Faculty Publications
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Item Mobile health system framework in India(Association for Computing Machinery, 2019) Pai, R.R.; Alathur, S.The healthcare system in India has been progressive with the specific health policies evolved over a period of time. Currently, it is in a phase of incorporating mobile technology into healthcare service delivery (i.e., mobile health) to make it a patient-centric model for managing acute, chronic, and preventive health conditions. This provides authentic interactions between the patient/user and the healthcare professionals for seeking health communication and information during emergencies and disaster conditions. However, there exists a great challenge in integrating mobile health solutions within the existing system and acceptance among the individuals, as the patient data is primarily collected through sensors and while the interventions are connected electronically. With this aim, the present research by using qualitative interviews (among residents and technology entrepreneurs) attempts to propose a provenance framework for the mobile health system in India. It also highlights its strengths and weakness and delineates components identified from qualitative interviews for describing the proposed provenance framework. © 2019 Association for Computing Machinery.Item COVID-19 detection from spectral features on the DiCOVA dataset(International Speech Communication Association, 2021) Ritwik, K.V.S.; Kalluri, S.B.; Vijayasenan, D.In this paper we investigate the cues of COVID-19 on sustained phonation of Vowel-/i/, deep breathing and number counting data of the DiCOVA dataset. We use an ensemble of classifiers trained on different features, namely, super-vectors, formants, harmonics and MFCC features. We fit a two-class Weighted SVM classifier to separate the COVID-19 audio from Non-COVID-19 audio. Weighted penalties help mitigate the challenge of class imbalance in the dataset. The results are reported on the stationary (breathing, Vowel-/i/) and nonstationary( counting data) data using individual and combination of features on each type of utterance. We find that the Formant information plays a crucial role in classification. The proposed system resulted in an AUC score of 0.734 for cross validation, and 0.717 for evaluation dataset. © © 2021 ISCA.Item Human Activity Recognition in Smart Home using Deep Learning Techniques(Institute of Electrical and Electronics Engineers Inc., 2021) Kolkar, R.; Geetha, V.To understand the human activities and anticipate his intentions Human Activity Recognition(HAR) research is rapidly developing in tandem with the widespread availability of sensors. Various applications like elderly care and health monitoring systems in smart homes use smartphones and wearable devices. This paper proposes an effective HAR framework that uses deep learning methodology like Convolution Neural Networks(CNN), variations of LSTM(Long Short term Memory) and Gated Recurrent Units(GRU) Networks to recognize the activities based on smartphone sensors. The hybrid use of CNN-LSTM eliminates the handcrafted feature engineering and uses spatial and temporal data deep. The experiments are carried on UCI HAR and WISDM data sets, and the comparison results are obtained. The result shows a better 96.83 % and 98.00% for the UCI-HAR and WISDM datasets, respectively. © 2021 IEEE.Item IoT-based Human Activity Recognition Models based on CNN, LSTM and GRU(Institute of Electrical and Electronics Engineers Inc., 2022) Kolkar, R.; Singh Tomar, R.P.; Vasantha, G.Smartphones' ability to generate data with their inbuilt sensors has made them used for Human Activity Recognition. The work highlights the importance of Human Activity Recognition (HAR) systems capable of sensing human activities like the inertial motion of a human body. The sensors are worn on a body part and tracked from whole-body motions and monitoring. Real-time signal processing is used to sense human body movements using wearable sensors. The work aims to provide opportunities for promising health applications using IoT. There are many challenges to recognising human activities, including accuracy. This work analyses Human Activity recognition concerning CNN, LSTM, and GRU deep learning models to improve the accuracy of the human activity recognition in the UCI-HAR and WISDM datasets. The comparative analysis shows promising results for Human activity recognition. © 2022 IEEE.Item Articulated Robotic Arm for Feeding(Springer Science and Business Media Deutschland GmbH, 2023) Nair, A.; Rajendran, D.; Jacob, J.C.; Varghese, N.S.; Suvin, P.S.In today’s fast-paced world, disabled people are a large minority group, starved of services, mostly ignored by society, and live in isolation, segregation, poverty, charity and even pity. There are numerous forms of disabilities. The disability suffered by most persons includes mental disability, emotional, physical and cognitive. Perhaps the most overlooked effect of a disability that affects the motor functions of the limb is the reliance on other people for the completion of even simple tasks that ordinary people perform on a daily basis, like taking a shower, dressing up, brushing teeth, or even having a meal. This chips away at the self-worth of a disabled person and gnaws away at their confidence. Through our project, we aim to provide a solution to those with compromised motor functions. This project aims to develop a 4 DOF robotic manipulator that is able to map the facial structure of the user, and with a feeding device (spoon/fork) attached to its arm transfers adequate portion of food accurately into the user’s mouth without spillage through smooth motion, by incorporating Image Processing, Manipulator Kinematics and Machine Learning. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.Item Emergence, Evolution, and Applications of Medical Cyber-Physical Systems(Springer Science and Business Media Deutschland GmbH, 2024) Rathore, R.; Bhowmik, B.The speedy advancement of technology has given rise to a new era of engineered systems called cyber-physical systems (CPS), which are redefining lifestyles worldwide through computer, networking, and control technologies. From health care to transportation, CPS has transformed several industries. By bringing about significant improvements in patient care, diagnosis, and treatment, Medical Cyber-Physical Systems (MCPS) transform the healthcare industry. Exploration of the area is, therefore, imperative. The paper discusses the trajectory of MPCS. It covers the development, necessity, and significance of MCPS. Next, we examine the various uses of MCPS in contemporary life. We then look at many issues and current developments in the medical organization. It makes it easier for patients, medical professionals, and equipment to share information effectively, which allows for prompt decision-making and preventative measures. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.Item Enhancing Healthcare AI with Cross-Silo Personalized Federated Learning on Naturally Split Heterogeneous Data(Institute of Electrical and Electronics Engineers Inc., 2024) Mukeshbhai, A.N.; Annappa, B.; Sachin, D.N.The potential of Artificial Intelligence (AI) in health-care is unavoidable. However, its success depends on the availabil-ity of large, high-quality datasets. Because of data heterogeneity across institutions and privacy concerns, traditional centralized Machine Learning (ML) approaches often face difficulties in this field. Federated Learning (FL) allows collaborative model training without requiring the transfer of sensitive patient data from the original institution. Recent research in FL within the healthcare domain has predominantly relied on centralized datasets, which do not represent real-time data heterogeneity and made assumptions by random data splitting to different medical client institutions. Additionally, it may be challenging for a single global model to encompass the diverse characteristics of various healthcare settings accurately. This paper examines the application of Personalized Federated Learning (PFL) in realistic cross-silo healthcare scenarios with federated natural split datasets in different medical client institutions. This paper discusses the experiments conducted on brain segmentation, survival prediction, melanoma classification, and heart disease di-agnosis. Our experiments show that the proposed PFL techniques consistently improve local model performance over standard FL strategies by up to 10% in different medical use cases. © 2024 IEEE.Item Utilizing Machine Learning for Lung Disease Diagnosis(Institute of Electrical and Electronics Engineers Inc., 2024) Markose, G.C.; Sitaraman, S.R.; Kumar, S.V.; Patel, V.; Mohammed, R.J.; Vaghela, C.For lung issues to be really treated and made due, early location and analysis are fundamental. In healthcare, machine learning (ML) strategies have arisen as an expected innovation with quick development, particularly in the field of clinical diagnostics. To analyze lung diseases, this research investigates the utilization of machine learning calculations. It centers around picture examination, patient information understanding, and the reconciliation of numerous information hotspots for an intensive investigation. This research's principal objective is to explore the chance of utilizing machine learning calculations to foresee and analyze a scope of lung conditions, including lung malignant growth, bronchitis, asthma, sensitivities, and persistent obstructive pneumonic disease (COPD). Proactive mediation depends on expecting the probability of lung issues before they manifest. Utilizing an assortment of machine learning techniques for classification and expectation, the examination assembled a heterogeneous dataset fully intent on laying the preparation for protection healthcare measures. © 2024 IEEE.Item A review of mobile health applications and its use phases(Inderscience Publishers, 2021) Pai, R.R.; Alathur, S.The use of mobile and wireless devices in healthcare plays a vital role in transforming health service delivery for patients and physicians across the globe. These devices are mainly driven because of rising healthcare costs and demand from the patients for diagnosing, treatment and care. Initiatives from the government and private companies have already started for developing mobile health interventions in the low resource setting areas. Yet, there has been less adequate reviews about mobile health applications across patients' use-phases. This paper provides a comprehensive review of mobile health applications across patients' use-phases. It reviews the most significant research articles and proposes variables that can be used for testing its significance among the people of low- and medium-income countries. It also emphasises the challenges involved in the successful deployment of mobile health applications which help in identifying the current state of research thereby establishing an agenda for future research direction. © © 2021 Inderscience Enterprises Ltd.Item A Multimodal Contrastive Federated Learning for Digital Healthcare(Springer, 2023) Sachin, D.N.; Annappa, B.; Ambesenge, S.; Tony, A.E.Digital healthcare applications have gained enormous global interest due to the rapid development of the internet of medical things (IoMT), which helps access massive amounts of multimodal healthcare data. Using this rich multimodal data without violating user privacy becomes crucial. Federated learning (FL) isolates data and protects user privacy. Clients collaboratively learn global models without data transmission. Most of the current FL approaches still depend on single-modal data. It is known that multimodal data always benefit from the complementarity of different modalities. This paper proposes a multimodal contrastive federated learning framework for digital healthcare. The proposed framework solves the multimodal federated learning problem. The proposed architecture used a geometric multimodal contrastive representation learning method to learn representations of multiple modalities in a shared, high-dimensional space. This helps optimize the representations to capture the inter-modal relationships better and improves the multimodal model’s overall performance. Experiments show that the proposed framework performs better than conventional single-modality FL and multimodal FL framework approaches. Given its generality and extensibility, the proposed framework can be used for many downstream tasks in healthcare applications. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.