An Edge AI-Vision Model for Elderly Social Assistance Robots Using Edge Impulse

Social robots are increasingly deployed in elderly care to provide companionship, emotional support, and assistive monitoring. AI-based vision models have enabled these robots to detect and interpret facial features in support of personalized engagement, but many current systems rely on cloud-based architectures that raise privacy and ethical concerns around the transmission of sensitive biometric data. Beyond interception risk, algorithmic bias stemming from insufficient dataset diversity may produce inferences that perform unequally across demographic groups. This study proposes Edge AI vision as a privacy-preserving framework for elderly care robotics. An Edge AI vision model was developed using Edge Impulse and trained to identify the eyes and mouth of male and female elderly subjects aged 55–94. The model achieved classification accuracy of 98.04% on male subjects and 99.51% on female subjects, with no statistically significant difference between groups (z = 1.43, p = 0.15), suggesting that edge-based inference can deliver equitable and privacy-preserving vision capabilities for elderly care robotics