Differences in Evacuation Responses from Word Choice. Bryan L. Hoskins, Ph.D. Oklahoma State University

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2 Differences in Evacuation Responses from Word Choice Bryan L. Hoskins, Ph.D. Oklahoma State University

3 Outline Introduction Study Design Variables Results Discussion

4 Introduction Voice systems Effective at communicating message Becoming more prevalent Messages need to be developed Smart buildings

5 Introduction NIST study Important types of information to give How people process information In Annex of NFPA72

6 Study Design Participants All college students in major Two different routes for each 43 individuals 14 groups (34 individuals)

7 Study Design Location Student Union Floor that they were typically unfamiliar with Instructed on route to take to arrive Many other people present

8 Study Design Instructions Fire in the building (specific location) Told to only take designated route Directions different based on variables Four steps to remember

9 Variables Familiar vs. unfamiliar Native language Single vs. repeated vs. half at a time Landmarks vs. compass vs. turn direction

10 Results Individuals All participants able to finish both routes 39 (45 %) wrong routes Comparisons with t-tests

11 Results Order of scenarios 22 had familiar route first 21 had unfamiliar route first No apparent effects based on which route was first

12 Results Native language 30 English as native language 13 English not native language No apparent effects based on native language in total mistakes

13 Results Instructions 15 had instructions only given at start 14 had half of instructions at start, half at midpoint 14 had repetition of message at midpoint

14 Results Instructions Instructions at start significantly worse, especially for unfamiliar route Repeated instructions about average Divided messages had best results

15 Results Type of directions 15 had turn notation (e.g. left or right) 14 had landmarks 14 had compass directions

16 Results Type of directions Compass directions were by far the worse Qualitative differences between landmarks and turns

17 Results Groups All groups able to finish both routes 13 (46 %) wrong routes 3 (10 %) had group correct Similar trends to individuals

18 Discussion Less is more Use guidance that is familiar to people Anything that involves a compass direction (south stair) is problematic Groups will communicate

19 Questions?

21 Biomechanical Characterization of Persons with Movement Disorders during Simulated Evacuations: A Pilot Study Richard Kesler Illinois Fire Service Institute University of Illinois Urbana-Champaign, IL

22 Outline State of Egress Data Movement Disorders Goals Experiment Design Evacuation Performance Summary Next Steps

23 State of Egress Data Recent increase in examination of unique situations and populations Stairs 1 Hospitals 2 Learning disorders 3 Others Opportunity at IFSI Access to unique population New type of biomechanical analysis 1. Kuligowski, E., Peacock, R., Wiess, E., & Hoskins, B. (2015). Stair Evacuation of People with Mobility Impairments. Fire and Materials, 39(4), Golmohammadi, D., & Shimshak, D. (2011). Estimation of the Evacuation Time in an Emergency Situation in Hospitals. Computers & Industrial Engineering, 61(4), Shields, T. J., Smyth, B., Boyce, K. E., & Silcock, G. W. H. (1999). Evacuation Behaviours of Occupants with Learning Difficulties in Residential Homes. Disability and Rehabilitation, 21(1),

24 Egress for Persons with Movement Disorders Challenges to those with impairments Buildings increase in size and complexity Access improvements for ADA Challenges to researchers and engineers Increased population with movement disorders Require specialized studies

Movement Disorders-United States >9.5 million cases 1 Projected 20 million by 2050 1 150-400,000 Multiple Sclerosis cases 1,2 1. Bach, J.-P., et al.

25 Movement Disorders-United States >9.5 million cases 1 Projected 20 million by ,000 Multiple Sclerosis cases 1,2 1. Bach, J.-P., et al. (2011), Projected numbers of people with movement disorders in the years 2030 and Mov. Disord., 26: doi: /mds Motl, R. W. (2013). Ambulation and Multiple Sclerosis. Phys Med Rehabil Clin N Am, 24(2),

26 Goals Examine impact of fatigue Characterize movement of MS population across level ground Examine impact of door design on door passage time Examine the usefulness of biomechanical techniques in future analyses

27 Experiment Design Protocol Familiarization Instrumentation Door operation and swing direction Baseline data collection 6 minute walk Post-activity data collection Approved by Illinois IRB

28 Recruited Population 15 Adults with MS 9 High EDSS/Low Function 6 Low EDSS/High Function 15 Age Matched Adults 12 Young Controls Age (years) Weight (kg) Height (cm) Multiple Sclerosis (MS) 54 ± ± ± 8.8 Age Matched Controls (AMC) 50 ± ± ± 11.6 Young Controls (YC) 21 ± ± ± 7.8

29 Baseline Data Collection Complete single lap as fast as possible 48 meters long Well lit Tile floor except for gait mat All doors closed/unlocked No other obstructions Wearing athletic clothes/shoes Door 2 Metrics Lap Time Individual Door Passage Times Door 3 Door 5 Door 4 Door 1

30 Baseline Data Collection Direction Handle Latch Closer Opening Force (N) Door 1 Pull Twist Yes Yes 33.7 Door 2 Push Yes 30.8 Door 3 Pull Twist Yes Door 4 Pull Twist Yes Yes 50.4 Door 5 Push Panic Yes Yes 28.8 Door 3 Door 2 Metrics Lap Time Individual Door Passage Times Door 5 Door 4 Door 1

31 Baseline Data Collection Direction Handle Latch Closer Opening Force (N) Door 1 Pull Twist Yes Yes 33.7 Door 2 Push Yes 30.8 Door 3 Pull Twist Yes Door 4 Pull Twist Yes Yes 50.4 Door 5 Push Panic Yes Yes 28.8

32 Baseline Data Collection Direction Handle Latch Closer Opening Force (N) Door 1 Pull Twist Yes Yes 33.7 Door 2 Push Yes 30.8 Door 3 Pull Twist Yes Door 4 Pull Twist Yes Yes 50.4 Door 5 Push Panic Yes Yes 28.8

33 Baseline Data Collection Direction Handle Latch Closer Opening Force (N) Door 1 Pull Twist Yes Yes 33.7 Door 2 Push Yes 30.8 Door 3 Pull Twist Yes Door 4 Pull Twist Yes Yes 50.4 Door 5 Push Panic Yes Yes 28.8

34 Baseline Data Collection Direction Handle Latch Closer Opening Force (N) Door 1 Pull Twist Yes Yes 33.7 Door 2 Push Yes 30.8 Door 3 Pull Twist Yes Door 4 Pull Twist Yes Yes 50.4 Door 5 Push Panic Yes Yes 28.8

35 Baseline Data Collection Direction Handle Latch Closer Opening Force (N) Door 1 Pull Twist Yes Yes 33.7 Door 2 Push Yes 30.8 Door 3 Pull Twist Yes Door 4 Pull Twist Yes Yes 50.4 Door 5 Push Panic Yes Yes 28.8

36 Baseline Data Collection Direction Handle Latch Closer Opening Force (N) Door 1 Pull Twist Yes Yes 33.7 Door 2 Push Yes 30.8 Door 3 Pull Twist Yes Door 4 Pull Twist Yes Yes 50.4 Door 5 Push Panic Yes Yes 28.8 Door 3 Door 2 Metrics Lap Time Individual Door Passage Times Door 5 Door 4 Door 1

37 6 Minute Walk Performance measure of walking endurance well established in MS community Walk as far and as fast as possible for 6 minutes Each lap 43 meters long All doors open Metrics Metabolic Consumption Gait Metrics

38 Instrumentation Metabolic Monitoring Cosmed K4b 2 portable monitoring unit Measures energy expenditure via oxygen consumption

39 Instrumentation Gait 26 ft long GAITRite gait mat Outputs traditional gait parameters Double Support Time Velocity

40 Post activity Data Collection Complete single lap as fast as possible Door 3 All doors closed Door 2 Metrics Lap Time Individual Door Passage Times Door 5 Door 4 Door 1

41 Evacuation Performance Door Passage Time Fastest with no latch/handle Faster when pushing Higher force = slower time Time (seconds) Door Passage Time (All Populations) * * * No statistical difference between doors 1 and 3 All others significantly different from each other (p<0.05)

42 Evacuation Performance Course Completion Time Much slower completion times in MS population Greater variability in MS population Time (seconds) Evacuation Course Completion Time * MS AMC YC * MS significantly different from AMC and YC (p<0.05)

43 Evacuation Performance Course Completion Time Classification by EDSS reveals cause of variability Time (seconds) Evacuation Course Completion Time * MS (High EDSS) MS (Low EDSS) AMC YC * MS (High EDSS) significantly different all others (p<0.05)

44 Evacuation Performance 6 Minute Walk Distance Travelled Significantly less distance for all MS Is there a difference if we look at EDSS? Distance (meters) Minute Walk - Distance Travelled * MS AMC YC * MS significantly different from AMC and YC (p<0.05)

45 Evacuation Performance 6 Minute Walk Distance Travelled Breakdown by EDSS High EDSS Shorter Distance Distance (meters) Minute Walk - Distance Travelled * MS (High EDSS) MS (Low EDSS) AMC YC * MS (High EDSS) significantly different from all others (p<0.05) MS (Low EDSS) significantly different from all others

46 Evacuation Performance Biomechanical Metrics Ando: General elderly population 20% slower Minimal difference found here MS population agrees with Boyce disabled (no-aid) population Age Match and Young significantly faster than Boyce healthy adults 1.25m/s vs our 2m/s) Time (seconds) Double Support Time * * MS AMC YC Velocity (m/s) * * First Lap Stride Length (cm) Velocity * * Last Lap Stride Length MS AMC YC * MS significantly different from all others (p<0.05) 0.0 MS AMC YC K. Ando, H. Ota, and T. Oki, Forecasting the flow of people, Railway Research Review 45 no. 8, (1988) Boyce, K. E., Shields, T. J., & Silcock, G. W. H. (1999). Toward the Characterization of Building Occupancies for Fire Safety Engineering: Capabilities of Disabled People Moving Horizontally and on an Incline. Fire Technology, 35(1),

47 Evacuation Performance Metabolic Energy Expenditure during 6MW No difference between populations Slightly less than VO 2 found in other populations 1,2 VO 2 (ml/min/kg) Oxygen Consumption MS AMC YC Average Maximum No statistical differences 1. Kern, L., Condrau, S., Baty, F., Wiegand, J., van Gestel, A. J., Azzola, A., Tamm, M., & Brutsche, M. (2014). Oxygen Kinetics During 6-Minute Walk Tests in Patients with Cardiovascular and Pulmonary Disease. BMC pulmonary medicine, 14, Kervio, G., Carre, F., & Ville, N. S. (2003). Reliability and Intensity of the Six-Minute Walk Test in Healthy Elderly Subjects. Medicine and science in sports and exercise, 35(1),

48 Evacuation Performance Oxygen Cost of Walking during 6MW High EDSS score Significantly higher O 2 CW than others Same trend observed by Sandroff All other populations agree with Waters ml O 2 /kg/m * MS (High EDSS) Oxygen Cost of Walking MS (Low EDSS) AMC YC * MS (High EDSS) significantly different from all others (p<0.05) Brian M. Sandroff, Rachel E. Klaren, Lara A. Pilutti, and Robert W. Motl, Oxygen Cost of Walking in Persons with Multiple Sclerosis: Disability Matters, but Why?, Multiple Sclerosis International, vol. 2014, Article ID , 7 pages, doi: /2014/ Waters, R. L., & Mulroy, S. (1999). The Energy Expenditure of Normal and Pathologic Gait. Gait Posture, 9(3),

49 Summary Effect of Door Design Door direction and handle style significantly impact door passage time MS population: Time delay in opening door should be incorporated into building evacuation models

50 Summary Updated movement data for MS population No pre/post differences on level ground Maybe with longer walks? Disability negatively affected time EDSS score correlated to distance travelled Future models should account for increasing prevalence of movement disorders

51 Next Steps Analysis of motion capture data forthcoming Impacts of sway and unsteady gait Personal space occupied Non-level walking Stairs Ramps Other populations with movement concerns Parkinson s Disease Wheelchair users

Acknowledgments Dr. Gavin Horn - IFSI Research Univ.

Liz Hsiao-Wecksler Morgan Boes - Human Dynamics and Controls

Rob Motl Rachel Klaren - Exercise Neuroscience Research

52 Acknowledgments Dr. Gavin Horn - IFSI Research Univ. of Illinois, Fire Service Institute Dr. Liz Hsiao-Wecksler Morgan Boes - Human Dynamics and Controls Laboratory Univ. of Illinois, Dept. of Mech. Engineering Dr. Rob Motl Rachel Klaren - Exercise Neuroscience Research Laboratory Univ. of Illinois, Dept. of Kinesiology and Community Health Thank you Questions?

54 Wearable Eye-Tracking Human Perception of Exit Signs During Building Evacuation Robert Till John Jay College of Criminal Justice New York, NY USA Dominick DeRubbio Agile Risk Group New York, NY USA

55 Overview - Demonstrate wearable eye-tracking technology as a tool to study pedestrian and evacuation dynamics Examine the visual behavior of individuals, both familiar and unfamiliar with a space, navigating toward an exit Use event-based coding analysis to summarize behaviors

56 Exit Behavior It is a generally accepted principle in fire protection that in an emergency most people will first attempt to leave a building the same way they entered. [McClintock et al., 2000]

57 Sometimes People Can t Mass transit stations passengers may be forced to disembark at unfamiliar locations High-rise buildings elevators are used as the primary means of entry, but stairs must be used in an emergency

58 Sometimes People Can t Airports Disembarking at an unfamiliar location, finding baggage, already checked through security. Exit Obstructions Areas where a person first gains entry to a facility may not be available.

59 Questions How do pedestrians behave when searching for the nearest exit under these conditions? Do they look at, and follow, signs? Do they take cues from others? Use some unknown navigation method? Perhaps a combination of these?

60 Why study eye movements? Conclusions from the body of research show that where people fixate (look) is closely linked to the attentional demands of the task and that wearable eye-tracking is a good tool for monitoring the sequential thought process.

61 Gaze During Natural Behavior Walking through a cluttered room [Franchak et al., 2009] Driving a car [Land and Lee, 1994] Making tea [Land et al., 1999] Washing hands [Pelz and Canosa, 2001] Playing table tennis [Land and Furneaux, 1997] Playing cricket [Land and McLeod, 2000]

62 Recording Behavior Fahy identified exit choice decisions, which determine travel paths and affect travel times as a type of data needed for evacuation models that we currently lack.

63 Recording Behavior She also stated videotaped observations of actual evacuations are ideal, since they show exactly what people did, and the elapsed time can be calculated directly from the tape.

64 Wearable eye tracking achieves Fahy s goals without obstructing the travel route of the wearer and with minimal influence on the wearer.

65 Study Goals 1. Determine if humans actually use exit signage.

66 Study Goals 2. Identify if there is a difference in the use of exit signs between those who were familiar and those who were unfamiliar with a building environment.

67 Hardware - Eye-Tracking Unit Eye Camera Scene Camera

68 Eye-Tracking Unit Eye Camera View Scene Camera View

69 Software to Weave The Views Together

70 Experts Worked in building or claimed to be familiar. Novices - Never visited the building before. Most walked from Start (room 3531) To Stair Exit B (Public Administration Dept.)

71 Environment - Floor

72 Data 2010 study 10 Subjects Later Study - 41 subjects tested/data from 40 used.

73 2010 Results suggested that novices used exit signs and experts did not.

74 Subject Evacuation Instructions Your goal is to locate a doorway that will take you outside of the building - i.e. you should be able to see daylight or a marked way out of the building - and take a picture of that location for documentation. Do not actually leave the building, and avoid passing through any turnstile, as this will require us to recheck you in through the security desk

75 Evacuation Instructions (2) Also, do not make use of any elevators. Remember, your goal is to simply "find" a way out of the building and document it. Once you have taken the picture, return to this office and the test will be complete

76 Experts Worked in building or claimed to be familiar.

77 Novices - Never visited the building before

78 Experts Worked in building or claimed to be familiar

79 Novices - Never visited the building before

80 Video - Expert

81 Video - Novice

82 Study Goals 1. Determine if humans actually use exit signage.

83 A total of 26 occupants or 65% of subjects tested utilized exit signs while identifying a way to the street. Clearly, some people use exit signs, novice or expert, judging from this small sample size.

84 Findings The average number of gazes at an exit sign per subject was 6.6.

85 Study Goal (2) Identify if there is a difference in the use of exit signs between those who were familiar and unfamiliar with the building environment.

86 Findings (61%) of the novices and (68%) of the experts gazed at exit signs while navigating the building.

87 Findings Novices recorded a total of 115 gazes at exit signs and experts recorded a total of 103 gazes.

88 Challenges and Conclusions Were the experts really expert? The outcome of the original experiment suggested that expert subjects didn t use exit signs they knew where to go. In later cases, experts did view the exit signs. It is possible that we had people self-identify as experts that were not?

89 Future Studies Increase sample size Work to better identify true experts and novices

90 Future Studies Create a more realistic experience (horns/strobes) Flashing versus non-flashing signs, height, color

91 Questions? Robert Till John Jay College of Criminal Justice New York, NY USA Dominick DeRubbio Agile Risk Group New York, NY USA

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