# Real-time non-intrusive workload monitoring-Integration of human factors in surgery training and assessment

> **NIH NIH R21** · PURDUE UNIVERSITY · 2020 · $183,111

## Abstract

Project Summary/Abstract (30 lines)
 High physiological and cognitive workload required in de-coupled surgical work demands may have
significant impact on patient outcome, surgical efficacy, and surgical performance. As novel surgical
techniques, e.g., telesurgery, are developed, surgical operations will become more complex and the mental
and physical demand on surgeons will likely increase, making it critical to develop remote and connected
workload monitoring methods for the safe and effective surgical procedure design, testing, and training. This
work will implement novel technology and machine learning analytics to quantify real-time and remote
workload and test how workload feedback can impact care delivery in both in telesurgery and surgical
simulation environments. Our overall hypothesis is that connected sensing technology in telesurgical
procedures and simulation can improve surgical training and understanding of the impact of their workload on
performance; ultimately improving patient health, surgery efficacy, and patient access (e.g., tele-mentoring) to
surgical care. Two specific aims are proposed to investigate this hypothesis.
 The objective of Specific Aim 1 is to develop a connected sensor system to objectively quantify
workload real-time in simulated telerobotic procedures. This involves: 1) integrating non-intrusive sensors into
a single system within the simulation trainer or environment, 2) training machine learning techniques to
objectively distinguish workload using a simulated surgical skills tasks, and 3) validating metrics across varying
levels of cognitive loads under various task difficulty with medical trainees and expert participants.
 The objective of Specific Aim 2 is to determine the impact of the real-time workload feedback
intervention on trainee performance times, errors, and intraoperative workload. Two tasks are proposed: 1)
Explore modalities preferred by surgeons for providing real-time feedback on workload and 2) Assess impact
of workload feedback on task performance and learning. Our primary hypothesis is that performance times and
errors will improve when participants are provided realtime feedback on workload compared to performance
with no feedback.
 The expected deliverables include 1) workload monitoring technology, algorithms, and software for
complementing current simulation-based training, 2) objective and automated workload metrics, 3) real-time
assistive intervention tool, and 4) preliminary evidence on impact of workload monitoring on training. The
technology in this proposed work will improve public health by reducing adverse events due to human factors
in surgery and improve access to surgical care with intervention technology that can adaptively train surgeons
and remotely assess proficiency.

## Key facts

- **NIH application ID:** 9983030
- **Project number:** 5R21EB026177-02
- **Recipient organization:** PURDUE UNIVERSITY
- **Principal Investigator:** Denny Yu
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $183,111
- **Award type:** 5
- **Project period:** 2019-09-01 → 2023-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9983030

## Citation

> US National Institutes of Health, RePORTER application 9983030, Real-time non-intrusive workload monitoring-Integration of human factors in surgery training and assessment (5R21EB026177-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9983030. Licensed CC0.

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