Parkinson’s disease manifests in movement disorder symptoms, such as hand tremor. There exists an assortment of therapy interventions. In particular deep brain stimulation offers considerable efficacy for the treatment of Parkinson’s disease. However, a considerable challenge is the convergence toward an optimal configuration of tuning parameters. Quantified feedback from a wearable and wireless system consisting of an accelerometer and gyroscope can be enabled through a novel software application on a smartphone. The smartphone with its internal accelerometer and gyroscope can record the quantified attributes of Parkinson’s disease and tremor through mounting the smartphone about the dorsum of the hand. The recorded data can be then wirelessly transmitted as an email attachment to an Internet derived resource for subsequent post-processing. The inertial sensor data can be consolidated into a feature set for machine learning classification. A multilayer perceptron neural network has been successfully applied to attain considerable classification accuracy between deep brain stimulation “On” and “Off” scenarios for a subject with Parkinson’s disease. The findings establish the foundation for the broad objective of applying wearable and wireless systems for the development of closed-loop optimization of deep brain stimulation parameters in the context of cloud computing with machine learning classification.