Abstract: A personal electronic device and wearable sensing system includes one or more dermal-contact electrochemical sensors integrated into one or more skin-contact surfaces of the electronic device, wearable, accessory, or interactive electronic system. The electrochemical sensors obtain measurements indicative of one or more analyte classes associated with a user. One or more additional sensors obtain non-electrochemical measurements associated with the user. A sensor-fusion module derives electrochemical and non-electrochemical features and generates a unified state estimate. One or more inference modules determine a user-state output based on the unified state estimate and a comparison of electrochemical-derived information, or features derived therefrom, to an individualized baseline reference profile associated with the user. In some embodiments, the analyte classes include endogenous physiological markers including glucose-related markers, exogenous exposure analytes, or both.

Abstract: Systems, methods, and apparatus are disclosed for omnimodal sensing, data fusion, and autonomous decision-making across physical and digital domains and further integrates a Multimodal Diagnostic System (MDS) and Impairment Recognition and Intervention System (IRIS) with defense architecture or a system architecture that can be compliant with the Modular Open Systems Approach (MOSA) and Sensor Open Systems Architecture (SOSA) to ensure interoperability. The system can utilize real-time multisensory fusion, cryptographic provenance via blockchain, and resilient magnetoelectric communication to support mission-critical decision-making across manned and unmanned platforms in denied or contested environments.

Abstract: A system or method of impairment recognition and intervention includes collecting sensor data using a vehicle sensor array, combining the sensor data using a sensor fusion module for initial data aggregation and synchronization from data collected from the vehicle sensor array to provide fused data and for combining processed data from all sensors into a unified state estimate, performing real-time analysis in detection of signs of impairment using a machine learning model or models that received the fused data as inputs, performing advanced data analysis, long term storage, and system management in communication with the machine learning model or models using a cloud processing and data storage component serving as a centralized platform, encrypting all stored data and encrypting communication with the cloud processing and data storage component using an encryption engine, and providing a tamper-evident log of critical events in detection of signs of impairment using blockchain technology.

Abstract: Systems, methods, and apparatus are disclosed for omnimodal sensing, data fusion, and autonomous decision-making across physical and digital domains. The disclosed architecture integrates biological-analog modalities including visual, auditory, olfactory, gustatory, and tactile sensors with symbolic or linguistic data sources such as text, structured records, or unstructured digital inputs. A data-fusion engine consolidates these heterogeneous streams into a unified context representation. A machine-learning inference model performs diagnostic, predictive, or safety-related reasoning from this representation. A distributed-ledger framework validates and preserves data provenance, and a resilient communication subsystem ensures command, control, and safety continuity through alternative channels such as cellular text, low-frequency radio, optical, acoustic, or magnetoelectric field-based transmission.

Abstract: A system and method of combining different sensor data for higher reliable diagnosis information on a portable mobile device includes using a camera for imaging a region of interest of a subject to obtain an image signal, a microphone for capturing acoustic information from the subject, and one or more processors. The one or more processors can be configured to spectrally analyze the image signal, estimate a first vital-sign of the subject corresponding to a diagnosis using the image signal, analyze the acoustic information, estimate a second vital-sign of the subject corresponding to the diagnosis using the acoustic information, and combine the first vital sign with the second vital-sign to provide a higher confidence level diagnostic of the diagnosis.