Distributed multisensor processing and classification under constrained resources for mobile health monitoring and remote environmental monitoring

Recent advancements in sensors, wireless technology, and a reduction in the form factor of computing devices, provide the realization of true autonomy in mobile sensing systems. Past field-deployable sensing systems for health-biomedical applications and even environmental sensing have been designed for data collection and data transmission at pre-set intervals, rather than for on-board processing. This lack of true autonomy has resulted in systems with lower lifetimes and those that require large amounts of bandwidth to transmit all sensory data at all times. The use of a new, general machine learning architecture that can be used for a variety of autonomous sensing applications that have very limited computing, power, and bandwidth resources is proposed in this paper. The general solutions for efficient processing in a multi-tiered (three-tier) machine learning framework that is suited for remote, mobile sensing systems with low computing capabilities is provided. Simple pattern recognition methods are used at the sensor level to filter significant events. Novel dimensionality reduction techniques that are designed for classification are used to compress each individual sensor data and pass only relevant information to the mobile multisensor fusion module (second-tier). Statistical classifiers that are capable of handling missing/partial sensory data due to sensor failure or power loss are used to detect critical events and pass the information to the third tier (central server) for manual analysis and/or analysis by advanced pattern recognition techniques. The applicability of the proposed technology in mobile health & alcohol monitoring is shown. Other uses of the provided solutions are also discussed.