The detection of involuntary and often imperceptible resting tremor of the hand is crucial for early diagnosis and effective treatment of neurological diseases such as Parkinson's disease. Until recently, the diagnosis was mainly subjective, as the estimate of the frequency and the intensity of the phenomenon was based on the cognizance of the attending physician. Furthermore, clinical conditions for recording the symptoms using accelerometer, alters the nature of the disease, and classifies the examination of the patient as invasive.  Howbeit, the objective criteria for continuous and non-invasive recording of the hand motion are being met by the daily interaction with the mobile phone. The employment of apt signal processing techniques and machine learning methods to signals recorded by phone’s accelerometer, can automate the immediate detection of the resting tremor in an objective and quantified way.  The "Multimedia Understanding Group" of "Information Processing" laboratory of the Electrical and Computer Engineering Department of the Aristotle University of Thessaloniki, under the direction of Professor Anastasios Delopoulos, after presenting the predominant signal processing techniques for the detection of tremor, will challenge the attendees in a “hackathon” and will provide them with the opportunity to implement rest tremor automatic detection algorithms from the aforementioned recordings.