Multi-Limb Sensor Fusion for Actigraphy-Based Sleep/Wake ClassificationdissertationsGreen, Dylan NathancreatorGray, MarissaAdvisorWebb, AndreaAdvisorHughes, ChristopherReaderBrown University. Department of Computer Sciencesponsor20262, 35 p.born digitalThesis (Sc. M.)--Brown University, 2026thesesActigraphy-based sleep monitoring has gained popularity due to its affordability and unobtrusive nature compared to polysomnography (PSG), the clinical gold standard. However, existing single-sensor actigraphy methods suffer from a fundamental class imbalance; since the dominant class, sleep, constitutes 80 − 90% of a night of study, algorithms that optimize for sleep detection accuracy consistently misclassify the minority wake class, inflating estimates of total sleep time while under-reporting wakefulness. We directly address this challenge with adaptations of the Cole-Kripke and Sadeh sleep detection algorithms, modified to support data fusion at the data, score, and decision levels from four Axivity AX6 actigraphy sensors, one on each wrist and ankle. Rather than optimizing for sleep detection, we report F1, Cohen’s Kappa, and MCC, evaluating our algorithms’ ability to detect both sleep and wake accurately. Validated against simultaneous PSG at the Brown University Health Sleep Lab, our results indicate that multi-sensor fusion eliminates placement-dependent F1 variance: the leading single-sensor algorithm had a 31% relative F1 difference between wrists, a risk which every multi-sensor approach mitigates. As a pilot study with four participants, we identified trends rather than specific benchmarks: higher-level wake-biased fusion strategies consistently outperformed lower-level fusion, thresholds from the seminal algorithms need to be adjusted for multi-sensor approaches, and Cole-Kripke adaptations consistently outperformed Sadeh. These findings establish multi-sensor actigraphy as a robust, low-cost approach to wakefulness detection, with further implications for operational contexts requiring simple, cheap monitoring hardware.SleepMultisensor data fusionClass ImbalanceEnglishRPB20260516