Smartphone-Native Bidirectional Pain-Related Multimodal Large Language Models: A Unified Framework for Pain Sensing, Contextual Reasoning, Safe Sensory Feedback, and Adaptive Interaction
Abstract
Pain is a complex sensory and affective experience that is difficult to measure objectively and cannot be reduced to a single physiological signal. Smartphones and wearable devices have already been used to collect pain reports, monitor physiological correlates, and support pain assessment. Separately, artificial nociceptive systems and sensory-feedback interfaces have investigated how machines can detect potentially harmful stimuli and communicate information through physical feedback. However, the integration of pain-related sensing, multimodal large language model reasoning, and safe, bidirectional sensory interaction within a smartphone-centered architecture remains a research question requiring systematic investigation.
This paper proposes Smartphone-Native Bidirectional Pain-Related Multimodal Large Language Models (SBP-MLLMs), a conceptual framework for integrating pain-related input and controlled sensory feedback with multimodal reasoning. The architecture includes a smartphone host, a pain-related data acquisition interface, a multimodal representation encoder, a contextual reasoning controller, a safety-constrained feedback planner, and an output interface. The input pathway combines voluntary pain reports with available physiological, behavioral, and contextual information, while explicitly distinguishing measured signals from subjective pain experience. The output pathway communicates pain-related information through visual, auditory, vibrotactile, or other validated non-injurious feedback. Where clinically justified, specialized external devices may be considered under appropriate professional oversight, but the proposed framework does not require deliberately inducing pain. A closed-loop controller compares intended feedback with observed user responses and device status, while respecting consent and safety constraints. The paper formalizes the architecture, identifies research questions, and proposes an experimental evaluation protocol. The framework is conceptual and has not been experimentally validated. Its potential contribution is the integration of pain-related perception, multimodal reasoning, and safe bidirectional interaction rather than the invention of pain sensing, pain monitoring, or sensory stimulation.
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BibTeX
@misc{zhang2026smartphonenative,
title = {{Smartphone-Native Bidirectional Pain-Related Multimodal Large Language Models: A Unified Framework for Pain Sensing, Contextual Reasoning, Safe Sensory Feedback, and Adaptive Interaction}},
author = {Jincheng Zhang},
howpublished = {Zenodo},
year = {2026},
month = oct,
doi = {10.5281/zenodo.23494245},
url = {https://zenodo.org/records/23494245},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Smartphone-Native Bidirectional Pain-Related Multimodal Large Language Models: A Unified Framework for Pain Sensing, Contextual Reasoning, Safe Sensory Feedback, and Adaptive Interaction
AU - Jincheng Zhang
PY - 2026
DA - 2026/10/20
PB - Zenodo
DO - 10.5281/zenodo.23494245
UR - https://zenodo.org/records/23494245
AB - Pain is a complex sensory and affective experience that is difficult to measure objectively and cannot be reduced to a single physiological signal. Smartphones and wearable devices have already been used to collect pain reports, monitor physiological correlates, and support pain assessment. Separately, artificial nociceptive systems and sensory-feedback interfaces have investigated how machines can detect potentially harmful stimuli and communicate information through physical feedback. However, the integration of pain-related sensing, multimodal large language model reasoning, and safe, bidirectional sensory interaction within a smartphone-centered architecture remains a research question requiring systematic investigation. This paper proposes Smartphone-Native Bidirectional Pain-Related Multimodal Large Language Models (SBP-MLLMs), a conceptual framework for integrating pain-related input and controlled sensory feedback with multimodal reasoning. The architecture includes a smartphone host, a pain-related data acquisition interface, a multimodal representation encoder, a contextual reasoning controller, a safety-constrained feedback planner, and an output interface. The input pathway combines voluntary pain reports with available physiological, behavioral, and contextual information, while explicitly distinguishing measured signals from subjective pain experience. The output pathway communicates pain-related information through visual, auditory, vibrotactile, or other validated non-injurious feedback. Where clinically justified, specialized external devices may be considered under appropriate professional oversight, but the proposed framework does not require deliberately inducing pain. A closed-loop controller compares intended feedback with observed user responses and device status, while respecting consent and safety constraints. The paper formalizes the architecture, identifies research questions, and proposes an experimental evaluation protocol. The framework is conceptual and has not been experimentally validated. Its potential contribution is the integration of pain-related perception, multimodal reasoning, and safe bidirectional interaction rather than the invention of pain sensing, pain monitoring, or sensory stimulation.
ER -