A Statistical (Process Monitoring) Perspective on Human Performance Modeling in the Age of Cyber-Physical Systems

With the continued technological advancements in mobile computing, sensors, and artificial intelligence methodologies, computer acquisition of human and physical data, often called cyber-physical convergence, is becoming more pervasive. Consequently, personal device data can be used as a proxy for human operators, creating a digital signature of their typical usage. Examples of such data sources include: wearable sensors, motion capture devices, and sensors embedded in work stations. Our motivation behind this paper is to encourage the quality community to investigate relevant research problems that pertain to human operators. To frame our discussion, we examine three application areas (with distinct data sources and characteristics) for human performance modeling: (a) identification of physical human fatigue using wearable sensors/accelerometers; (b) capturing changes in a driver’s safety performance based on fusing on-board sensor data with online API data; and (c) human authentication for cybersecurity applications. Through three case studies, we identify opportunities for applying industrial statistics methodologies and present directions for future work. To encourage future examination by the quality community, we host our data, Code, and analysis on an online repository.