GAiST
Self-determined living in old age thanks to smart home technology - The ageing population poses new challenges for society and the healthcare system. Older people want to live independently in their own homes for as long as possible, while at the same time security and support must be guaranteed. The GAIST project is developing an intelligent smart home system that records everyday situations, health conditions and preferences of residents and provides adaptive support. By combining sensor technology, AI-supported data analysis and personalized recommendations for action, risks can be identified at an early stage, routine tasks can be made easier and individual assistance services can be provided. The use of a semantic system architecture enables flexible integration of different devices and applications, improves quality of life and supports safe, autonomous living in old age.
Project description
The increasing ageing of the population poses new challenges for the healthcare system and social infrastructure. Older people want to live independently in their own homes for as long as possible, while at the same time safety, medical care and everyday support must be guaranteed. Standardized assistance solutions are reaching their limits here, as individual needs, living environments and health conditions vary greatly.
The GAIST project meets this challenge with a smart home system that enables personalized support in everyday life. Sensor technology, AI-supported data analysis and adaptive assistance modules record residents' individual routines and preferences. Based on this data, tailored recommendations and action aids are provided that detect risks at an early stage, make routine tasks easier and promote safe, autonomous living. The use of a semantic system architecture allows the flexible integration of different devices and services, resulting in a coherent, adaptive assistance system.
Focal points of the project
- Personalized assistance: providing individual support in everyday life based on the user's health status, preferences and routines
- Adaptive smart home technology: dynamic adaptation of assistance modules to changing needs and living situations.
- Semantic system architecture: Use of structured, machine-readable representations to integrate various sensors, devices and applications for a coherent, safe living environment.
Everything at a glance
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Duration
01.02.2024 - 31.07.2026 (Ongoing) -
Research area
Sensor technology and health -
Funding
As part of the "SmartLivingNEXT" technology program by the Federal Ministry of Economics and Climate Protection (BMWK), all partners are funded with 2.6 million euros
Research methods & procedure
FRACTAL adopts a disruptive, cross‑layer methodology. The project uses cutting‑edge microelectronics, high‑performance processors and AI techniques to build a modular, open reference architecture for cognitive edge computing. A modular open reference architecture allows components to be selected and configured based on application needs and is visualised through the FRACTAL System configurator. Hardware advances include a Hierarchical Adaptive Time‑Triggered Multicore Architecture (HATMA) that enables adaptation while balancing safety, security, power and environmental scenarios; it leverages an Adaptive Time‑Triggered Network on Chip (ATTNoC) for scheduling adaptation. An AI‑based metascheduler supports dependable, time‑critical scheduling and run‑time adaptation. The software stack uses microservices and provides data ingestion, storage, orchestration and containerised ML model servers; this ensures the same open architecture across low‑, mid‑ and high‑end edge nodes. FRACTAL validated its methodology through eight real‑world use cases, demonstrating the modular architecture and edge AI capabilities across domains
Development of specialized AI models
We are developing modern AI models that are trained to recognize complex patterns in the electrical activity of the heart. We focus on five central topics: the evaluation of signal quality, precise heartbeat detection and the detailed analysis of rhythm, morphology and specific cardiological events.
Implementation of a cloud edge architecture
To make the analysis highly efficient, we use a combined computing strategy. This involves pre-processing the data directly on the mobile device (edge), while computationally intensive analyses are carried out in the cloud. This enables fast and in-depth monitoring of cardiac arrhythmias in real time.
Prevention and therapeutic support
The knowledge gained is used to initiate medical measures in good time, whether in prevention, ongoing therapy or follow-up treatment. The aim is holistic monitoring that both increases safety and enables tailored support in everyday life.
