Fatigue-Malaise
Fatigue-Malaise screening by GIA®, powered by digitalhumanOS™, uses Voice AI, Computer Vision, and Speech Biomarkers to detect fatigue-malaise through a single patient conversation. Screening performance: 71.7% accuracy. Screening takes under 5 minutes with results in 60 seconds.
Persistent fatigue drains energy and motivation, hindering rehabilitation and diminishing quality of life.
Fatigue and malaise can significantly impact patient engagement and progress in post-acute care. Identifying these conditions allows for tailored care plans and improved outcomes.
Key Facts
- Screening Time
- Under 5 minutes
- Results
- 60 seconds
- Modalities
- Voice + Vision + Speech
- Registration
- 510(k)
- Status
- Live
About Fatigue-Malaise screening.
What are the advantages of using AI to detect fatigue and malaise?
AI provides an objective and consistent assessment, complementing subjective patient reports.
How can this information be used to improve patient outcomes?
Identifying fatigue allows for adjustments to therapy schedules, nutritional support, and other interventions to improve energy levels.
What is the accuracy rate for detecting fatigue and malaise?
The model achieves an accuracy rate of 71.7%.
The science behind Fatigue-Malaise screening.
Research on voice technology for health monitoring in older adults validates fatigue detection through speech analysis — with direct applications to post-acute and long-term care settings.
Voice Technology to Identify Fatigue from Japanese Speech (2023-07)Peer-reviewed research demonstrates that fatigue can be extracted as a measurable voice feature — enabling objective clinical assessment without patient self-reporting.
Fatigue Model for Japanese Speech (2023-02)