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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.
SenseCare
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 :
SenseCrowd
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 :
SenseSafe
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 :
SenseLearn
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,