Use Cases & Trials
UC HEALTH 2 - Elderly Monitoring
5G/6G, Vital Signs Monitoring, Eldery Care, Reha, Fall Detection, Device Location, QoS Enforcement
This use case is addressing the healthcare sector and targets serving the continuous monitoring of the elderly population through the use of beyond 5G core network functionalities, wearable devices and other non-invasive monitoring tools. The objective of this use case is to validate and evaluate an innovative approach to provide a higher level of elderly monitoring and care. The real-time analysis and processing of data is essential to enhance the security of the elderly and to improve their quality of life.
Partners
Use Case Overall
This use case relies on 5G-/6G-enabled networks to provide continuous monitoring of elderly through wearable devices and other non-invasive monitoring tools, which require real-time data analysis and processing to increase elderly security and increase their quality of life. The devices’ real-time transmission of gathered data, assistant features and assisted workouts, through novel AI-powered data analysis, enables advanced automatic pre- and post-diagnostics and threat assessment that can “trigger” immediate intervention (emergency responders or care providers, e.g. trainers) and improve healthcare provider decision-making through better-informed data.
This use case aims to deploy an integrated monitoring tool in a set of controlled elderly populations with the following set of devices and applications:
Smartwatch with a multitude of sensors, advanced fall detection coupled with an analytics application analysing the patient data and assessing the risk of falls.
Pain detection algorithm for smart wearable devices.
3D Body Tracking Sensor running the meineReha Coaching App acting as workout diary and training assistant.
Telehealth platform for storing patient records and OTT multimedia services for regular teleconferencing between the elderly and their hospital care providers.
Besides the state-of-the-art sensors and knowledge gathered by these sensors, this use case aims to assess the feasibility and benefits of the usage of advanced sensors (such as pain detection, integrated sensors that include glucose measurement and in- and outdoor air quality) with Edge and Core data processing through AI-powered algorithms that can in real-time provide enhanced knowledge for the patient or residents and medical staff, but also input to the smart virtual assistant that can provide enhanced feedback and support to the elderly person. Some of the AI-powered algorithms used are: anomaly/threat detection (intelligent correlation of factors); advanced fall detection; pain assessment during workouts; several health indicators like HRV, skin temperature, atrial fibrillation detection, pain intensity; stress detection; sleep analysis; detect different gasses and smells.
The Beyond 5G Core Network
Regarding the secure connection of services and devices, the Open5GCore software toolkit, acting as mobile core network is employed.
Open5GCore is implemented as a large collection of micro-services, each with dedicated functionality, interfaced via a service-based API similar to IT micro-services, with the API aligned with the 3GPP technical specifications (3GPP TS23501).
With this, it achieves a very high level of flexibility regarding deployment size and options, including fundamental control plane functionality such as authentication, authorization, mobility, and session management.
Specifically for largely distributed use cases with increased privacy requirements, splitting functionality between the edge of the network and the central core is assumed.
Considering the split, the edge enables fast execution of local operations, the central part maintains the communication profile information of the device, while the rest can be placed either centrally or at the edge, depending on the latency requirements, the number of devices connected to the edge, and the qualities of the edge-central transport network.
The toolkit is currently extended to support device localisation using edge intelligence and alignment with the specification TS23.273 #6.1.1. For QoS enforcement requests, Open5GCore is integrating with OpenCAPIF from Telefonica.
Key Objectives
Ensure secure and reliable connectivity and data transmission.
Enable accurate location computation.
Enable QoS enforcement via trusted Application Function interfacing.
Validate Beyond 5G features in real environment through piloting in nursing homes.
Promote physical activity among the elderly.
Improve physical mobility tracking during workouts.
Implement early physical degradation detection mechanisms.
Develop fall risk prediction capabilities.
Collect and analyse KVI related data from medical experts.
Measure technological metrics, medical accuracy.
Expected Innovations/Outcomes
Innovations
- Flexible data path.
- Location computation.
- Continuous real-time patient monitoring (vitals, fall events).
- 6G based prioritized traffic in case of fall detection.
Outcomes
- Increase Core Network technology readiness and acceptance.
- Improved fitness level for the elderly.
- Fall detection prioritized communication.
- Data enhanced training sessions (includes data to compare subjective observations of medical staff with measured data).
KPIs
- Time to establish the connectivity between the Edge and the central network over third party backhauls to be <= 5 seconds for all transport networks.
- End-to-end connectivity establishment to be <= 2 seconds.
- Have a stable connection for a maximum of 10 000 devices that can connect and exchange the end-to-end data.
- Capacity for upload of vital signs, training session recordings and results to be 300 MBs for 100 MHz of bandwidth.
- Have zero packet loss rate during peaks.
- Latency of sending prioritized messages, e.g. fall events to be less than 2 seconds in total, from the smart watch to the mobile or web App running on the devices of the personnel staff.
KVIs
- Provide optimized use of human resources for elderly care.
- Provide evidence-based care to counteract mobility issues.
- Enable early detection of infections via vital signs monitoring.
- Enable fast reaction to fall events through detection and trustworthy QoS enforcement.
- Increased caretaker career attractiveness via novel digital tools.
- Increase 5G/6G technology attractiveness in nursing homes.
- Provide accurate positioning of mobile end user devices in healthcare institutions.
- Provide reliable and secure communication for healthcare institutions.
Use Case Alignment with UN SDGs
UC-HEALTH-2 leverages 5G/6G, AI, and wearable technologies to deliver continuous, personalized, and safe monitoring for the elderly, directly advancing SDGs on health and well-being, digital innovation, reduced inequalities, and inclusive communities.
Enhances access to essential health services for the elderly, contributing to universal health coverage, by facilitating continuous health monitoring and early detection of potential health issues through advanced technologies (Target 3.8).
The deployment of advanced sensors, wearables, and AI-driven analytics in elderly care demonstrates innovative use of digital health technologies, advancing healthcare infrastructure and research (Target 9.5).
The solution addresses the needs of vulnerable elderly populations, including those with dementia or limited communication, reducing health and social inequalities by providing tailored, accessible care (Target 10.2).
Indoor-outdoor positioning and rapid caregiver response enhance the safety and autonomy of elderly individuals in both private and public environments, supporting age-friendly, inclusive communities (Target 11.7).
Trials
- Trial @ Daycare (Berlin, Charité Geriatric Research department) (starting June 2025). Semi-structured interviews: Involving seniors and experts on Geriatric Care - Validation of the system with real patient data.
- Pilot @ Nursing Home (Berlin, Johannesstift) (starting September 2025) - Long term deployment and pilot with elderly.
