I am a Senior Lecturer. My responsibility is to conduct and support research teaching related to Embedded System Design, IoT, and Deep Learning courses. The research focus is on Health Informatics (Preventive healthcare, Ambient Assisted Living, and AI-based Decision Making ).
I did my graduate degree in Major: Electronics and Communications BIST, Bhopal, University, 2009. After securing my Master's Degree in Microelectronics and VLSI 2011, I joined as a lecturer at Rajiv Gandhi Technical University. I worked from June 2011 to Dec 2013 in Computer Science Department. I taught Embedded System, Digital Signal Processing, and Wireless Communication; moreover, I supervised Bachelor’s and Master’s students in the similar domains I taught on various projects. In Dec 2013, I moved to New Zealand to pursue a Doctoral Degree. My doctoral dissertation was conducted in Smart Home for Ambient Assisted Living under Prof. Subhas Chandra Mukhopadhyay and examined applying a relatively new wireless protocol for smart home monitoring design. During the endeavor of Ph.D. research, I have developed my skills in heterogeneous wearable and non-wearable sensors based on Wireless Network designing for home and big buildings. I have learned the skills of big data handling, developing data mining, and machine learning algorithms to extract decision-making information, followed by using the internet of things (IoT).
Before joining Linnaeus University, I worked at different research and teaching universities. From Oct 2018 to Oct 2020, I worked at Technical University of Denmark, Lyngby, Denmark, and TUM Munich, as a Marie Curie Fellow. I was a Eurotech Marie Skłodowska-Curie fellow at Innovation Dept., Technical University of Denmark, Denmark and Building Realization and Robotics, Architecture Department, Technical University of Munich. My responsibility was to conduct and support research teaching related to Health Computer Interaction. I was investigating and developing the ecosystem for the elderly through Ambient Assisted Living. I have addressed the challenges of digital and audio signal processing of daily living activities, considering the privacy aspects, recognizing the behavioral pattern and anomaly, electronic health record management, and analysis.
1DV701 : Bachelor’s level, Computer Networks - an introduction, 7.5 credits
1DV702: Bachelor’s level, Computer Networks - administration, 7.5 credits
1DT302: Bachelor’s level, Embedded Systems - an introduction, 7.5 credits
2DT304: Bachelor’s level, Project with Embedded System, 7.5 credits
2DV702: Bachelor’s level, Internet Security, 7.5 credits
2DV703: Bachelor’s level, Wireless Security, 7.5 credits
2DT303: Bachelor’s level, Reliability in Embedded System, 7.5 credits
Digital Enhanced Living
Behavioral pattern generation and Anomaly Detection
Image processing for the healthcare
Assistive Technology for wellness
Intelligent Non-Speech Audio Assistance
Neonatal Preventive Healthcare Monitoring
HL7 Version 2, Clinical Document Architecture (CDA) & Fast Health Interoperability Resources (FHIR)
Article in journal (Refereed)
Ghayvat, H., Awais, M., Bashir, A.K., Pandya, S., Zuhair, M., et al. (2022). AI-enabled radiologist in the loop : novel AI-based framework to augment radiologist performance for COVID-19 chest CT medical image annotation and classification from pneumonia. Neural Computing & Applications.
Status: Epub ahead of print
Patel, C., Bhatt, D., Sharma, U., Patel, R., Pandya, S., et al. (2022). DBGC : Dimension-Based Generic Convolution Block for Object Recognition. Sensors. 22 (5).
Pandya, S., Ghayvat, H. (2021). Ambient acoustic event assistive framework for identification, detection, and recognition of unknown acoustic events of a residence. Advanced Engineering Informatics. 47.
Ghayvat, H., Gope, P. (2021). Smart aging monitoring and early dementia recognition (SAMEDR) : uncovering the hidden wellness parameter for preventive well-being monitoring to categorize cognitive impairment and dementia in community-dwelling elderly subjects through AI. Neural Computing & Applications.
Status: Epub ahead of print
Patel, C.I., Labana, D., Pandya, S., Modi, K., Ghayvat, H., et al. (2020). Histogram of Oriented Gradient-Based Fusion of Features for Human Action Recognition in Action Video Sequences. Sensors. 20 (24). 1-32.
Article, review/survey (Refereed)
Bhatt, D., Patel, C., Talsania, H., Patel, J., Vaghela, R., et al. (2021). CNN Variants for Computer Vision : History, Architecture, Application, Challenges and Future Scope. Electronics. MDPI. 10 (20).