Current Developments in Elderly Care Technology

The field of elderly care technology is moving towards more innovative and efficient solutions for ensuring the safety and well-being of elderly populations. Recent research has focused on developing real-time human action recognition models, interactive virtual companion systems, and robust fall detection systems that can be implemented in assisted living environments without requiring expensive sensors or high-end computational resources.

These advancements have the potential to enhance the quality of life for elderly users, fostering sustainable care and smarter communities. Noteworthy papers in this area include:

• A Real-Time Human Action Recognition Model for Assisted Living, which proposes a model that combines deep learning and live video prediction to predict falls and other health risks.
• CyanKitten: AI-Driven Markerless Motion Capture for Improved Elderly Well-Being, which introduces an interactive virtual companion system that utilizes advanced posture recognition and multimodal interaction capabilities.
• Pose-Based Fall Detection System: Efficient Monitoring on Standard CPUs, which presents a robust fall detection system that uses pose estimation techniques and threshold-based analysis to distinguish between fall and non-fall activities.
• ForcePose: A Deep Learning Approach for Force Calculation Based on Action Recognition, which introduces a novel deep learning framework that estimates applied forces by combining human pose estimation with object detection.