Sleep Disorder Recognition using Wearable Sensor and Raspberry Pi

Sleep analysis is usually done inside a sleep laboratory under close supervision of doctors with the help of cardiac rhythm using electro cardiography (ECG), breathing patterns, brain activities using electro encephalography (EEG), eye movement using electrooculography (EOG) and muscle activity during sleep. The data collected through these devices is thus utilized for further analysis and has uncertainty and noise. In this paper, a novel approach for sleep analysis is discussed with Raspberry Pi. Sample data is collected during night with the sensor attached to the patient’s pillow and observations are made during various sleep stages. The data is also utilized for the calculation of sleep efficiency that is further divided into awake, light sleep and deep sleep percentage as shown by the hypnogram. This methodology helps us in easy analysis of quality of sleep and calculation of sleep debt. The methodology is useful for the patients who are unable to go to the hospital.