Sleep plays a fundamental role in the life of every human. The prevalence of sleep disorders has increased significantly, now affecting up to 50% of the general population. Sleep is usually analyzed by extracting a hypnogram containing sleep stages. The gold standard method polysomnography (PSG) requires subjects to stay overnight in a sleep laboratory and to wear a series of obtrusive devices. This work presents an easy to use method to perform somnography at home using unobtrusive motion sensors. Ten healthy male subjects were recorded during two consecutive nights. Sensors from the Shimmer platform were placed in the bed to record accelerometer data, while reference hypnograms were collected using a SOMNOwatch system. A series of filters were used to extract a motion feature in 30 second epochs from the accelerometer signals. The feature was used together with the ground truth information to train a Naive Bayes classifiers that distinguished wakefulness, REM and non-REM sleep. Additionally the algorithm was implemented on an Android mobile phone. Averaged over all subjects, the classifier had a mean accuracy of 79.0 % (SD 9.2%) for the three classes. The mobile phone implementation was able to run in realtime during all experiments. In future this will lead to a method for simple and unobtrusive somnography using mobile phones.