{'fraction_non_alphanumeric': 0.039160839160839164, 'fraction_numerical': 0.008391608391608392, 'mean_word_length': 5.45045045045045, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
An Inter-Device Accuracy Comparison of Consumer Sleep Trackers

The ability to investigate sleep is of scientific and clinical interest. Polysomnography (PSG) has long been considered the gold standard assessment for sleep physiology; however, its cost and inconvenience have spurred the development of consumer devices capable of evaluating sleep outside the laboratory. The development of dedicated consumer sleep monitoring devices, e.g., the Zeo Personal Sleep Manager, smart bands, e.g., the Microsoft Band 2 (MB2), and activity trackers, e.g., the Fitbit Charge 2 (FC2), with the ability to automatically distinguish between sleep and wakefulness has important implications for sleep research and medicine.1–3