Kalyani Bhole

Savitribai Phule Pune University

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Pritesh Shah

Symbiosis International University

Trupti Gujarathi

Savitribai Phule Pune University

Prajakta Mhatre

Bharati Vidyapeeth Deemed University

Prateek Singh

National Center for Disease Control

Ravi Sekhar

Symbiosis International University

Sanket Mahore

Savitribai Phule Pune University

Dayaram Sonawane

Sandip Foundation

Najuka Jagtap

Savitribai Phule Pune University

Vinita Yerande

Savitribai Phule Pune University

Shashikant Rathod

Datta Meghe Institute of Medical Sciences

Cooperative Institutions

Savitribai Phule Pune University

Siddhartha Medical College

Symbiosis International University

Arunai Engineering College

University of New Brunswick

Chaitanya Bharathi Institute of Technology

MIT World Peace University

Shivaji University

Institute of Medical Sciences

Indian Institute of Technology Roorkee

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GAIT ANALYSIS USING IMU SENSOR

2022 13th International Conference on Computing Communication and Networking Technologies (ICCCNT) (2019)

Trupti Gujarathi Kalyani Bhole

Inertial Measurement Unit (IMU) based systems that employ gyroscopes has a growing interest in gait analysis. We described the IMU-based gait analysis method that uses angles obtained from an accelerometer and gyroscope sensor within MPU6050 to identify phases of each gait cycle during walking. The gait analysis algorithm is composed of Heel strike and Toe off factors detection observed by means of detection of initial contact(IC) and terminal contact(TC) so that gait parameters are calculated from those gait details. Most of the present gait phase detection strategies use multiple sensor modules connected to every segment of lower body. We have used two MPU6050 sensors placed on a shank of both legs to collect gait signals. For experimental purpose, each participant was asked to walk for a distance of 40 meters for the straight corridor at a normal speed. These gait signal data measured by using Arduino uno micro-controller is then transmitted to an android app `Blueterm' wirelessly via the HC-05 Bluetooth module. Collected data by the app is stored as a text file in a device containing app. This database is further processed to an algorithm that has been developed using MATLAB to extract a period of events that happened during walking such as stride time, stance time, step time, cadence, etc. The results are useful in identifying the biomechanical stability of patients post surgery. Basically, this paper presents a wearable IMU sensor-based system and its associated gait analysis algorithm to obtain quantitative measurements of the individual's gait parameters to monitor patient progress in orthopedics and rehabilitation.

Cadence

STRIDE

10.1109/icccnt45670.2019.8944545

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Citations (56)

Human Fracture Detection Using Machine Learning

International Journal for Research in Applied Science and Engineering Technology (2024)

Kalyani Bhole

Abstract: Accurate and timely detection of fractures is crucial for effective medical diagnosis and treatment planning. In this project, we propose a novel approach for human fracture detection by leveraging the YOLO (You Only Look Once) model, known for its efficient object detection capabilities. Our system aims to automatically identify and localize fractured areas within X-ray images by highlighting them with bounding boxes, facilitating prompt diagnosis. Utilizing deep learning techniques, especially the efficient object detection capabilities of YOLO, we enable rapid and precise localization of fractures, assisting radiologists and healthcare professionals in promptly diagnosing fractures. The proposed system offers a promising solution to enhance the efficiency and accuracy of fracture detection, ultimately improving patient care and treatment outcomes significantly.

10.22214/ijraset.2024.59613

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Generative Artificial Intelligence in Legal Drafting

Tali Basha Kalyani Bhole Sandeep Yelisetti

10.1109/iccigst60741.2024.10717541

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Smart wrist watch

Najuka Jagtap Jagannath Wadgaonkar Kalyani Bhole

The motivation of the projected work is to design and development of the smart wrist watch which indicates the oxygen saturation(SpO2), temperature of the body along with real time clock and calorie burn calculator also able to keep a record of all these parameters which can be stored in personal computer. This database can be useful to physician for health diagnosis. The system has very small size and low cost. Now a days, every person is suspicious about their health, so this watch gives the real time indications of above parameters. This paper presents a reliable, effectual system which has a great impact on human body. According to the indications of body parameters on watch, people can adjust the temperature of body by drinking the water. Also if the level of oxygen decreases in the blood, human can go to the highly oxygenated area. It can show the daily calorie burn in the body as per the exercise. This device is very useful for those who are working in busy schedule or those who are giving less time to their health. In this proposed work, a genuine effort has been completed to design and development of the smaller size smart wrist watch.

Calculator

Smartwatch

10.1109/sceecs.2016.7509273

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Citations (4)

Prediction of COVID-19 & Pneumonia using Machine Learning & Deep Learning Model

Prajakta Mhatre Kalyani Bhole

As a result of the COVID-19 pandemic, medical examinations (RTPCR, X-ray, CT-Scan, etc.) may be required to make a medical decision. COVID-19's SARS-CoV-2 virus infects and spreads in the lungs, which can be easily recognized by chest X-rays or CT scans. However, along with COVID-19 instances, cases of another respiratory ailment known as Pneumonia began to rise. As a result, clinicians are having difficulty distinguishing between COVID-19 and Pneumonia. So, more tests were required to identify the condition. After a few days, the COVID-19 SARS-CoV-2 virus multiplied in the lungs, causing pneumonia and COVID-19 named Novel Corona virus infected Pneumonia. We employ Machine Learning and Deep Learning models to predict diseases such as COVID-19 Positive, COVID-19 Negative, and Viral Pneumonia in this research. A dataset of data is used in a Machine Learning model. A dataset of 120 images was used in the Machine Learning model. By extracting eight statistical elements from an image texture, we calculated accuracy. Adaboost, Decision Tree & Naive Bayes have overall accuracy of 88.46%, 86.4% and 80%, respectively. When we compared the algorithms, Adaboost algorithm performs the best, with overall accuracy of 88.46%, sensitivity of 84.62%, specificity of 92.31%, F1-score of 88% and Kappa of 0.8277. VGG16 Architecture is used in CNN model for 838 images in Deep Learning model. The model's total accuracy is 99.17 %.

AdaBoost

Viral Pneumonia

10.1109/iconsip49665.2022.10007474

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PID Controller With Fuzzy Inference System As Simple Feedback For Closed Loop Control Of Intravenous Anesthesia

International Journal of Engineering and Advanced Technology (2019)

Kalyani Bhole

Anesthesia is an imperative activity during operation. Anesthesiologist perceives patient’s physiology and adjusts the drug rate. Sometimes, it may go overdose or under-dose which is proscribed. This process is merely in need of an expertise which in turn, may not be available every time and everywhere. But we can store this expertise with the help of fuzzy inference system and utilize it to judge the depth of anesthesia. A genuine effort has been made to design a PID controller with fuzzy inference system as simple feedback to control intravenous anesthetic drug delivery. This paper demonstratesthe design and implementation of experts’ based system with PID controller and comparison of the same with open loop target controlled infusion and fuzzy inference system without a controller. The conclusion of this study is that the PID controller with fuzzy inference system as simple feedback gives fast response with no overshoot. Ultimate prototype is implemented on Xilinx’s Spartan 3E Field Programmable Gate Array.

Overshoot (microwave communication)

Open-loop controller

10.35940/ijeat.a9859.109119

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Machine Learning Approach for Prediction of Aortic and Mitral Regurgitation based on Phonocardiogram Signal

2022 13th International Conference on Computing Communication and Networking Technologies (ICCCNT) (2021)

Pallavi J. Khade Pradip Mane Sanket Mahore Kalyani Bhole

Phonocardiogram is a recording of the heart sounds and murmurs. From the PCG signal, we can identify heart valve diseases like Mitral Stenosis(MS), Aortic Stenosis(AS), Mitral Regurgitation(MR), and Aortic Regurgitation(AR). The noise present in the PCG signal make diagnosis difficult. To overcome this problem, we used a machine-learning algorithm to predict and classify the PCG signal. For the classification of the HVDs, we extracted the features from the PCG signal and train the ML models with features. In this paper, we have to use the K-Nearest neighbors(KNN), AdaBoost, and Support Vector Machine(SVM) algorithm to classify HVDs like Aortic and Mitral Regurgitation. We train our model using features extracted from PCG signals like time and frequency domain features. For the performance evaluation of the ML models, we used evaluation metrics like accuracy, sensitivity, specificity, kappa, and AUC score. The average accuracy of KNN, AdaBoost, and SVM is 90.47%, 92.85% and 94.04%. SVM outperforms KNN and AdaBoost, where average accuracy is 94.04% with sensitivity 84.72%, specificity 95.30%, F1 score 85.16%, Kappa 0.82, and AUC score of 0.73.

Phonocardiogram

AdaBoost

10.1109/icccnt51525.2021.9579971

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Citations (4)

Cybernetic Technologies in Industry 4.0

2022 13th International Conference on Computing Communication and Networking Technologies (ICCCNT) (2023)

Bhavesh Raju Mudhivarthi Pritesh Shah Ravi Sekhar D Murugesan Kalyani Bhole

The presently ongoing industry 4.0 is by far the most rapidly transformational and widely trending revolution as compared to its previous three technological generations. The fourth generation of industry deals with cybernetics, which includes smart automation, interconnectivity, machine learning, real-time data acquisition and control. The various cybernetic technologies active in this industry 4.0 consist of 3D printing, bigdata, artificial Intelligence, cyberphysical systems and more. These technologies are the reason behind the successful implementation of the fourth industrial generation practices. Cybernetic technologies help in real time monitoring, analysing and controlling physical industrial machinery, devices and objects through data collected through smart sensors connected to the internet-network cloud. Cybernetic technologies have a lot of daily applications including smart health monitoring, smart factory, smart home appliances, and so on. This paper presents some of the important aspects of cybernetc technologies in industry 4.0, including smart sensors, asset utilisation, product development, supply chain, predictive maintenance and more.

Industry 4.0

Interconnectivity

Cyber-physical system

SCADA

10.1109/icccnt56998.2023.10307822

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STOCHASTIC BIOLOGICAL MODEL OF NEURON

International Journal of Engineering Applied Sciences and Technology (2022)

Prajakta Mhatre Kalyani Bhole

Brain is the vital organ for action, learning, memory, thought formation and other cognitive activities. The neuron is the fundamental functional unit of the brain. It is a specialized cell created to transmit information and receive information from other nerve cells, muscle cells or gland cells. This paper describes the stochastic behavior of biological model of Neuron and its implementation in MATLAB Simulink to study the behavior of a neuron in a real-time environment. The Hodgkin–Huxley mathematical model describes how initiated and propagated action potentials in neurons takes place are for external stimulus. The stochastic behavior of a Hodgkin-Huxley neuron is studied for various external stimulus and helped to find various action potentials.

Stimulus (psychology)

Hodgkin–Huxley model

Biological neuron model

Stochastic modelling

Mirror neuron

10.33564/ijeast.2022.v06i10.038

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Designing of dual-axis Solar tracking system with remote monitoring

Sangamesh Malge Kalyani Bhole Ravi Narkhede

The use of renewable energy resources like solar energy is the need of time in countries like India, where energy demand is increasing very rapidly with population. Solar panels are the transducers which convert solar energy into electrical energy. The conventional, i.e. fixed installation of these solar panels on a flat surface is very inefficient and less cost effective solution. This paper comes with an engineering solution to enhance efficiency of solar panel in a very cost effective way. This engineering solution is the dual axis solar tracking of solar panel. The dual axis Solar tracking system for solar panel is the most appropriate way to increase efficiency of solar panel by tracking the sun completely. The proposed system comprises of ARM processor BeagleBone Black - BBB which has Linux OS, Light dependent Resistors (LDRs), Servo motors. The internet facility provided by BBB takes this system to the next level by allowing users to monitor real time solar tracking of solar panels with total amount of electricity generated by these solar panels from anywhere in the world.

Solar tracker

Solar micro-inverter

10.1109/iic.2015.7150991

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Citations (10)