Acquisition and Automated Segmentation of Inertia Sensor Data for Mobile Camptocormia Assessment

The camptocormia angle has been established as a strong indicator for evaluating the progress of Parkinson's disease and the efficacy of therapeutical approaches. A wearable setup is proposed to measure the camptocormia angle with the perpendicular method using five inertial sensors. This study identifies suitable inertial measurement unit sensors for mobile long-term measurement. Moreover, a machine-learning approach is presented for segmenting the recorded data into periods with different dominant activities. An artificial neural network was the better classifier compared to a support vector machine to recognize certain common activities in patients with camptocormia. The artificial neural network's accuracy, sensitivity, and F1-score were 92.4 %, 82.9 %, and 82.1 %, respectively. Clinical Relevance- The presented approach is expected to lead to a wearable system for long-term monitoring of the progress of camptocormia, yielding improved parameters compared to the conventional static photo method.