PIRMedic: physics-driven fault diagnosis for PIR sensors

ABSTRACTPassive Infra-Red (PIR) sensors are an integral part of modern living. They have diverse applications ranging from automatic lighting and heating control in smart buildings, towel dispensers in washrooms, security alarms (for intrusion detection) to even human detection robots (for search and rescue). Unfortunately, PIR sensors are prone to failures during deployment due to reasons such as environmental damage, incorrect installation and component degradation among others that can lead to incorrect or faulty data. Currently, such failures are typically detected using either : (a) heavily engineered data-driven, statistical approaches that can have high false positive rates due to unseen data patterns or (b) expensive methods that use additional hardware such as video cameras or a golden reference sensor. The second approach inhibits scalability. In this work, we first create a taxonomy for the most common PIR sensor failures. We then present PIRMedic---a physics-driven approach, implemented at the edge, to detect the various classes of failures. We show that we can both detect and diagnose the failures in a PIR sensor using an intrinsic hardware signal viz., the analog output from the pyroelectric element in the sensor. Using this hardware signal in conjunction with frequency analysis and supervised machine learning methods, we obtain a high accuracy of 98 -- 99% in failure detection and diagnosis. We evaluate our methods using real-world deployments in three distinct locations, in different environment and usage conditions.