Tunstall Emergency Response collaborated with the Technological University of the Shannon (TUS) through the Enterprise Ireland-supported COMAND Technology Gateway to explore the application of artificial intelligence and mobile sensing technologies to enhance fall detection and telecare services.
The collaboration was delivered through an Enterprise Ireland Innovation Partnership project, supporting applied research and innovation between industry and academia. The COMAND Technology Gateway is co-funded by the Government of Ireland and the European Union through the ERDF Southern, Eastern & Midland Regional Programme 2021-27
The joint research initiative, FallResp, focused on developing a mobile-based fall detection and response system capable of leveraging smartphone sensors and intelligent data analysis to support more responsive and reliable emergency monitoring.
As telecare providers continue to expand services that support independent living and remote care, they face increasing technical and operational challenges, including:
- Ensuring reliable detection of falls in real-world conditions
- Reducing false alarms that can place unnecessary burden on monitoring centres
- Extending monitoring capabilities beyond traditional fixed-location devices
- Integrating emerging digital technologies into existing telecare services
- Supporting scalable and responsive emergency response systems
Traditional fall detection solutions, often based on fixed wearable devices or manual alerts, can be limited in their ability to adapt to diverse user behaviours and environments.
Development of an intelligent mobile-based system
Through the FallResp project, Tunstall Emergency Response and COMAND explored the development of an intelligent mobile-based system capable of detecting falls and supporting emergency response services using smartphone technologies. The research focused on developing and evaluating a prototype system that leverages mobile sensors, cloud-based data processing, and machine learning techniques. Key elements include:
Mobile-Based Fall Detection Platform: A prototype mobile system capable of capturing sensor data from smartphones to support fall detection and monitoring.
AI-Driven Detection Models: Machine learning techniques were explored to analyse motion patterns and distinguish fall events from normal daily activities.
Cloud-Based Data Processing: A backend infrastructure was developed to support data collection, analysis, and system evaluation.
Experimental Data Collection Framework: Controlled experiments and testing scenarios were designed to collect activity data and validate fall detection models.
Cross-Platform System Development: The system was designed to operate across widely available mobile platforms, demonstrating the feasibility of scalable digital health solutions.
Impact of project
The FallResp project demonstrates how artificial intelligence and mobile sensing technologies can enhance telecare services and support more responsive emergency monitoring systems.
Key outcomes include:
- Demonstration of the feasibility of smartphone-based fall detection solutions
- Improved understanding of how mobile sensors and AI can support fall monitoring
- Development of prototype technologies that can support future telecare innovations
- Strengthened collaboration between industry and academic researchers
- New opportunities for research and innovation in digital health technologies
The project ran from March 2024 to November 2025, delivering several research and development milestones focused on system design, prototype development, and experimental validation.
James Doyle, Managing Director of Tunstall Emergency Response Ltd commented “Working with the Technological University of the Shannon enabled us to explore innovative AI-driven approaches to fall detection and emergency response. The collaboration brought together our operational expertise in telecare services with TUS’s advanced research capabilities in artificial intelligence and mobile systems. The results demonstrate the potential to significantly improve fall detection accuracy while extending monitoring services beyond traditional indoor environments.”
Reflecting on the collaboration, Dr Yuansong Qiao, Research Fellow at COMAND Technology Gateway stated “This project provided an excellent opportunity to apply advanced artificial intelligence and mobile sensing technologies to a real-world healthcare challenge. Working closely with Tunstall Emergency Response allowed us to design and validate innovative fall detection algorithms that can operate reliably on everyday smartphones.”
The FallResp project demonstrates how emerging artificial intelligence and mobile technologies can support the evolution of telecare services, helping to enable more flexible, scalable, and intelligent emergency response systems and reinforces:
- Tunstall Emergency Response’s commitment to innovation in telecare and emergency response technologies
- TUS and the COMAND Technology Gateway’s role in delivering applied research solutions to industry challenges
- The value of Enterprise Ireland-supported academic–industry collaboration in advancing digital health technologies
To learn more about COMAND Technology Gateway and explore their expertise and services visit their website for more information or follow on LinkedIn.
