High-Stress Environments May 11 th 2018 Human-Robotic Interaction 2 - - PowerPoint PPT Presentation

high stress environments
SMART_READER_LITE
LIVE PREVIEW

High-Stress Environments May 11 th 2018 Human-Robotic Interaction 2 - - PowerPoint PPT Presentation

Adaptive Robotics for Jamison Heard Vanderbilt University PhD Candidate High-Stress Environments May 11 th 2018 Human-Robotic Interaction 2 Peer Mentor Roles Information Consumer High Stress Environments 3 Supervisory Peer Workload


slide-1
SLIDE 1

Adaptive Robotics for High-Stress Environments

Jamison Heard Vanderbilt University PhD Candidate May 11th 2018

slide-2
SLIDE 2

Peer Mentor

Information

Consumer

Roles Human-Robotic Interaction

2

slide-3
SLIDE 3

High Stress Environments

Peer Supervisory

3

slide-4
SLIDE 4

Workload and Performance

Overall Workload Performance

Poor Good

Underload Overload Normal Load Acceptable Level

4

slide-5
SLIDE 5

Workload Components

Overall Workload Cognitive Physical Auditory Visual Speech

5

slide-6
SLIDE 6

Objective Workload Metrics

Cognitive Physical Auditory Visual Speech

Brain-Activity Measures Cardiovascular Measures Eye-Tracking Measures Speech-Based Measures Respiration-Rate Noise Level Noise Level Speech-Response Time Postural Measures Heart-Rate Skin Temperature

6

slide-7
SLIDE 7

Challenges with Physiological Signals

Individual Differences Day-to-Day Variability Age Physical Fitness Training Circadian Rhythms Stressful Events

7

slide-8
SLIDE 8

Workload Assessment Algorithm Development and Validation

8

slide-9
SLIDE 9

Workload Assessment Algorithm

9

slide-10
SLIDE 10

Experimental Design

Peer-Based Evaluation Supervisory-Based Evaluation

10

slide-11
SLIDE 11

Experimental Design

Independent Variables

  • Workload
  • Peer
  • Low and High
  • Supervisory
  • Underload, Normal Load, and

Overload

  • Peer manipulated teaming

partner

  • Human or Robot

Dependent Variables

  • Subjective Workload Metrics
  • NASA TLX
  • In-Situ Workload Ratings
  • Objective Workload Metrics
  • Heart-Rate
  • Heart-Rate Variability
  • Skin-Temperature
  • Noise Level
  • Respiration-Rate
  • Posture Magnitude

11

slide-12
SLIDE 12

Peer-Based Evaluation

18 Participants 4 Tasks:

  • 1. Photo-Search

Hallway Search Liquid and Solid Containment Sampling

12

slide-13
SLIDE 13

Supervisory-Based Evaluation

30 Participants 4 Concurrent Tasks: NASA MATB

  • Tracking
  • System Monitoring
  • Resource Management
  • Communications

13

slide-14
SLIDE 14

Algorithm Evaluation

14

  • Four Analysis:
  • Population Generalizability
  • Train on 70% of the Participants, test on the other 30%
  • Cross-Teaming Generalizability
  • Train on Peer Evaluation Data, Test on Supervisory
  • Vice-Versa
  • Cross-Task Generalizability
  • Train on 3 Peer-based Tasks, Test on the 4th task
  • 4-Fold Cross-Validation
  • Cross-Interaction Generalizability
  • Train on Peer Human-Human Teaming Data, Test on Human-Robot Teaming
  • Vice-Versa
slide-15
SLIDE 15

SUP PEER BOTH SUP PEER BOTH SUP PEER BOTH Cognitive Workload Physical Workload Overall Workload

88 96 95 63 90 81 90 96 95 98 63 98 99 66 98 100 67 100

Average Classification Accuracy (%) by Workload Component and Evaluation

Peer Evaluation Supervisory Evaluation

15

Results: Population & Cross-Teaming

slide-16
SLIDE 16

Results: Cross-Task

T1 T2 T3 T4 T1 T2 T3 T4 T1 T2 T3 T4 Cognitive Workload Physical Workload Overall Workload

76 96 94 89 82 83 93 66 94 89 90 99

Average Classification Accuracy (%) by Workload Component and Peer Task

Peer Evaluation

16

T# represents peer-task number

slide-17
SLIDE 17

Results: Cross-Interaction

H-H H-R HH-HR H-H H-R HH-HR H-H H-R HH-HR Cognitive Workload Physical Workload Overall Workload

94 95 96 87 90 86 94 95 93

Average Classification Accuracy (%) by Workload Component and Interaction Paradigm

Peer Evaluation

H-H: Human-Human, H-R: Human-Robot, HH-HR: Human-Human and Human-Robot

17

slide-18
SLIDE 18

Workload Assessment Algorithm

Determine Adaptations Assess and Predict

Performance Prediction

i-CiFHaR

Interaction Decision Framework Workload Component Estimates Predicted Performance Task (Re-)Allocations Interaction Changes Workload Metrics Activity Recognition Workload Models

Adaptive Workload System Architecture

Current Tasks Future Tasks Communication Modality

Apply Adaptations

Envisioned System

18

slide-19
SLIDE 19

Activity Recognition

19

slide-20
SLIDE 20

Activity Recognition Background

20

External Sensors Wearable Sensors Cameras Environmental Physiological Accelerometer Gyroscope

slide-21
SLIDE 21

Current EMS Hand-Off Process

https://www.mlrems.org/patient-handoff/

21

slide-22
SLIDE 22

EMS Procedures

22

Airway Management

  • Placing an IV
  • Administer IV Medication
  • IO Line
  • Nasal Airway
  • Oral Airway
  • Intubation
  • Crike Kit
  • Chest Decompression
  • Cardiopulmonary Resuscitation (CPR)
  • Tourniquet
  • Combat Gauze
  • Splinting
  • Stethoscope
  • Placing Monitoring Equipment

Intravenous Therapy Vital Checking Wound and Fractures High Trauma

slide-23
SLIDE 23

Hierarchical Task Analysis

23

CPR Preparation Give Breaths Compressions Check for Breathing Lift Patient’s Chin Use Bag-Valve Mask Use Mouth 30 Chest Compressions

slide-24
SLIDE 24

Wearable Sensors

Apple Watch

  • Accelerometer
  • Gyroscope

MYO

  • Accelerometer
  • Gyroscope
  • EMG

24

slide-25
SLIDE 25

Accelerometer Data Example

Compressions Breath CPR

25

slide-26
SLIDE 26

Video: Open Pose

26

slide-27
SLIDE 27

Open Pose Challenges

27

slide-28
SLIDE 28

Envisioned System

28

Wearable Sensors OpenPose Activity

Recognition Automatic EMS Procedure Detection Architecture

Heat Map Generation Health Database Triage Score Generation

slide-29
SLIDE 29

Recap

29

  • Workload Assessment
  • Uses objective workload metrics to derive workload estimates for overall

workload and its contributing components

  • Analyzed algorithm across populations, human-robotic teaming paradigms,

and cross-tasks

  • Needs contextual information about the human’s current task
  • Automatic EMS Procedure Detection
  • Uses wearable and visual sensor data
  • Procedures are decomposed into sub-tasks for easier recognition
slide-30
SLIDE 30

Questions??

30