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Sentilyser: Embedding Voice Markers in Homeopathy Treatments
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Author(s): Arunima Mookherjee (Symbiosis International University (Deemed), India), Preeti Mulay (Symbiosis International University (Deemed), India), Rahul Joshi (Symbiosis International University (Deemed), India), Pranav Sunil Prajapati (Symbiosis International University (Deemed), India), Sonali Johari (Symbiosis International University (Deemed), India)and Swati Sunil Prajapati (Independent Researcher, India)
Copyright: 2019
Pages: 26
Source title:
Interdisciplinary Approaches to Information Systems and Software Engineering
Source Author(s)/Editor(s): Alok Bhushan Mukherjee (North-Eastern Hill University Shillong, India)and Akhouri Pramod Krishna (Birla Institute of Technology Mesra, India)
DOI: 10.4018/978-1-5225-7784-3.ch008
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Abstract
Cognitive and somatic health is predominantly congruous. Happiness creates a propitious impact on a person's wellbeing. Likewise, stress can be detrimental to a person's overall health. Nevertheless, it is perplexing to truly understand an emotional state of a patient for an unerring diagnosis. Considering the old adage, necessity is the mother of invention, there is a need for a sentimental and emotional analysis tool for the accurate identification of the mental state of the patient. Thus, Sentilyser was created. It is a clinical decision support system. Using the recorded voice of a person, the mood is analyzed using support vector regression. Arousal and valence of the audio file is calculated, and thus, the corresponding mood is predicted. The objective of this chapter is to propose the use of Sentilyser as a part of a patient's diagnosis, which will not only help the physician reach a better conclusion but will also help them in cases where the patients themselves are unaware about their own psychological state.
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