RELEVIUM
Our Technology
RELEVIUM uses three tools to track symptom progression: wearable devices, AI algorithms, and patient and caregiver applications, enabling physicians to apply personalized care plans to the patients.
Wearable Devices
A multi-sensor smartwatch and an innovative remote ultrasound patch
The smartwatch includes a complex array of sensors that will continuously compute indicators related to the patient’s state such as heart rate variability, respiratory rate, estimates of stress level, sleep quality indicators, and physical activity related indicators.
The ultrasound skin patch will be used to frequently measure the patient’s muscle mass. The sensors will also be able to identify the progression of muscle atrophy and quantity, allowing for direct measurement and monitoring of cardiac performance.
In combination with machine learning and signal processing algorithms, these wearable devices will contribute to individualized lifestyle interventions created by the physician for the patients.
AI Algorithms
For continuous remote monitoring of pain and sarcopenia, as well as for decision support
All the information, whether the indicators from the watch, patch, or patient or clinician self-report, will be aggregated and available to view through a dashboard. These solutions will support individualized and customized care plans. With AI-driven health services, data analysis will be used to derive causal models for estimates of future disease trajectory and provide early warnings of deterioration of patients´ QoL. Patients' nutrition monitoring and physical activity will also be derived from these wearable devices.
High-Level
Architecture
Showcasing the communication that exists between the patient, caregiver, and wearable devices
The web platform will receive data obtained from the complex array of sensors that exist within the smartwatch and skin patch, while simultaneously receiving data from the patient and caregiver self-reports. On this platform, the data will be analyzed and transformed into meaningful indicators regarding the state of the patient, available for viewing on the dashboard.
