Medical diagnostic-based sensor selection

Wearable sensing systems have facilitated a variety of applications in Wireless Health. Due to the considerable number of sensors and their constant monitoring these systems are often expensive and power hungry. Traditional approaches to sensor selection in large multisensory arrays attempt to alleviate these issues by removing redundant sensors while maintaining overall sensor predictability. However, predicting sensors is unnecessary if ultimately the system needs only to quantify diagnostic measurements specific to the application domain. We propose a new method for optimizing the design of medical sensor systems through diagnostic-based bottom-up sensor selection. We reduce the original sensor array from ninety nine to twelve sensors while maintaining a prediction error rate of less than 5% over all diagnostic metrics in our testing dataset.