Samsung Research America’s Digital Health Team has presented two AI foundation models designed to learn from wearable biosignals. The work centres on data captured by smartwatches, including heart activity, sleep, and physical activity.
The company discussed its Connected Care vision at the Health Forum during Galaxy Unpacked in July 2026. Samsung described a future of preventive, personalised, and connected care, supported by health technology and healthcare partnerships. Its research team positions health foundation models as one component of new consumer health experiences.
Sharanya Desai, Head of Digital Health Algorithms at Samsung Research America, said: “This research is significant because it lays the technical groundwork for delivering health insights that are efficient, precise, and continuous through a health foundation model.
“We will continue to develop and advance health foundation models that can be applied to a variety of biosignals and health features that can operate on-device with limited sensors and computing resources.”
Samsung’s health AI foundation model researchA health foundation model uses self-supervised learning to identify features in unlabeled biosignal data. Samsung says that pretraining on large health datasets allows one model to support downstream tasks such as biosignal analysis, biomarker development, and health issue prediction.
The research covers two models with different aims. xMAE, short for Physiology-Aware Masked Cross-Modal Reconstruction for Biosignal Representation Learning, learns temporal relationships between different biosignals. HiMAE, or Hierarchical Masked Autoencoder, learns health patterns across multiple time scales in wearable time-series data.
Samsung says xMAE was accepted to the International Conference on Machine Learning. HiMAE was accepted to the International Conference on Learning Representations. The company describes both as work on physiological relationships and temporal structures in biosignal data.
The models address different parts of wearable-data analysis. xMAE connects two cardiac signals that measure related activity through different mechanisms. HiMAE analyses data at short and long intervals, allowing one pretrained model to support classification, numerical prediction, and data generation.
xMAE links continuous PPG data to ECG signalsElectrocardiograms, or ECGs, measure the heart’s electrical activity directly. Samsung describes ECG as useful for measuring heart rate and heart-rate variability. It can also identify abnormal heart rhythms and risks associated with conditions such as atrial fibrillation.
Wearable ECG readings generally require a user to pause and take an active measurement. Photoplethysmography, or PPG, takes a different approach. PPG detects changes in blood flow and can run passively through sensors in wearable devices such as smartwatches.
Both signals originate from cardiac activity. They occur with a time difference, which Samsung compares with hearing thunder after seeing lightning. xMAE learns that temporal relationship by reconstructing masked parts of an ECG signal from PPG data.
This design aims to analyse cardiovascular-health features through continuously measured PPG data without separate manual ECG measurements. The model’s pretraining used about 9,400 hours of ECG and PPG data.
Subbu Venkatraman, Head of the Digital Health Research Lab at Samsung Research America, commented: “Biosignals are inherently dynamic, with unique time-varying physiological properties. The key contribution of this research lies in proving the viability of health foundation models capable of capturing both the inter-signal relationships and their underlying temporal structures.
“We remain committed to advancing foundational health AI research and translating it into healthcare solutions that meaningfully improve people’s health and wellbeing.”
Samsung reports that xMAE outperformed unimodal biosignal models and existing multimodal learning methods in 15 of 19 evaluation tasks. Those tasks covered cardiovascular disease prediction, abnormal test-result detection, and sleep-stage classification. The company also says the learned features showed potential for use across sensor devices, body locations, and data-gathering environments.
HiMAE analyses wearable data across time scalesWearable data can carry different information over different time periods. Short segments can show fast-changing signals such as heartbeats. Longer segments can reveal patterns that build over time, such as sleep or physical activity.
HiMAE uses multiple encoders to analyse short and long data segments separately. Samsung says this arrangement enables the model to identify the time scale needed for a health task. Heart-rate analysis and sleep prediction can therefore draw on different parts of the time-series data.
The training method reconstructs masked portions of wearable data. Samsung says this lets HiMAE learn patterns from biosignals where labelled data is limited. The model then supports classification, numerical prediction, and data generation from a single pretrained system.
Samsung says HiMAE achieved high performance with a smaller model than existing models. The company also reports that it can produce results in less than one millisecond on a smartwatch-class central processing unit.
That processing claim places the model’s analysis on the device rather than on cloud servers. Foundation models trained on unlabelled physiological streams provide a mechanism to extract diagnostic markers, run predictive health classifications, and generate user guidance from consumer hardware all without continuous server connectivity.
See also:Google AI health coach to use Abbott glucose data
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Abbott and Google are linking continuous glucose monitoring data with Google’s AI-powered health coaching tools, giving the Gemini-powered service access to another source of personal health information.
Under a multiyear agreement, data from Abbott’s Lingo continuous glucose monitor will be integrated into the Google Health app. Users will be able to view glucose trends alongside information related to activity, sleep, and other wellness metrics.
Google Health Coach, which is built with Gemini, will use Lingo data alongside other health information to provide recommendations covering nutrition, activity, sleep, and recovery. Access to the AI coach requires a Google Health Premium subscription.
Google says Health Coach is not intended for medical purposes and warns that its AI responses can be inaccurate or incomplete. The company advises users to verify its responses and consult healthcare professionals when appropriate.
Google had already been expanding the types of health information available to Health Coach. The company said in May that US users could sync medical records with the Google Health app, including laboratory results, vital signs, and medication information, and share those records with the coach to ask questions or receive summaries.
The Google Health app can also receive information from compatible wearables and third-party applications through Health Connect, Apple Health, and Google Health APIs.
The companies expect the Lingo integration to begin rolling out in the Google Health app later this year.
AI coaching meets continuous glucose dataLingo is an over-the-counter continuous glucose monitoring system designed for adults aged 18 and older who do not use insulin. The wearable tracks glucose levels throughout the day, while its accompanying app shows how factors including food, movement, and other daily activities correspond with changes in glucose.
According to FDA documents, Lingo continuously measures glucose in interstitial fluid rather than directly from blood. Its sensor is inserted under the skin on the back of the upper arm and uses an electrochemical process to measure glucose before transmitting the readings to the Lingo app through Bluetooth Low Energy.
The FDA says the Lingo app can display real-time glucose values, trend arrows, and glucose graphs. The sensor can be worn for up to 14 days, but the Lingo app does not provide glucose or system alerts.
The product is based on technology used in Abbott’s FreeStyle Libre platform. Abbott made Lingo available without a prescription in the US in 2024, and the system is also available in the UK.
Although Lingo shares underlying sensor technology with FreeStyle Libre 2, the products have different intended uses. FreeStyle Libre 2 is cleared for diabetes management, while the FDA describes Lingo as a system for helping adults who do not use insulin understand how glucose readings relate to nutrition, exercise, and daily activities.
FDA documentation also states that users should not take medical action based on Lingo readings without consulting a qualified healthcare professional.
Abbott and Google are also planning a large real-world study focused on metabolic health.
The study will combine continuous glucose readings with wearable, laboratory, and survey data to examine relationships between activity, sleep, well-being, and metabolic health. Abbott and Google said the findings will be used to refine Google Health Coach and inform future Lingo features.
Google connects more health data to GeminiThe Google Health app brings information from multiple sources into one place. Users can sync activity, fitness, sleep, vital-sign, and medical-record data and connect compatible applications and devices.
Google says users can decide what information they save, turn optional features on or off, export their data, and delete it. The company also states that Google Health data is not used for Google Ads.
Google Health Coach can use personal health records to personalise its responses, while the Google Health app can sync information from compatible apps and devices. The Abbott integration will add ongoing glucose data from Lingo.
Google has also been extending Gemini into other healthcare-related tasks. Zocdoc announced this week that US users can search for healthcare appointments and book providers directly through the Gemini app.
The Zocdoc connected app provides real-time appointment availability from a network of more than 200,000 providers across more than 200 specialities. Zocdoc said it is Gemini’s first connected-app partner for health appointments.
(Photo by Towfiqu barbhuiya)
See also: Novo Nordisk and AWS bring agentic AI into drug discovery
Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and is co-located with other leading technology events including the Cyber Security & Cloud Expo. Click here for more information.
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