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Measures of sleep quality and quantity can provide valuable insights into the health and wellbeing of an individual. Traditionally, sleep assessments are performed in the clinic/hospital setting using polysomnography tests. Recent advances in wearable sensor technology have enabled objective assessment of sleep at home. Actigraphy has been widely used for this purpose and several algorithms have been published in the literature over the years. However, implementation of these algorithms is not widely available, which creates a barrier for wider adoption of wearable devices in clinical research.

SleepPy: A python package for sleep analysis from accelerometer data