Towards a task-driven framework for multimodal fatigue analysis during physical and cognitive tasks

被引：2
|
作者：
Tsiakas, Konstantinos [1 ]
Papakostas, Michalis [2 ]
Ford, James C. [3 ]
Makedon, Fillia [2 ]
机构：
[1] Yale Univ, Dept Psychiat, New Haven, CT 06520 USA
[2] Univ Texas Arlington, Comp Sci & Engn Dept, Arlington, TX 76019 USA
[3] Geisel Sch Med, Dept Psychiat, Dartmouth, NS, Canada
来源：
5TH INTERNATIONAL WORKSHOP ON SENSOR-BASED ACTIVITY RECOGNITION AND INTERACTION (IWOAR 2018) | 2018年
基金：
美国国家科学基金会;
关键词：
Human monitoring; Computer Vision; Human-Computer Interaction; Fatigue Estimation; Cognitive and Physical Training; MULTIPLE-SCLEROSIS; MOTION SENSORS;
D O I：
10.1145/3266157.3266222
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
This paper outlines the development of a task-driven framework for multimodal fatigue analysis during physical and cognitive tasks. While fatigue is a common symptom across several neurological chronic diseases, such as multiple sclerosis (MS), traumatic brain injury (TBI), cerebral palsy (CP) and others, it remains poorly understood, due to various reasons, including subjectivity and variability amongst individuals. Towards this end, we propose a task-driven data collection framework for multimodal fatigue analysis, in the domain of MS, combining behavioral, sensory and subjective measures, while users perform a set of both physical and cognitive tasks, including assessment tests and Activities of Daily Living (ADLs).
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页数：3
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