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Biometric emotional Analytics is mainly focused on monitoring the concept and unconscious changes of human trade by evaluating the body parameters and succeeding Complex features. As a result, it also helps in evaluating the emotions and behaviour of common people. According to the opinion of Kaklauskas et al. (2020), body posture, walking development, facial expressions and others are the basic gestures which help in determining a human's emotional state through artificial intelligence. The application of AI emotion recognition is the code Revolutionary technology that demands authenticity and clear results. It also can be stated that biometric emotional analysis is mainly used for identifying the consumer's demand to handle the consumer base as per situational maturity. Hence it also helps the consumer management team to identify the emotional arousal and mental health concerns of the target audience base to emphasise commercialisation adequately. Biometric emotional analytics also helps in identifying the fundamental principles to represent and control unique services for further consumer engagement.
Relation to computer science in general
Recognising physical or behavioural cues like fingerprints, faces, iris, walking habits, talking habits and others are related to the development of customer science in the case of biometric emotional analysis.
Research questions and methods
- What is the commercial interest of analysing human emotion based on biometric emotional Analytics?
- What are the parameters that are used in biometric monitoring to evaluate human habits?
- What are the basic challenges and opportunities of biometric emotional analytics?
It can be stated that adequate focus on researching the scanning device is able to analyse the biometric emotional Analytics. In that case, focusing on basic Technology factors for converting and comparing the biometric data can help the research to evaluate human behaviour and their emotions according to situational maturity (Mantello et al. 2023). It can be depicted based on the illustration that an adequate focus on data storage can also evaluate the comparison between previously documented emotions and present emotions to identify the emotional changes and their balance.
More to a similar context, effective implementation of the critical evaluation from the insights outlined in the secondary resources would be taken for a better research outcome. Thus, analysis from the different aspects like financial impact, social impact and others has analysed for a clear and non-bias outcome. Hence, it can assist in identifying the way for improving biometric AI analysis for future development.
Biometric emotional analytics is considered one of the most important attributes that can help in identifying consistent and unconscious changes in human traits. Additionally, analysing the body parameters helps in evaluating Complex features for adequate monitoring. The fundamental features of this biometric emotional Analytics are to evaluate body pose, walking development, facial expression and others (Christopoulos et al. 2021). In a different context, sections like body weight, normal activity, heart rate responses and others play a crucial role in analysing the emotional situation of a human being. Moreover, kinematic and kinetic information also helps in identifying the body segments that can help to evaluate the motor activities of common people with the help of biometric emotional Analytics.
Contrariety emotional artificial intelligence intelligent is not able to evaluate the overall feel of a human being. It has been reported that the hiring system is mainly using this biometric analytical intelligence along with customer evaluation (Mantello et al. 2023). In that case, voice patterns and facial expressions are evaluated of the candidates during the interview period which may not analyse the whole emotional attributes of applicants. In that scenario, it makes create internal conflict. Additionally, antisocial activities also can be taken place with the help of biometric emotional analytics that can affect the overall societal growth.
Christopoulos, A., Mystakidis, S., Pellas, N. and Laakso, M.J., 2021. Arlean: An augmented reality learning analytics ethical framework. Computers, 10(8), p.92.
Kaklauskas, A., Abraham, A., Dzemyda, G., Raslanas, S., Seniut, M., Ubarte, I., Kurasova, O., Binkyte-Veliene, A. and Cerkauskas, J., 2020. Emotional, affective and biometrical states analytics of a built environment. Engineering Applications of Artificial Intelligence, 91, p.103621.
Mantello, P., Ho, M.T., Nguyen, M.H. and Vuong, Q.H., 2023. Bosses without a heart: socio-demographic and cross-cultural determinants of attitude toward Emotional AI in the workplace. AI & society, 38(1), pp.97-119.