Ken Inoue
Papers
1
Total Citations
14
H-Index
1
About
Ken Inoue is a leading researcher at the intersection of ambient intelligence, gerontechnology, and human-robot interaction, with a focused commitment to improving quality of life for aging populations. His most cited work, "Dementia Scale Classification Based on Ubiquitous Daily Activity and Interaction Sensing" (2019, 14 citations), exemplifies his core contribution: pioneering non-invasive, sensor-driven methods for early cognitive decline detection. In this seminal paper, Inoue demonstrates how integrating passive indoor activity monitoring with active behavioral data from humanoid robot interactions can automatically classify dementia severity, moving assessment from clinical settings into everyday life. This dual-sensing approach—capturing both routine movement patterns and nuanced social engagement with robots—represents a significant methodological advance in ubiquitous healthcare. Inoue’s research is notable for its human-centered design, leveraging technology not as a replacement for care but as a subtle, continuous diagnostic tool. His work directly addresses the global challenge of aging, offering scalable, data-driven pathways to earlier intervention. For students and researchers, Inoue’s profile is a compelling model of how interdisciplinary engineering can create compassionate, practical solutions for one of society’s most pressing health crises.
Research Focus
Key Achievements
Top Papers
- 1