Human-Centered Sensing

Catalog description

This course introduces students to a wide range of human-centered signals captured through hardware and software that are widely used in both daily life and human systems engineering research. The course is organized into a series of modules, each focusing on a distinct category of human-centered sensing, including behavioral, kinematic, physiological, and affective dimensions. Each module will cover the associated sensing technologies, relevant hardware and software, data analytics methods, and key research topics. Students will gain hands-on experience configuring wearable devices for human-subject data collection, practice signal processing and machine learning techniques to mine multimodal human-centered sensing data, and conduct literature reviews on the latest research on the applications of human-centered sensing in engineering, healthcare, and social science domains. Topics include: GPS positioning and BLE/UWB proximity sensors (behavioral sensing); accelerometers and pressure sensors (motion and kinematic sensing); photoplethysmography (PPG) and electrodermal activity (EDA) sensors (physiological sensing), and facial affect recognition (affective sensing). Learning objectives are threefold: (1) develop familiarity with human-centered sensors and software; (2) acquire practical skills in signal processing and data mining techniques, and; (3) build a strong foundation for interpreting and critiquing cutting-edge literature in the field. Prerequisites: Graduate Standing and SSIE Z500 Computational Tools (or equivalent computer programming background). Offered every Fall semester or as needed.