Actigraphy is the use of a wrist- or body-worn sensor, usually one built around an accelerometer, to estimate when a person is asleep, awake, resting, or active over time. In sleep medicine and sleep research, actigraphy is often used to measure sleep timing, sleep duration, fragmentation, and day-to-night activity patterns in real-world settings over many days or weeks.
Why It Matters
Actigraphy matters because it is far easier to deploy at scale than in-lab polysomnography. That makes it useful for understanding habitual sleep schedules, circadian regularity, insomnia patterns, recovery, aging, and longitudinal changes that would be difficult to capture from a single night in a sleep lab.
It is especially useful when the question is about trends rather than microscopic sleep physiology. If a care team wants to know whether sleep timing is drifting, fragmentation is increasing, or routines are stabilizing during treatment, actigraphy can be a practical signal source.
Where AI Fits
AI helps actigraphy by improving sleep-wake classification, combining motion with heart rate or other signals, and detecting changes from a person's own baseline across long sequences of nights. This is why actigraphy often overlaps with digital biomarkers, remote patient monitoring, time series forecasting, and anomaly detection.
At the same time, actigraphy is not a full substitute for polysomnography. Quiet wakefulness can look like sleep, detailed sleep-stage estimation remains limited, and stronger systems still need ground truth against validated reference measures.
Related Yenra articles: Sleep Analysis, Sleep Environment Optimization, Health Monitoring Wearables, Telemedicine, Elderly Care Management, and Patient Outcome Prediction.
Related concepts: Digital Biomarker, Remote Patient Monitoring, Time Series Forecasting, Anomaly Detection, and Ground Truth.