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

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).

[1]  Rung Ching Chen,et al.  Bus Drivers Fatigue Measurement Based on Monopolar EEG , 2017, ACIIDS.

[2]  J. Roerdink,et al.  The influence of mental fatigue and motivation on neural network dynamics; an EEG coherence study , 2009, Brain Research.

[3]  Zahra Sedighi Maman,et al.  A data-driven approach to modeling physical fatigue in the workplace using wearable sensors. , 2017, Applied ergonomics.

[4]  Fillia Makedon,et al.  CPLAY2: An HCI Game System for the Assessment and Intervention of Children with Cerebral Palsy , 2016, PETRA.

[5]  Dimitri van der Linden,et al.  The urge to stop: The cognitive and biological nature of acute mental fatigue. , 2011 .

[6]  Sandra Weintraub,et al.  II. NIH Toolbox Cognition Battery (CB): measuring executive function and attention. , 2013, Monographs of the Society for Research in Child Development.

[7]  R. Chervin,et al.  Fatigue in multiple sclerosis: mechanisms, evaluation, and treatment. , 2010, Sleep.

[8]  Madhav Erraguntla,et al.  Measuring Fatigue through Heart Rate Variability and Activity Recognition: A Scoping Literature Review of Machine Learning Techniques , 2017 .

[9]  H. Heinze,et al.  Electrophysiological and behavioral effects of frontal transcranial direct current stimulation on cognitive fatigue in multiple sclerosis , 2018, Journal of Neurology.

[10]  Rohit Bakshi,et al.  Fatigue associated with multiple sclerosis: diagnosis, impact and management , 2003, Multiple sclerosis.

[11]  A. K. Lall,et al.  Mental Fatigue Quantification by Physiological and Neurophysiological Techniques: An Overview , 2018 .

[12]  Burcin Becerik-Gerber,et al.  Monitoring fatigue in construction workers using physiological measurements , 2017 .

[13]  E. Chiauzzi,et al.  Patient-centered activity monitoring in the self-management of chronic health conditions , 2015, BMC Medicine.

[14]  Andra M. Smith,et al.  Predictive Models of Cognitive Fatigue in Multiple Sclerosis , 2019, Archives of clinical neuropsychology : the official journal of the National Academy of Neuropsychologists.

[15]  N. Larocca,et al.  The fatigue severity scale. Application to patients with multiple sclerosis and systemic lupus erythematosus. , 1989, Archives of neurology.

[16]  P S Freedson,et al.  Objective Monitoring of Physical Activity Using Motion Sensors and Heart Rate , 2000, Research quarterly for exercise and sport.

[17]  S. Slobounov,et al.  EEG correlates of fatigue during administration of a neuropsychological test battery , 2012, Clinical Neurophysiology.

[18]  M. Decramer,et al.  Quantifying physical activity in daily life with questionnaires and motion sensors in COPD , 2006, European Respiratory Journal.

[19]  Fabio Babiloni,et al.  EEG activity as an objective measure of cognitive load during effortful listening: A study on pediatric subjects with bilateral, asymmetric sensorineural hearing loss. , 2017, International journal of pediatric otorhinolaryngology.

[20]  Ludwig Kappos,et al.  Effect of early versus delayed interferon beta-1b treatment on disability after a first clinical event suggestive of multiple sclerosis: a 3-year follow-up analysis of the BENEFIT study , 2007, The Lancet.