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BEGIN:VEVENT
DTSTAMP:20220215T151535Z
UID:A95BC3C6-4885-47A7-AFF2-C338544B31B7
DTSTART;TZID=Canada/Eastern:20220208T113000
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DESCRIPTION:Cognitive load experienced by humans is an important factor aff
 ecting their performance. Cognitive overload or underload may result in su
 boptimal human performance and may compromise safety. In driving\, cogniti
 ve overload\, due to various secondary tasks\, such\n\nas texting\, result
 s in distracted driving. On the other hand\, cognitive underload may resul
 t in fatigue. In manufacturing\, a distracted operator may be prone to mus
 cle injuries. Similar results are likely in many other fields of human per
 formance. Cognitive load is not directly measurable\n\nand neither is ther
 e a unit for it. However\, the change in cognitive load can be indirectly 
 measured through various physiological\, behavioral\, performance based an
 d subjective means. In this presentation\, a performance metric for compar
 ison of different metrics to determine the cognitive workload is proposed 
 in terms of the signal to noise ratio. Using this\n\nperformance metric\, 
 several measures of cognitive load\, that fall under the four broad groups
  of physiological\, behavioral\, performance based\, and self-reported mea
 sures\, were compared for their ability to measure changes in cognitive lo
 ad. Using the proposed metrics\, the cognitive load measures were compared
  based on data collected from 28 participants while they underwent n-back 
 tasks. The results show that the proposed performance evaluation method ca
 n be useful to individually assess different measures of cognitive load. F
 inally\, the role of signal processing to improve cognitive load measures 
 is highlighted.\n\nSpeaker(s): Dr. Balakumar Balasingam \, \n\nOttawa\, On
 tario\, Canada\, Virtual: https://events.vtools.ieee.org/m/302800
LOCATION:Ottawa\, Ontario\, Canada\, Virtual: https://events.vtools.ieee.or
 g/m/302800
ORGANIZER:sreeramanr@sce.carleton.ca
SEQUENCE:3
SUMMARY:Approaches for Cognitive Load Measurement for Human-System Automati
 on
URL;VALUE=URI:https://events.vtools.ieee.org/m/302800
X-ALT-DESC:Description: <br /><p>Cognitive load experienced by humans is an
  important factor affecting their performance. Cognitive overload or under
 load may result in suboptimal human performance and may compromise safety.
  In driving\, cognitive overload\, due to various secondary tasks\, such</
 p>\n<p>as texting\, results in distracted driving. On the other hand\, cog
 nitive underload may result in fatigue. In manufacturing\, a distracted op
 erator may be prone to muscle injuries. Similar results are likely in many
  other fields of human performance. Cognitive load is not directly measura
 ble</p>\n<p>and neither is there a unit for it. However\, the change in co
 gnitive load can be indirectly measured through various physiological\, be
 havioral\, performance based and subjective means. In this presentation\, 
 a performance metric for comparison of different metrics to determine the 
 cognitive workload is proposed in terms of the signal to noise ratio. Usin
 g this</p>\n<p>performance metric\, several measures of cognitive load\, t
 hat fall under the four broad groups of physiological\, behavioral\, perfo
 rmance based\, and self-reported measures\, were compared for their abilit
 y to measure changes in cognitive load. Using the proposed metrics\, the c
 ognitive load measures were compared based on data collected from 28 parti
 cipants while they underwent n-back tasks. The results show that the propo
 sed performance evaluation method can be useful to individually assess dif
 ferent measures of cognitive load. Finally\, the role of signal processing
  to improve cognitive load measures is highlighted.</p>
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