Assessing Physical Frailty and Fall-Risk Indicators with Social Robots: An in situ Evaluation with Older Adults

2026-07-16

AI Summary

This study addresses the limitations of traditional frailty assessments, which rely on coarse-grained clinical indicators, overlook biomechanical signatures of functional decline, and incur high costs. The authors propose an automated frailty evaluation system leveraging a social robot integrated with a behavior tree architecture to coordinate perception, decision-making, and interaction modules. While guiding older adults through standardized clinical tests such as the Short Physical Performance Battery (SPPB) and Timed Up-and-Go (TUG), the system simultaneously captures multimodal mobility metrics—including vision-based skeletal tracking, inertial measurement unit (IMU) data, and gait analysis. This work represents the first integration of social robotics and behavior trees for frailty assessment, enabling efficient and objective quantification of functional status in real-world clinical settings. Experimental results demonstrate strong agreement between robot-derived task durations and gait parameters with clinical gold standards (ICC > 0.9), and substantial concordance in SPPB total scores compared to therapist ratings (κ = 0.67), confirming the system’s reliability and clinical applicability.

Abstract

Frailty assessments are crucial to evaluate the risk of adverse events and the health and social care needs of older adults, yet their administration remains resource-intensive and typically relies on coarse clinical outcomes, such as task completion times, which may overlook biomechanical indicators of functional decline. To address this, we present a robotic framework that guides older adults through standardised frailty and fall-risk tests while capturing clinical scores and additional frailty-related metrics, offering a deeper insight into a user's condition. The system uses a Behaviour Tree architecture that coordinates perception, decision-making, interaction, and measurement modules. Using vision-based skeleton tracking, the robot evaluates established clinical tests, including the Short Physical Performance Battery (SPPB) and the Timed Up and Go (TUG). The framework was co-designed with healthcare professionals and evaluated in situ during six months in a rehabilitation centre's research lab with N=81 older adults. Robot-derived measurements were compared against therapist assessments and clinical reference instruments, including a gait analysis walkway and an inertial measurement unit (IMU). Results showed excellent agreement for most test completion times and gait-related parameters ($ICC > 0.9$). And, substantial agreement for the overall SPPB score comparing the robot and the therapist ($k = 0.67$) and moderate agreement comparing the robot and the IMU ($k=0.55$). The findings highlight that social robots can provide reliable and objective frailty assessments in healthcare settings while enabling the collection of relevant mobility indicators beyond conventional outcomes.

Problem

• Physical Frailty
• Fall-Risk Assessment
• Older Adults
• Biomechanical Indicators
• Clinical Evaluation

Innovation

• social robot
• frailty assessment
• skeleton tracking
• Behaviour Tree
• fall-risk evaluation