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Human-Robot Teaming Field Deployments: A Comparison Between Verbal and Non-verbal Communication

Tauhid Tanjim, Promise Ekpo, Huajie Cao, Jonathan St. George, Kevin Ching, Hee Rin Lee, Angelique Taylor

TL;DR

This study evaluates how robotic crash carts influence workload and attitudes in high-stakes emergency care by comparing verbal (speech) vs non-verbal (LED) object-search guidance against a standard crash cart in a high-fidelity pediatric resuscitation simulation. Using a Wizard-of-Oz RCC, the authors conduct a between-subjects field study with 115 participants across interprofessional roles, measuring workload via NASA-TLX and attitudes via ACIR-Q, complemented by qualitative feedback. Key findings indicate verbal guidance reduces mental demand and effort compared to LED cues and the traditional cart, though some frustration persists, highlighting the nuanced trade-offs of communication modalities. The work informs RCC design to minimize disruption in emergency workflows and points to future enhancements in autonomy and closed-loop sensing to further reduce search time and support patient care.

Abstract

Healthcare workers (HCWs) encounter challenges in hospitals, such as retrieving medical supplies quickly from crash carts, which could potentially result in medical errors and delays in patient care. Robotic crash carts (RCCs) have shown promise in assisting healthcare teams during medical tasks through guided object searches and task reminders. Limited exploration has been done to determine what communication modalities are most effective and least disruptive to patient care in real-world settings. To address this gap, we conducted a between-subjects experiment comparing the RCC's verbal and non-verbal communication of object search with a standard crash cart in resuscitation scenarios to understand the impact of robot communication on workload and attitudes toward using robots in the workplace. Our findings indicate that verbal communication significantly reduced mental demand and effort compared to visual cues and with a traditional crash cart. Although frustration levels were slightly higher during collaborations with the robot compared to a traditional cart, these research insights provide valuable implications for human-robot teamwork in high-stakes environments.

Human-Robot Teaming Field Deployments: A Comparison Between Verbal and Non-verbal Communication

TL;DR

This study evaluates how robotic crash carts influence workload and attitudes in high-stakes emergency care by comparing verbal (speech) vs non-verbal (LED) object-search guidance against a standard crash cart in a high-fidelity pediatric resuscitation simulation. Using a Wizard-of-Oz RCC, the authors conduct a between-subjects field study with 115 participants across interprofessional roles, measuring workload via NASA-TLX and attitudes via ACIR-Q, complemented by qualitative feedback. Key findings indicate verbal guidance reduces mental demand and effort compared to LED cues and the traditional cart, though some frustration persists, highlighting the nuanced trade-offs of communication modalities. The work informs RCC design to minimize disruption in emergency workflows and points to future enhancements in autonomy and closed-loop sensing to further reduce search time and support patient care.

Abstract

Healthcare workers (HCWs) encounter challenges in hospitals, such as retrieving medical supplies quickly from crash carts, which could potentially result in medical errors and delays in patient care. Robotic crash carts (RCCs) have shown promise in assisting healthcare teams during medical tasks through guided object searches and task reminders. Limited exploration has been done to determine what communication modalities are most effective and least disruptive to patient care in real-world settings. To address this gap, we conducted a between-subjects experiment comparing the RCC's verbal and non-verbal communication of object search with a standard crash cart in resuscitation scenarios to understand the impact of robot communication on workload and attitudes toward using robots in the workplace. Our findings indicate that verbal communication significantly reduced mental demand and effort compared to visual cues and with a traditional crash cart. Although frustration levels were slightly higher during collaborations with the robot compared to a traditional cart, these research insights provide valuable implications for human-robot teamwork in high-stakes environments.

Paper Structure

This paper contains 15 sections, 5 figures.

Table of Contents

  1. INTRODUCTION
  2. RELATED WORK
  3. Human-Robot Teaming in High-Stakes Environments
  4. Multimodal Interaction in HRI
  5. HRI Field Studies
  6. Crash Cart Robot Field Deployments
  7. Robotic Crash Cart
  8. Study Design
  9. Participants
  10. The Emergency Simulation Room Setup
  11. Study Task
  12. Wizard-of-Oz Robot Control
  13. Data Collection & Analysis
  14. Results
  15. Discussion

Figures (5)

  • Figure 1: Study session with participants engaging with a crash cart robot.
  • Figure 2: The robotic crash cart is a Wizard-of-Oz platform, controlled by a Raspberry Pi connected to a tablet with a user interface that helps teams search for supplies using drawer lights and speech.
  • Figure 3: System diagram of the robotic crash cart illustrating the portable touch screen User Interface, processing via Raspberry Pi 4B using ROS, and outputs through light strips and audio signals.
  • Figure 4: Participants’ perceived NASA-TLX scores.
  • Figure 5: Participants’ mean ACIR-Q scores.