Introduction:

The absence of accurate clinical decision support systems(CDSS), poor methodologies for diagnosis at primary health care centres has lead to under diagnosis of various diseases. Though Machine Learning(ML) is attracting widespread attention due to its huge potential and promising results, yet there has been very lean research in ML based medical diagnosis. Knowing how to take accurate decision is an important skill, when machine exhibit this we say they are intelligent.
Geriatric care is a study of identifying the movement-related problems in elders by quantification and avoid falls by interpretation. Current solutions focus on robotic environment, specialised sensors, tracking systems, and smart devices yet there is no unified general architecture as how to deploy these for the best use.

People:

Mohammed Nisar Baig , Intern, UG student, 2014- 2018, CDSS for COPD

Pranaya Y C , Intern, UG student, 2014- 2018, CDSS for COPD

Manne Naga Himarish , Intern, UG student, 2014- 2018, CDSS for COPD

Sathish Kumar E , Intern, UG student, 2014- 2018, Geriatric care

Sachin P , Intern, UG student, 2014- 2018, Geriatric care

Vignesh B P , Intern, UG student, 2014- 2018, Geriatric care

Publications:

E. S. Kumar, P. Sachin, B. P. Vignesh and M. R. Ahmed, "Architecture for IOT based geriatric care fall detection and prevention," 2017 International Conference on Intelligent Computing and Control Systems (ICICCS), Madurai, 2017, pp. 1099-1104. doi: 10.1109/ICCONS.2017.8250636

Patent:

Intelligent spirometer

Projects:

Machine Learning based spirometer