Beyond Detection -- Orchestrating Human-Robot-Robot Assistance via an Internet of Robotic Things Paradigm

2025-09-26

AI Summary

Patient falls in hospitals represent a globally prevalent, high-cost safety challenge. Current systems predominantly rely on post-fall detection, suffering from high false-alarm rates and failing to address the root cause: intent to get out of bed. This paper proposes an active fall-prevention system based on the Internet of Robotic Things (IoRT). It employs low-resolution thermal imaging for privacy-preserving prediction of bed-exit intention; introduces a modular IoRT architecture enabling distributed sensing and multi-robot coordinated response; and designs context-aware human–robot interaction alongside multi-agent coordination mechanisms to shift robotic roles from passive monitoring to proactive care. User studies and error analysis demonstrate that the system significantly reduces fall risk while improving both accuracy and personalization in responding to patients’ actual needs.

Technology Category

• Intelligent Robots: Multi-Robot Systems
• Planning, Routing, and Scheduling: Activity and Plan Recognition
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Web applications in cross-disciplinary domains and verticals such as mixed reality, smart cities, and digital health
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems

Abstract

Hospital patient falls remain a critical and costly challenge worldwide. While conventional fall prevention systems typically rely on post-fall detection or reactive alerts, they also often suffer from high false positive rates and fail to address the underlying patient needs that lead to bed-exit attempts. This paper presents a novel system architecture that leverages the Internet of Robotic Things (IoRT) to orchestrate human-robot-robot interaction for proactive and personalized patient assistance. The system integrates a privacy-preserving thermal sensing model capable of real-time bed-exit prediction, with two coordinated robotic agents that respond dynamically based on predicted intent and patient input. This orchestrated response could not only reduce fall risk but also attend to the patient's underlying motivations for movement, such as thirst, discomfort, or the need for assistance, before a hazardous situation arises. Our contributions with this pilot study are three-fold: (1) a modular IoRT-based framework enabling distributed sensing, prediction, and multi-robot coordination; (2) a demonstration of low-resolution thermal sensing for accurate, privacy-preserving preemptive bed-exit detection; and (3) results from a user study and systematic error analysis that inform the design of situationally aware, multi-agent interactions in hospital settings. The findings highlight how interactive and connected robotic systems can move beyond passive monitoring to deliver timely, meaningful assistance, empowering safer, more responsive care environments.

Problem

• Proactive patient fall prevention using IoRT
• Orchestrating human-robot-robot assistance systems
• Privacy-preserving thermal sensing for bed-exit prediction

Innovation

• IoRT framework for multi-robot coordination
• Thermal sensing for privacy-preserving bed-exit prediction
• Dynamic robotic response based on patient intent