Preconference Workshop

Click here to register as a delegate for conference


Topic: Role of IEEE Standards for High-Quality Healthcare Delivery at Affordable Cost

Experts: Industry experts


In the current world, the cost for medical services, pharmaceuticals, products and devices, relative to benchmarks are too high. Enabling safe and affordable high-quality health care delivery is essential in the current generation.  Therefore, there is a need for lowering costs and improving outcomes. IEEE Standards provide the interconnection and interoperation of medical devices with computerized healthcare information systems in a way that is suitable for a clinical environment at affordable cost. This family of standards provides real-time plug-and-play interoperability and facilitates the efficient exchange of vital signs and medical device data acquired at the point-of-care. In this workshop participants will gain the insights into different standards set by IEEE to avail the quality health care at affordable cost.


Topic: Artificial Intelligence & Deep Learning Models in Medicine & Biology using Python

Experts: Mr. Mahesh Anand S, Scientific solutions computing


Machine learning refers to a vast set of tools for understanding data. These tools can be classified as ‘Supervised’ & ‘Unsupervised’. Supervised Machine learning involves building a statistical model for predicting or estimating, an output based on one or more inputs. With unsupervised statistical learning, there are inputs but no supervising output; nevertheless, we can learn relationships and structure from such data.

Deep Learning is a subfield of Machine Learning concerned with algorithms inspired by the structure and function of the brain called Artificial Neural Networks. A property of deep learning is that the performance of this type of model improves by training them with more examples by increasing their depth or representational capacity. In addition to scalability, another often cited benefit of deep learning models is their ability to perform automatic feature extraction from raw data, also called feature learning.

Biology and medicine are rapidly becoming data-intensive. A recent comparison of genomics with social media, online videos and other data-intensive disciplines suggests that genomics alone will equal or surpass other fields in data generation and analysis within the next decade. The volume and complexity of these data present new opportunities, but also pose new challenges. Automated algorithms that extract meaningful patterns could lead to actionable knowledge and change how we develop treatments, categorize patients or study diseases, all within privacy-critical environments.

The objective of this hands-on workshop is to emphasize a broad range of techniques for Deep Learning using Open Source Python software. The objective of this training program is to emphasize a broad range of techniques for Machine Learning and Deep Learning using Open Source Python software for the applications in Healthcare, Medicine, and Biology.


Topic: Virtual Instrumentation in Healthcare

Experts: Mr.Manimaran, VI Solutions,


A virtual instrumentation consists of an industry-standard computer or workstation equipped with
powerful application software, cost-effective hardware such as plug-in boards, and driver
software, which together perform the functions of traditional instruments. Also it represents a
fundamental shift from traditional hardware-centered instrumentation systems to software-
centered systems that exploit the computing power, productivity, display, and connectivity
capabilities of popular desktop computers and workstations. Although the PC and integrated
circuit technology have experienced significant advances in the last two decades, it is software
that truly provides the leverage to build on this powerful hardware foundation to create virtual
instruments, providing better ways to innovate and significantly reduce cost. With virtual
instruments, engineers and scientists build measurement and automation systems that suit their
needs exactly (user-defined) instead of being limited by traditional fixed-function instruments

The deliberations on this FDP include the following areas:

  1. To perform the data acquisition using NI Hardware interface
  2. To process the data(Signal/Image) with NI Bio-Medical Toolkit ,Vision Module & Data Logging for Offline Analysis
  3. To perform Machine Learning
  4. To develop a GUI Application


Topic: Workshop on Medical Technologies

Experts: Mr. Dinesh Bindiganavale, Mr. Prakash SonwalkarPRADIN TECHNOLOGIES PVT LTD.,

What is the workshop’s main goal?
To build awareness among talented students towards industry expectations in building a career in the medical technology domain
Who is the intended audience?
Undergraduate and postgraduate students pursuing a full course or electives in biomedical engineering, medical electronics, instrumentation technology /engineering.
What form will this workshop take?
The workshop is spread over 2-sessions. The morning sessions will be interactive presentations followed in the afternoon by lectures and demonstrations of select devices.

Morning Session

  1. Introduction to Biological Signal Monitoring and Vital Signs
  2. Design Process in Medical Device Industry

Afternoon Session

  1. Introduction to Fetal Monitoring
  2. Demonstration and case study of our Fetal monitoring device and Simulator

Topic: Medical IoT

Experts: Mr. Rohith and Team, DigiToad Technologies,

The session would focus on understanding the concepts of connecting sensors to the  internet and visualize the data on cloud application. It would also familiarize the participants with the challenges in IoT design. The  session would also include a demonstration on “IoT based Pulse Oximetry Application “. As an outcome of the session, the participants would learn about the IoT Gateway and Sensor Node.

The session features various design challenges and criteria for selecting a specific processor based on application requirements, understanding the architecture of advanced IoT MCUs, learn by experience technique and interfacing peripherals such as sensors.

Key Learning includes:

  • Architecture of advanced IoT MCUs
  • Design goals and selecting a specific MCU
  • Integration of software design tools and hardware
  • IoT sensor Integration Labs
  • Project Demonstration

Topic: 3D printing

Experts: Mr. Jigyans Kranti Mohapatra, Mr. Adway Kanhere  RIT, Bangalore

3D Printing is a process for making a physical object from a three-dimensional digital model, typically by laying down many successive thin layers of a material. It brings a digital object (its CAD representation) into its physical form by adding layer by layer of materials. In simple words its is the process of converting a digital file to a real-world model. 2017 was the year of 3d printing. Last year saw a major rise in popularity of the technology. Many desktop printers were launched that made the technology accessible to the masses. Major industries have turned their attention towards this promising technology. Many major companies like HP, have opened their additive manufacturing section. GE has an entire section dedicated for additive manufacturing (3D Printing). The applications are many, of which a few are listed below.

  • Medical: it is revolutionising the industry in many ways, from hip joint replacement to helping a blind woman feel her baby’s ultrasound.
  • Aeronautics: it assists in development and manufacture of parts that would be difficult to make using conventional manufacturing process. Turbine blades with designs that were one a challenge to make are significantly easier to manufacture.
  • Automotive: Recently completely 3D Printed cars and bike were manufactured. The complex construction possibility granted by 3D Printing enabled the designers to push the boundaries of what can be created.
  • Research and Development: it enhances the speed of development of a product.