Navigating human-robot interaction challenges in private and residential aged care homes

AI summary

Overview

Research area: Human–robot interaction (HRI) and ambient intelligence for older adults, focused on residential aged care (RAC) homes and private homes.

Technical level: Intermediate — the chapter assumes familiarity with robotics, sensing modalities, and AI deployment concepts, but explains them in prose rather than equations.

Scope (one sentence): A technical book chapter that reviews robotic platforms, perception and sensing technologies, interaction design, privacy safeguards, and real-world evaluation approaches for assistive robots and ambient sensor systems deployed in aged care facilities and private homes.

What This Paper Is About

The chapter argues that RAC homes and private homes are "extreme" HRI environments — cluttered and constantly changing layouts, variable lighting and noise, mobility aids in the way, residents with diverse cognitive and sensory needs, and very little on-site technical support. It surveys the robots and ambient sensor systems meant to help with daily living, safety monitoring, and social engagement, and asks what design, privacy, and evaluation practices are needed for these systems to actually work and be trusted in real care settings.

Key Contributions

1. A structured design space. The chapter organises the problem into recurring themes: perception in lived-in spaces, interaction modalities tailored to diverse users, and deployment patterns that scale in practice — presented as a concept map linking background constraints, interaction, perception, privacy, evaluation, and open challenges.

2. Translation of privacy and trust requirements into concrete engineering choices. Ethical and regulatory expectations are mapped to actionable design decisions such as on-device inference, minimal data flows, visible consent and mute controls, and transparent data handling aligned with emerging AI and privacy regulation.

3. A practical evaluation agenda for real-world settings. The chapter proposes metrics that go beyond model accuracy — false alarms per day, time to recover from faults, staff workload, and long-term system reliability — and calls for in-the-wild and longitudinal protocols.

4. A taxonomy and landscape map. Assistive technologies are grouped into five categories (socially-assistive robots, assistive robots, teleoperated robots, service robots, and ambient intelligence systems), complemented by a sensor taxonomy across four system types (assistive/social robots, telepresence robots, domestic service robots, and ambient intelligence).

Main Findings

• Both settings are "extreme" HRI environments, but for different reasons. Private homes are highly heterogeneous in layout, lighting, furnishings, and routines, with minimal technical support; RAC homes are more standardised but add shared spaces, rotating staff, infection-control procedures, and multiple residents whose needs must be balanced.

• Robots rarely operate alone. The chapter documents hybrid deployments where mobile robots complement fixed and wearable sensors — for example, combining fall detection from ambient sensors with verbal prompts or physical assistance from a robot — coordinated through a local edge hub that enforces privacy policy while summaries flow to clinical systems.

• Speech recognition still underperforms for older adults. Despite progress from large multilingual models such as Whisper and SeamlessM4T v2, benchmarks show ASR systems continue to struggle with older adult speech in noisy home settings, especially when speech is monotone or hesitant. The recommended mitigations include confirmation steps for safety-critical actions, short unambiguous utterances, press-to-talk modes, and fallbacks such as a tablet.

• Interaction should favour small, consistent cue sets. Non-verbal interaction works best with a small set of gestures (point, wave, open-hand stop), slow exaggerated motion, and redundant channels such as expressive lighting bands and large on-screen icons that remain legible under glare, clutter, and low light.

• Reliability and customisability beat feature breadth. In a three-month in-situ study, the social robot Pepper was used regularly, around 1 h/day, mostly in one-to-one sessions; staff success depended on how easily they could tailor activities, with reliability and time pressure acting as barriers. Follow-up work with a lightweight mobile configuration app increased appropriation and reduced reliance on technical staff.

• Companion robots work as programmes, not as devices. Meta-analytic evidence suggests the robotic seal PARO can reduce agitation and improve mood in dementia care when sessions are structured and integrated into activity schedules rather than left as ad-hoc interactions.

• Anthropomorphism is double-edged. Studies indicate anthropomorphic cues can both increase trust and trigger discomfort depending on context and user expectations, so systems should be proactive but bounded, announce intentions, offer opt-outs, and hand over to human channels when confidence is low.

• Four deployment principles recur in field work: schedule interactions rather than relying on ad hoc use; give caregivers fast controls for setup and content customisation; enable fallback mechanisms when speech fails; and instrument operations (docking success, false speech activations, alert rates) for iterative improvement.

Methodology in Plain English

This is a review and synthesis chapter, not an experimental study. The authors surveyed prior literature, field deployments, benchmarks, and regulatory guidance, then organised the material into a chapter structure: background and constraints, interaction modalities and integration, perception and ambient sensing technologies, privacy and trust, benchmarking and evaluation, and open challenges. They supplemented the review with illustrative figures — example care environments, an interaction scenario, a system architecture diagram, a sensor taxonomy, and examples of robot form factors — and with comparison tables contrasting RAC homes and private homes along dimensions such as supervision, governance, environmental variability, technical support, and mobility-aid prevalence. They also propose practical frameworks of their own, including error-recovery ladders for interaction flows (a fallback to a screen prompt, then a retry step before escalation to human support) and a task, risk, and burden lens for making stakeholder trade-offs explicit.

Why This Matters

Impact on research. The chapter reframes aged-care HRI as a deployment problem rather than a lab-accuracy problem, and pushes the field toward metrics that reflect what care providers actually care about: alarm burden, recovery time, workload, and long-term engagement. It also connects technical design choices directly to emerging regulation such as the EU AI Act, ISO and IEC 23894:2023 for AI risk management, and the Australian Privacy Principles.

Real-world applications:

• Safety monitoring in private homes and care facilities, combining ambient sensors with robot-delivered prompts or assistance.
• Structured therapeutic and social programmes using companion robots in dementia care, with defined dose, frequency, and facilitator training.
• Telepresence for family connection and remote clinical touchpoints, paired with scheduled sessions and a short menu of common tasks.
• Medication reminders, adherence summaries, and fall alerts routed into existing electronic health record systems through standard interfaces built on FHIR.

Industry relevance. Vendors and facility managers get a concrete checklist: keep the integration surface small (a handful of well-defined events with severity levels and human-readable rationales), design for graceful degradation when connectivity drops, make sensing states visible, and expect to compete on reliability, maintainability, and regulatory readiness rather than on feature lists.

Future Directions

• Robust multimodal perception in lived-in spaces. Open challenges include multi-modal fusion that holds up under partial views, occlusion, clutter, and variable lighting, plus safe navigation and physical assistance in confined spaces.
• Privacy-preserving personalisation. Personalisation must be transparent and reversible, and the chapter flags privacy-aware personalisation as an unsolved research direction — how to adapt approach distance, prompt timing, and content without expanding data exposure.
• Evaluation standards and benchmarking ladders. The field lacks shared benchmarks, simulators, and longitudinal in-the-wild protocols for safety events, usability, alarm burden, and long-term reliability.
• Reference architectures, deployment playbooks, and operational sustainability. The chapter calls for hybrid robot-plus-ambient reference architectures, operational tooling, maintainability measures, and pathways to regulation-ready evidence, alongside attention to equity, accessibility, and cultural competence.

Target Audience

Robotics and HRI researchers moving from lab prototypes toward care deployments; engineers designing perception, sensing, and interaction stacks for older users; health-informatics and clinical-informatics teams integrating robots and sensors with EHR systems; aged-care providers, facility managers, and vendors evaluating or procuring assistive robotics; and policy or regulatory readers who want to see how privacy, safety, and AI-governance requirements translate into concrete system design choices.

Authors’ abstract

This chapter explores how robotic systems and ambient intelligence can enhance care for older adults in residential aged care (RAC) homes and private homes. Although these settings differ, we frame both as extreme and challenging environments for human-robot interaction (HRI), characterised by cluttered and dynamic layouts, diverse resident needs, and limited on-site technical support. We review embodied and non-embodied systems that assist with daily living, safety monitoring, and social engagement, highlighting hybrid deployments where robots complement fixed and wearable sensors. To ensure these technologies are usable and effective, design considerations consider the cognitive, physical, and social needs of older adults. Interaction design prioritises accessibility for older adults with low digital literacy, using robust but simple voice interfaces, small sets of non-verbal cues, accessible screen-based companions, and safe object handovers. Privacy, trust, and acceptability are addressed via practical strategies such as on-device inference, data minimisation, visible consent controls, and transparent data handling aligned with emerging artificial intelligence (AI) and privacy regulation. Evaluation emphasises real-world metrics including false alarms per day, time to recover from faults, staff workload, and long-term system reliability. We close by outlining open research challenges and future directions for scalable, regulation-ready deployments that support aging with dignity, safety, and independence.