Overview
Earn your certificate in Introduction to Wearable Technology in Digital Health. This self-paced online program introduces how wearable technologies collect physiological and behavioral data, how those signals can be transformed into meaningful digital biomarkers, and how artificial intelligence is helping turn wearable data into personalized digital health insights.
Millions of people now use smartwatches, fitness trackers, and other wearable devices to monitor aspects of their health such as physical activity, sleep, heart rate, and blood oxygen. The real potential of wearable technology, however, goes beyond simply collecting measurements. This course explores how continuous physiological signals can be interpreted, analyzed, and transformed into meaningful information about health and well-being.
Learners begin by examining the ethical and privacy considerations associated with wearable health data, including responsible use, protection of personal information, and key ethical principles. They then explore how modern wearable technologies work, including sensors such as photoplethysmography (PPG), electrocardiography (ECG), accelerometers, and blood oxygen sensors, while considering both the strengths and limitations of real-world wearable data.
The course then moves from measurement to interpretation. Learners explore how signals such as heart rate, heart rate variability, blood oxygen, respiration, activity, and sleep can be processed and transformed into digital biomarkers related to areas such as sleep quality, stress, fitness, and recovery. Learners can follow either a coding-based or AI-guided no-code pathway, making the course accessible to participants with different technical backgrounds and learning preferences.
A distinctive feature of the program is the opportunity to work with wearable data in a personalized setting. Learners can connect their wearable data to Stanford Data Ocean and explore their own physiological patterns, discover personalized insights, and apply concepts from the course to real-world data.
The final portion of the course focuses on physiological state changes, examining how patterns in cardiovascular signals, sleep, stress, recovery, and other health metrics can provide insight into changes over time. Learners explore how wearable data can support a better understanding of individual baselines, longitudinal health patterns, and the relationship between physiology, behavior, and well-being.
By the end of the course, learners will understand how wearable technologies collect health data, how to interpret that data responsibly, how physiological signals can become meaningful digital biomarkers, and how artificial intelligence is shaping the future of personalized digital health.
Modules Included
- Ethics & Privacy
Explore the responsible use of wearable health data, protection of personal health information, privacy considerations, and ethical principles for collecting, analyzing, and interpreting continuously generated physiological data. - Introduction to Wearables
Learn how modern wearable technologies work and what different sensors measure, including PPG, ECG, accelerometers, and blood oxygen sensors. Examine the strengths of wearable data, including continuous and real-world monitoring and personalized longitudinal baselines, as well as limitations such as motion artifacts, battery and wear-time constraints, missing data, and device variability. - Digital Biomarkers
Explore how raw physiological signals can be transformed into meaningful digital biomarkers. Learn about signal preprocessing, feature extraction, and modeling using measurements related to heart rate, heart rate variability, blood oxygen, respiration, activity, sleep, and other physiological characteristics. Apply these concepts through either a coding-based or AI-guided no-code pathway to investigate measures such as sleep quality, stress, fitness, and recovery. - Physiological State Changes
Examine how wearable signals can reveal changes in physiological state over time. Explore patterns in cardiovascular rhythms, heart rate, heart rate variability, ECG, sleep architecture, stress, recovery, and other health metrics, and investigate how longitudinal wearable data may provide insights into behavioral and health patterns.
Personalized Wearable Data Experience
Turn your own wearable data into personalized, AI-assisted digital health learning.
Learners can connect wearable data to Stanford Data Ocean, select a period of data for analysis, and apply concepts from the course directly to their own physiological information. Through this personalized experience, learners can explore individual patterns, identify trends, and generate AI-assisted insights while applying responsible approaches to interpreting personal health data.
From wearable sensors to digital biomarkers to personalized insights, the program provides a practical foundation for understanding how wearable technology and artificial intelligence are shaping the future of digital health.
