The current work describes a methodology to automatically detect the severity of bradykinesia in motor disease patients using wireless, wearable accelerometers. This methodology was tested with cross validation through a sample of 20 Parkinson's disease patients. The assessment of methodology was carried out through some daily living activities which were detected using an activity recognition algorithm. The Unified Parkinson's Disease Rating Scale (UPDRS) severity classification of the algorithm coincides between 70 and 86% from that of a trained neurologist depending on the classifier used. These severities were calculated for 5 second segments of the signal with 50% of overlap. A bradykinesia profiler is also presented in this work. This profiler removes the overlap of the segments and calculates the confidence of the resulting events. It also calculates average severity, duration and symmetry values for those events. The profiler has been tested with a bogus dataset. Future work includes better training for the severity classifier with a larger sample and testing the profiler with real, longterm patient data in a projected pilot phase in three European hospitals.