The objective of this research is to study active sensing and adaptive fusion using vision and acoustic sensors for continuous, reliable fall detection and assessment of fall risk in dynamic and unstructured home environments. The approach is to incorporate active vision with infrared light sources and camera controls, an acoustic array that identifies the sound characteristics and location, and sensor fusion based on the Choquet integral and hierarchical fuzzy logic systems that supports uncertain heterogeneous sensor data at varying time scales, qualitative data, and risk factors. The project will advance the state of the art in (1) active vision sensing for human activity recognition in dynamic and unpredictable environments, (2) acoustic sensing in unstructured environments, (3) adaptive sensor fusion and decision making using heterogeneous sensor data in dynamic and unpredictable environments, (4) automatic fall detection and fall risk assessment using non-wearable sensors, and (5) algorithms for cyber physical systems that address the interplay of anomaly detection (falls) and risk factors affecting the likelihood of an anomaly event. The project will impact the health care and quality of life for older adults. New approaches will assist health care providers to identify potential health problems early, offering a model for eldercare technology that keeps seniors independent while reducing health care costs. The project will train the next generation of researchers to handle real, cyber-physical systems. Students will be mentored, and research outcomes will be integrated into the classroom. Novel outreach activities are planned to reach the elderly community and the general public