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Scienza Health
Live NowMental/Behavioral Health

Energy Levels

Energy Levels screening by GIA®, powered by digitalhumanOS™, uses Voice AI, Computer Vision, and Speech Biomarkers to detect energy levels through a single patient conversation. Screening performance: Clinically validated vocal biomarker screening. Screening takes under 5 minutes with results in 60 seconds.

Low energy drains motivation and limits participation, impacting rehabilitation and delaying recovery.

Tracking energy levels allows for personalized therapy schedules in post-acute care. By understanding patient energy levels, we can optimize care plans and improve outcomes.

Screening PerformanceClinically validated vocal biomarker screening

Key Facts

Screening Time
Under 5 minutes
Results
60 seconds
Modalities
Voice + Vision + Speech
Registration
510(k)
Status
Live
510(k) RegisteredClinically reviewed content·
FREQUENTLY ASKED

About Energy Levels screening.

How does the system monitor patient energy levels?

The system analyzes vocal patterns, speech characteristics, and behavioral cues to assess energy levels.

How can this information be used to improve therapy outcomes?

Identifying low energy levels allows for adjustments to therapy schedules, nutritional support, and other interventions to improve patient engagement.

Is there any clinical evidence to support the use of this validated system?

Yes, the system is clinically validated and provides an objective measure of energy levels, enhancing care planning.

CLINICAL RESEARCH

The science behind Energy Levels screening.

PEER-REVIEWED RESEARCH

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

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)

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