David Ada Adama

About

David Ada Adama is a leading researcher at the intersection of artificial intelligence, assistive robotics, and ambient assisted living. His work focuses on enabling robots to learn, recognize, and adapt to human activities of daily living—a critical capability for supporting aging populations and individuals with care needs. Adama’s most influential contribution is his 2018 paper on human activity learning using a classifier ensemble (35 citations), which demonstrated how RGB-depth sensors can equip assistive robots with robust learning capabilities in real-world environments. He has also pioneered adaptive segmentation and sequence learning from skeleton data, and advanced the application of transfer learning to bridge the gap between human and robot action domains—allowing robots to generalize from limited training data. His more recent work extends into cloud-based monitoring systems for older adults in community settings, integrating IoT and low-cost sensing for unobtrusive behavior modeling. With a consistent focus on making assistive technology more intelligent, adaptive, and accessible, Adama’s research is shaping the next generation of socially aware robotic caregivers.

Research Focus

Key Achievements

Top Papers

1. 1
   Human activity learning for assistive robotics using a classifier ensemble

   35 citations · 2018
2. 2
   Adaptive Segmentation and Sequence Learning of human activities from skeleton data

   9 citations · 2020
3. 3
   Human Activities Transfer Learning for Assistive Robotics

   5 citations · 2017
4. 4
   A Centralised Cloud-Based Monitoring System for Older Adults in a Community

   3 citations · 2021
5. 5
   Learning Human Activities for Assisted Living Robotics

   3 citations · 2017
6. 6
   Key Frame Extraction and Classification of Human Activities Using Motion Energy

   2 citations · 2018
7. 7
   Transfer Learning in Assistive Robotics: From Human to Robot Domain

   2 citations · 2019

Key Collaborators

Contact & Links