SeniorBot: A Human-Supervised Multiscale Gerontechnology Framework for Longitudinal Health-Trajectory Monitoring and Adaptive Robotic Assistance

Background: Indonesia is undergoing rapid population ageing. In 2025, approximately 34.7 million Indonesians were aged 60 years or older, representing 12.33% of the population, with the proportion projected to reach 20.31% by 2045 [1]. Population ageing increases the need for care models that preserve functional ability, autonomy, social connection, and the capacity to remain safely in the community. Wearable sensing, patient-facing conversational AI, and care robotics are developing rapidly, but the supporting evidence remains distributed across modality-specific literatures with uneven clinical maturity rather than one validated integrated care architecture [5,14,25]. Objective: This paper proposes SeniorBot, a human-supervised multiscale gerontechnology framework integrating periodic molecular context, dense longitudinal sensing, contextual AI interpretation, assistive interaction, tiered escalation, and a future morphofunctional robotic embodiment to support ageing in place. Methods: We performed an evidence-informed conceptual synthesis of the original SeniorBot proposal and targeted literature spanning healthy ageing, wearable and molecular biosensing, digital biomarkers, longitudinal anomaly detection, conversational and socially assistive AI, human oversight, privacy and age-inclusive design, and reconfigurable robotics. Evidence was used to establish component plausibility, maturity, and boundary conditions rather than to estimate pooled effectiveness. The framework was constructed using explicit distinctions between near-term capability, emerging capability, and future embodiment hypotheses. Results: SeniorBot conceptualizes health as a trajectory rather than a collection of isolated measurements. Sparse molecular information, including selected genomic, epigenomic, proteomic, and metabolomic data, may provide slow-changing biological context. Wearable physiological signals, gait, mobility, sleep, voice, behavioral interaction, and environmental exposures provide denser longitudinal information. Before escalation, the framework combines data-quality assessment, population safety rules, individual longitudinal baselines, multimodal concordance, context, and uncertainty. AI-generated signals are not medical diagnoses and do not independently prescribe or modify treatment. SeniorBot defines a future pathway toward a reconfigurable mobility-support configuration. This does not assume that a human-bearing transformable robot is currently clinically available. Instead, it is grounded in demonstrated engineering principles such as appendage repurposing, modular reconfiguration, continuous 3D shape morphing, geometric locking, and deformable load-bearing robotic structures [17–19]. The conceptual synthesis produced a closed-loop architecture in which molecular context and dense longitudinal sensing pass through data-quality and contextual gates before contributing to a personal health-trajectory model, graded response orchestration, and meaningful human oversight. Proposed longitudinal domains include cardiovascular and respiratory signals, mobility and gait, falls, sleep and circadian behavior, voice and interaction, cognition-related microphenotypes, social behavior, and environmental exposure. The maturity synthesis was asymmetric: wearable sensing and conversational assistance have direct feasibility evidence [5,14,25], longitudinal multimodal state modeling remains translational [26–29], and continuous physical autonomy or human-bearing morphofunctional reconfiguration remains frontier engineering requiring a separate validation track [25,30–32]. Conclusions: SeniorBot is proposed not as a finished robot but as a testable convergence architecture linking molecular context, longitudinal digital phenotypes, contextual AI, meaningful human oversight, and adaptive physical assistance. Whether such integration improves safety, independence, caregiver experience, or healthcare utilization remains an empirical question.