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ACM Interact. Mob. Wearable Ubiquitous Technol."],"published-print":{"date-parts":[[2020,6,15]]},"abstract":"<jats:p>Traditionally, sleep monitoring has been performed in hospital or clinic environments, requiring complex and expensive equipment set-up and expert scoring. Wearable devices increasingly provide a viable alternative for sleep monitoring and are able to collect movement and heart rate (HR) data. In this work, we present a set of algorithms for sleep-wake and sleep-stage classification based upon actigraphy and cardiac sensing amongst 1,743 participants. We devise movement and cardiac features that could be extracted from research-grade wearable sensors and derive models and evaluate their performance in the largest open-access dataset for human sleep science. Our results demonstrated that neural network models outperform traditional machine learning methods and heuristic models for both sleep-wake and sleep-stage classification. Convolutional neural networks (CNNs) and long-short term memory (LSTM) networks were the best performers for sleep-wake and sleep-stage classification, respectively. Using SHAP (SHapley Additive exPlanation) with Random Forest we identified that frequency features from cardiac sensors are critical to sleep-stage classification. Finally, we introduced an ensemble-based approach to sleep-stage classification, which outperformed all other baselines, achieving an accuracy of 78.2% and F1 score of 69.8% on the classification task for three sleep stages. Together, this work represents the first systematic multimodal evaluation of sleep-wake and sleep-stage classification in a large, diverse population. Alongside the presentation of an accurate sleep-stage classification approach, the results highlight multimodal wearable sensing approaches as scalable methods for accurate sleep-classification, providing guidance on optimal algorithm deployment for automated sleep assessment. The code used in this study can be found online at: https:\/\/github.com\/bzhai\/multimodal_sleep_stage_benchmark.git<\/jats:p>","DOI":"10.1145\/3397325","type":"journal-article","created":{"date-parts":[[2020,6,15]],"date-time":"2020-06-15T22:30:37Z","timestamp":1592260237000},"page":"1-33","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":74,"title":["Making Sense of Sleep"],"prefix":"10.1145","volume":"4","author":[{"given":"Bing","family":"Zhai","sequence":"first","affiliation":[{"name":"Newcastle University, Open Lab, Urban Sciences Building, Newcastle upon Tyne, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ignacio","family":"Perez-Pozuelo","sequence":"additional","affiliation":[{"name":"University of Cambridge &amp; The Alan Turing Institute, Department of Medicine, Cambridge, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Emma A. 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