Zacchi, Joao-Vitor
    Kuhn, Christopher B.
    [J]. 2021 IEEE INTELLIGENT TRANSPORTATION SYSTEMS CONFERENCE (ITSC), 2021, : 2696 - 2702
  • [10] Situation recognition and handling based on executing situation templates and situation-aware workflows
    Hirmer, Pascal
    Wieland, Matthias
    Schwarz, Holger
    Mitschang, Bernhard
    Breitenbucher, Uwe
    Saez, Santiago Gmez
    Leymann, Frank
    [J]. COMPUTING, 2017, 99 (02) : 163 - 181