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Annotated datasets from various phycological fields are used to train our machine-learning algorithm.

After extensive training and validation, the algorithm recognizes minutiae scale of emotions for applications across our four product platforms: SenseCare, SenseLearn, SenseSafe and SenseCrowd.

The strength of our algorithm results from the volume and granularity with patients and non-patients scientific investigation. This genuine data enables it to detect and assess a broad range of emotions with precision.



Annotated datasets from the Clinical, Health, Counselling and Psychological fields are used to train our algorithm to reveal emotions that can be used as a tool to aid assessment and tracking of patient’s mental health wellness.
Use cases include :



Annotated datasets derived from Cross-cultural, Developmental, Social and Forensic Psychology fields are used to train our algorithm to detect emotions of suspicious characters and prevent occurrences of violent behaviour. Use cases include :



Annotated datasets derived from Cognitive, Experimental and Social Psychology fields are used to train our algorithm to recognise emotions of driver’s drowsiness, fatigue or sudden medical changes. Use cases include :



Annotated datasets derived from Behavioural, Educational, and Human Factors Psychology fields are used to train our algorithm to identify the attention and engagement levels of participants / users. Use cases include :

Opsis is focused on advancing its emotion recognition technology and helping our clients address challenges faced in key industries : healthcare, automotive, security, education and training and media analytics,