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

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

|Authors: Aniol Civit, Antonio Andriella, Alba Martínez, Joan Ars, Aida Ribera, Cristian Barrué, Guillem Alenyà

|Venue: arXiv preprint | Year: 2026

|arXiv: 2607.15156v1

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.

Key Contributions

• Frailty assessments are crucial to evaluate the risk of adverse events and the health and social …
• To address this, we present a robotic framework that guides older adults through standardised fra…
• The system uses a Behaviour Tree architecture that coordinates perception, decision-making, inter…
• Using vision-based skeleton tracking, the robot evaluates established clinical tests, including t…
• The framework was co-designed with healthcare professionals and evaluated in situ during six mont…

Topics

• vision
• reinforcement-learning
• human-robot-interaction
• benchmark

Code & Data

No code repository linked in paper metadata.

BibTeX

@article{Civit2026_260715156v1,
  title     = {Assessing Physical Frailty and Fall-Risk Indicators with Social Robots: An in situ Evaluation with Older Adults},
  author    = {Aniol Civit and Antonio Andriella and Alba Martínez and Joan Ars and Aida Ribera and Cristian Barrué and Guillem Alenyà},
  year      = {2026},
  eprint    = {2607.15156v1},
  archivePrefix = {arXiv},
  primaryClass  = {cs.RO},
  url       = {https://arxiv.org/abs/2607.15156v1}
}