Opsis debuts world 1st Multi-modality Emotion Recognition Software - SenseCare version 2.0

SINGAPORE - Sept. 8, 2022 - PRLog -- Opsis is releasing SenseCare version 2.0, a new multi-modal Facial, Speech tone and Body language emotion recognition AI software. The A*STAR spin-off, helmed by industry veteran Dr Stefan Winkler and Andrew Ow, aims to disrupt the healthcare industry by helping volunteers and counselors to assess and evaluate quickly and accurately in detecting mental health issues and provide early interventions.

Opsis Emotion AI launches with its flagship product SenseCare 2.0: a multi-modal, emotion recognition AI software that combines Face (Emotion), Voice tone (speech) and Posture (body language) with Psychological annotation to identify a person's sentiments, which is a world first to have this capability. A Healthcare grade technology that utilises a highly accurate Psychology Circumplex Model resulting in more precise emotion measurements with Multi-dimension data points.

"This will provide our user a deep, real-time insights into an almost endless array of a person's emotions with precision thereby generating comprehensive integrated reports based on various parameters and analysis of data collected." says Andrew Ow, CEO and Co-Founder of Opsis Emotion AI.

Our tool can assist physicians and caregivers in improving their psychological assessments by identifying signs of non-motor Stage 1 and Stage 2 symptoms for Depression, Anxiety, Cognitive decline, Psychosis, insomnia and Hallucinations and design more appropriate interventions to prevent behavioural and psychological deterioration, and improve the quality of life for the elderly.

Andrew further adds, "Our proven proprietary emotion recognition technology and expertise are core to our capabilities and we are ready to showcase these to potential partners."

Visit https://opsis.sg/ for more details.
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About Opsis
Opsis is a spin-off from A*STAR, with its core technology of award winning emotion recognition AI being developed after 5 years of R&D. Its machine-learning algorithm is trained from close to half a million annotated datasets of patients and non-patients from various psychological fields. It utilizes Psychology Circumplex Model with accuracy of up to 93%.