Artificial Intelligence-Based Monitoring System for Elderly Care: A Study on Human Activity Recognition

Strategic Insights -

Researchers from Mediterranea University of Reggio Calabria have developed an artificial intelligence-based monitoring system to support the care of elderly individuals. The system, which combines Internet of Things (IoT) technologies with machine learning algorithms, is designed to recognize human movements and activities, allowing healthcare staff to assess the motor skills of older individuals and identify potential health risks. The study, which was published in Applied Sciences, highlights the potential of AI-based solutions to promote independent living and reduce healthcare costs.

Key Takeaways:

  • The researchers developed an IoT-based system that integrates MEMS sensors with a state-of-the-art microcontroller to recognize human movements and activities.
  • The system uses machine learning algorithms to identify movement patterns, statistical analysis to assess the frequency and quality of movements, and data visualization to track changes over time.
  • The study aims to promote independent living among the elderly by providing healthcare staff with accurate assessments of motor skills and timely identification of potential health risks.
  • The system is designed to be user-friendly and minimize discomfort and stress associated with using technology.
  • The research concluded that the model achieved a high level of accuracy in recognizing specific movements, contributing to a precise assessment of the motor skills of the elderly.

Statistics:

  • The system achieved an accuracy of 92% in recognizing specific movements, such as grasping, leg flexion, circular arm movements, and walking.
  • The study used an artificial intelligence model called Random Forest to recognize human movements and activities.
  • The system is designed to be energy-efficient and cost-effective, promoting sustainable adoption.
  • The study highlights the potential of AI-based solutions to reduce hospital admissions and lower healthcare costs.

Sources:

  • MEMS and IoT in HAR: Effective Monitoring for the Health of Older People. Applied Sciences, 2025,15(8):4306.
  • MDPI AG (Publsher). (Applied Sciences - http://www.mdpi.com/journal/applsci)
  • Luigi Bibbo, Giovanni Angiulli, Filippo Lagana, Danilo Prattico, Francesco Cotroneo, Fabio La Foresta, Mario Versaci (Authors)
  • NewsRx. Research from Mediterranea University of Reggio Calabria in Artificial Intelligence Provides New Insights (MEMS and IoT in HAR: Effective Monitoring for the Health of Older People). Journal of Engineering. May 12, 2025; p 2957.