Conditional Acceptance of Smart Care Devices for Elderly Care

Emotion-recognition-enabled smart care devices are increasingly proposed for home-based rehabilitation and elderly care. However, evidence on prospective user and family caregiver acceptance remains limited.^1,2,3 A cross-sectional online survey investigated public attitudes, concerns, interaction preferences, and design expectations regarding such devices.¹ The study included 506 adults in China, with respondents answering from either a prospective individual-user perspective (n=339) or a family caregiver perspective (n=167).¹

Study Design and Findings

The survey employed descriptive statistics, Top-2 Box summaries, and visual comparisons to analyze acceptance, scenario priorities, sensing preferences, data-sharing attitudes, and perceived device roles.¹ Overall, respondents showed favorable but conditional acceptance of these devices.¹ Privacy transparency, reliability, and recognition accuracy emerged as key adoption conditions.¹ Concerns about emotional data misuse and machine accuracy remained salient among participants.¹

Participants prioritized home-based rehabilitation and health management scenarios.¹ They preferred wearable physiological sensing over camera-based monitoring and favored human-in-the-loop interaction over fully automated responses.¹ Family caregivers showed descriptively stronger endorsement than prospective individual users on several selected items, although the overall pattern was broadly consistent across groups.¹ This consistency should not be interpreted as evidence of statistically significant subgroup differences.¹

The findings suggest that emotion-recognition-enabled smart care devices may be more acceptable when designed around four principles: safety first, human-in-the-loop control, low-intrusion sensing, and bounded empathy.¹

Limitations

The sample consisted mainly of online adult respondents rather than actual older adult users.¹ Therefore, the findings should be interpreted as exploratory evidence on prospective acceptance rather than as direct evidence of real-world adoption.¹

The study's reliance on self-reported data from an online survey also introduces potential biases, such as social desirability bias, where respondents may report socially acceptable attitudes rather than their true feelings. Furthermore, the cross-sectional design limits the ability to infer causality or track changes in acceptance over time. Longitudinal studies would be beneficial to understand how attitudes evolve with increased exposure to and experience with smart care devices.

Clinical Implications and Future Directions

The conditional acceptance observed in this study provides crucial insights for the development and implementation of emotion-recognition-enabled smart care devices in clinical and home care settings. The emphasis on "safety first" underscores the need for robust cybersecurity measures and clear ethical guidelines to prevent emotional data misuse. Healthcare providers and device manufacturers must prioritize transparent communication regarding data collection, storage, and sharing protocols to build trust among users and caregivers.

The preference for "human-in-the-loop control" highlights the importance of designing devices that augment, rather than replace, human interaction and decision-making. Clinicians should be trained to integrate these devices into existing care pathways, leveraging their capabilities to provide timely alerts or support, while maintaining their professional oversight. This approach ensures that the technology serves as a tool to enhance care quality and efficiency, rather than creating a sense of detachment or over-reliance on automated systems.

The finding that "low-intrusion sensing" is preferred, particularly wearable physiological sensing over camera-based monitoring, has significant implications for device design. Future iterations should explore and prioritize less intrusive methods of data collection that respect user privacy and comfort. This could include advancements in passive sensing technologies or multimodal approaches that combine various low-intrusion sensors to achieve accurate emotion recognition without compromising personal space.

Finally, the concept of "bounded empathy" suggests that while these devices can offer emotional support, their role should be clearly defined and not over-promised. Clinicians should educate patients and caregivers about the capabilities and limitations of these devices, managing expectations to prevent disillusionment. The technology should complement human empathy and care, providing objective data and timely interventions, rather than attempting to replicate the complex nuances of human emotional connection.

Future research should expand beyond prospective acceptance to include pilot studies with actual older adult users and their caregivers in real-world settings. This would provide valuable ecological validity and identify practical challenges and facilitators to adoption. Furthermore, comparative studies across different cultural contexts would be beneficial, given the potential variations in attitudes towards technology, privacy, and elderly care. Investigating the impact of different levels of technological literacy and prior experience with smart devices on acceptance would also offer a more nuanced understanding of user perspectives. Ultimately, the successful integration of emotion-recognition-enabled smart care devices hinges on a user-centric design approach that prioritizes ethical considerations, transparency, and the enhancement of human-centered care.