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care. By linking biochemical indicators with physical signals, hybrid systems can inform closed-loop or
semi–closed-loop therapeutic strategies. This is particularly important for older adults, who exhibit altered
pharmacokinetics, increased sensitivity to dosing, and multimorbidity that complicates standardized
treatment. Hybrid sensing thus offers a pathway toward optimal tailored therapy that balances efficacy and
safety.

Translational success depends on long-term wearability, usability, mitigation of biofouling, signal
instability , and data security. Modular architectures that allow periodic replacement of chemical
[43]
components while maintaining long-term physical monitoring address mismatched sensor lifetimes . To
[44]
mitigate signal overlap and data confounding in multimodal monitoring, hybrid systems should adopt
multi-level data fusion strategies, enabling computational separation of overlapping physiological
signatures . Protecting sensitive biometric information will require secure transmission, edge processing,
[45]
and privacy-aware frameworks . Lastly, cost-effectiveness will be a critical determinant for the large-scale
[46]
adoption of hybrid wearable systems across diverse healthcare settings.

As healthcare shift toward home-centered and preventive models, wearable hybrid sensors are poised to
function as a central biological interface between older adults and digital health ecosystems. Integrated with
telemedicine platforms and smart home systems, they provide body-level physiological and biochemical
ground truth that complements ambient sensing and reduces diagnostic ambiguity. Recent precision health
models have illustrated how continuous wearable data, when integrated with telemedicine and smart-home
infrastructure, can support individualized risk assessment and proactive care beyond conventional
clinic-centered management .
[47]

Ultimately, the promise of wearable hybrid sensors in geriatric healthcare lies in seamless integration into
daily life. Human-centered design emphasizing unobtrusiveness, intuitive interfaces, minimal maintenance,
and energy-efficient operation will be essential for sustained engagement. When coupled with scalable
manufacturing and advanced analytics [e.g., AI/machine learning (ML)], hybrid wearable systems offer a
foundation for transforming geriatric care from reactive management toward continuous, predictive, and
personalized health support. Finally, this work highlights both technological opportunities and the need for
large-scale longitudinal validation in multimorbid elderly populations to translate hybrid sensing into
clinically meaningful solutions.

DECLARATIONS
Acknowledgments
The authors acknowledge helpful discussions within the Aiiso Yufeng Li Family Department of Chemical
and Nano Engineering at UC San Diego. This work was supported by the UCSD Center for Wearable
Sensors.

Authors’ contributions
Conceptualization: Kim, B.; Ding, S.
Writing - original draft: Kim, B.
Writing - review and editing: Kim, B.; Ding, S.; Wang, J.
Supervision: Ding, S.; Wang, J.

Availability of data and materials
Not applicable.

AI and AI-assisted tools statement
During the preparation of this manuscript, the AI tool ChatGPT (version 5.1, released 2026-11-12) was used
for language and graphic editing. The tool did not influence the study design, data collection, analysis,