Advances in sensor miniaturization and computational power have served as enabling technologies for monitoring human physiological conditions in real-world scenarios. Sleep disruption may impact neural function, and can be a symptom of both physical and mental disorders. This project proposes wearable in ear electroencephalography (ear- EEG) for overnight sleep monitoring as a 24/7 continuous and unobtrusive technology for sleep quality assessment in the community. Twenty two healthy participants took part in overnight sleep monitoring with simultaneous ear-EEG and conventional full polysomnography (PSG) recordings. The ear-EEG data were analyzed in the both structural complexity and spectral domains; the extracted features were used for automatic sleep stage prediction through supervised machine learning, whereby the PSG data were manually scored by a sleep clinician. Results: The agreement between automatic sleep stage predictions based on ear-EEG from a single in ear sensor and the hypnogram based on the full PSG was 74.1% in the accuracy over five sleep stage classification; this is supported by a Substantial Agreement in the kappa metric (0.61). The in ear sensor is both feasible for monitoring overnight sleep outside the sleep laboratory and mitigates technical difficulties associated with PSG. It therefore represents a 24/7 continuously wearable alternative to conventional cumbersome and expensive sleep monitoring. Significance: The ‘standardized’ one size fits all viscoelastic in-ear sensor is a next generation solution to monitor sleep this technology promises to be a viable method for readily wearable sleep monitoring in the community, a key to affordable healthcare and future eHealth. This project is implemented with MATLAB software.