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A machine learning based ‘wearable depression, anxiety and somatisation monitoring IOT system (WDASMS)’ for the prevention of suicides
- 1 Department of Computer Science and Engineering, Periyar Maniammai Institute of Science & Technology, Thanjavur, Tamilnadu, India.
- 2 Department of Computer Science and Engineering, Periyar Maniammai Institute of Science & Technology, Thanjavur, Tamilnadu, India.
- 3 Department of Computer Science and Engineering, Periyar Maniammai Institute of Science & Technology, Thanjavur, Tamilnadu, India.
- 4 Department of Computer Science and Engineering, Periyar Maniammai Institute of Science & Technology, Thanjavur, Tamilnadu, India.
* Author to whom correspondence should be addressed.
Abstract
Tamil Nadu reportedly is the alternate loftiest rates of pupil self-murder in India. According to report from the National Crime Records Bureau (NCRB) for 2020, about 46 people failed each day by self-murder in the state; further than two of these victims each day were scholars. The government of India’s domestic seminaries for meritorious pastoral children have reportedly witnessed 49 self-murders in just five times and half of the children who killed themselves on the premises were Dalits and Adivasis, Educationalists point at the pressure mounted on scholars for marks and competitive examinations as one of the main reasons. Utmost seminaries have councillors but going to the council has a smirch about it still. Domestic seminaries where these cases are passing more need to be covered more. Children dying by self-murder due to academic pressure are unheard of in other countries. Utmost of the A huge number of enquiries revealed depression and anxiety to be implicit risk factors for self-murder. Heart health is good among students between the ages of 15 and 25. A few physiological parameters are altered by anxiety and depression. Hence, current pulsation is the symptom for psychiatric diseases. Clinical trials have demonstrated that mental reasons, most frequently anxiety, depression, and somatoform illnesses, are present in cases with pulsation. Psychiatric problems like anxiety and somatization are linked to palpitation. A study found that individuals with high levels of both depression and anxiety are 54.77 times more likely to commit suicide, which is a significant increase above individuals with either high levels of anxiety or depression (2.46). (26.32).. Naturally students studying +2 and preparing for high marks are in under depression and it's dragged for two times. When the examinations are nearing and the results are nearing, the combined depression and anxiety factor increases the threat for committing self-murders for84.5. Hence, a system with literacy and decision making medium is needed to constantly monitor the students to take prompt conduct. A wearable IoT device transmits with scholars Id, the pulsation position constantly to the fellow knot. Logistic Retrogression machine literacy algorithm is used for decision making grounded on the probability of circumstance. The authorities are informed in their widgets and prompt conduct can be taken. This system will be more applicable to the boarding seminaries, where the children are far down from their parents and the number of self-murders are high particularly among +2 grade. This system can be used for colourful communities, where the working culture gives depression and anxiety.
Keywords
self murder, palpitation, depression, anxiety, IOT, machine learning
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Cite this article
Narayanan,A.E.;Vijayadharshini,D.;Ruthrabala,P.;Ajay,A.S. (2023). A machine learning based ‘wearable depression, anxiety and somatisation monitoring IOT system (WDASMS)’ for the prevention of suicides. Applied and Computational Engineering,20,19-25.
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