The assessment and treatment of motor symptoms such as tremor in Parkinson's disease depends exclusively on the physician's visual observation of standardised movements (i.e. motor tasks). Wearable sensors such as accelerometers are able to detect some manifestations of these pathological signs in movement disorders. Sensor data from motor tasks, however, must be processed sequentially with annotated data from clinical experts. Hence, we designed TreCap, a custom-built wearable device with new software to capture and evaluate motor symptoms such as tremor in real time. Inertial sensor data is systematically processed, stored and tailored to each motor task by this software, including annotated data from clinical rating scores and deep brain stimulation parameters. For prototype testing, the wearable device was validated in a pilot study on subjects with physiological hand tremor. The processed data sets are suitable for machine learning to classify motor tasks. Results on healthy subjects demonstrate an accuracy of 95% with support vector machine algorithms. TreCap software is expandable and allows full access to the configuration of all sensors via Bluetooth. Finally, the functions of the entire device provide a platform to be apt for future clinical trials.