Good day, Dr. Pramod Kesavan Namboothiri. I'm Arjun. I've had a look through your background and your professional record. I'm interested in hearing more about your work and how you see yourself fitting in here with our faculty. Ready to start?

Please start by introducing your professional journey in academia.

So I am Doctor Pramod Keshavan Nampudi.

I did my PhD from IIT Bombay.

On the nanomedical nanoscience.

And my expertise in nano composites.

And the variables sensors.

So after my PhD, I joined as an assistant professor in Engineering college Kochi.

After almost six months of ex uh.

Working there and then then I joined MI Team Manipal.

Biomedical Department assistant professor.

So almost total 14 years. Almost.

Eight years of experience as a teaching and research faculty.

And my research.

Interest, as I said, in wearables sensors, nanocomposite, polar nanocomposites.

In this area I got several such grants from.

Uh, national granting agencies like DB, DST serve.

And I'm getting PhD students.

Thanks for sharing that!

Yes.

My expertise as I said it is in wearables sensors.

To start, I'll need some context. Briefly share your academic background and relevant experience. Specifically, highlight roles or projects connected to artificial intelligence, machine learning in healthcare, health informatics, or computer science.

And.

Uh, polymer nanocomposites.

What I did is that I collaborate with my colleagues for the AI part of it.

And my projects, whatever I got from a DSD and cert related to rehabilitation engineering and movement analysis for Parkinson's disease patients.

So all those things we make the sensors.

We'll get the data from the patients and normal subjects unless using UH.

Machine learning algorithms and.

Uh, provide the suggestions for the doctorate clinicians so.

I am not an expert in AI or ML as such.

Yes.

Could you describe how data from your sensor projects—like the gait analysis for Parkinson’s disease or post-stroke rehabilitation—was processed using machine learning or AI algorithms? Specifically, what role did you play in integrating these technologies?

See, my expertise is in nanocomposite preparation and sensor fabrications, and.

I was the PA of all these projects. What we did is that we made the census. We went to the.

Uh, we have collaboration with the Medical College and we will we take took this composites to the.

Medicare, College and.

Took data from the patients while they're moving, they're while they're doing their activities.

And we tried to categorize this.

Data. We are trying to understand this data using machine learning algorithms.

Uh, so like how this gate is happening, how the cadence is changing, how the uh, gate parameters for each?

Leg is changing how the patient is walking, like in for example in Parkinson's disease, the patients will be walking in a different speed, uh, different way when the Parkinson disease.

Progresses so how that is happening and we use machine learning algorithms to predict what is the stage of the.

Patient The stage of the Parkinson's disease.

And uh.

We use this.

Data again to understand how to.

Treat how how to tackle these issues and these wearable sensors can be taken to the taken to the taken to their home and like remotely we can the doctors can sense or occur the data, collect the data and analyze it.

Clarifying.

Yes.

Let's verify your ability to teach theory and laboratory courses. How do you approach teaching complex concepts like signal processing or biomaterials fabrication to ensure students build both theoretical understanding and practical competence?

Signal processing is not my area.

But when it comes to material science or boulder nano combo seats?

The strategy which I took is take is that explain the fundamentals and take examples from research articles.

On my materials.

And show them how these fundamental theories, whatever we study, applied in this.

Papers or research articles?

And to make them understand.

Create more.

Specialized assignments for the.

Student so that they can read.

These articles.

Or practical examples from the Internet and.

Provide reports.

Or do mini projects on that.

Thank you, Professor. Could you elaborate on how you ensure that students connect theoretical principles with real-world applications during these assignments or mini-projects?

For example, one article maybe talking about making multifunctional alum particles.

See these research articles.

For imaging biomedical imaging.

So it may be using some nanoparticles or it may be using a portal structure where it is adding dopants at core for luminescence and.

Other dopants at the shell on the shell for CT or Mr. imaging contrast agent. So they're using different things in this articles.

So students can understand these articles.

Discuss these articles as a group.

And reflect on where these things are used, these strategies are used, how it is developed, what are the pros and cons of all these things.

And provide us like not only report like presentation, sometimes classroom presentations etcetera can be possible.

Excellent approach, Professor. Utilizing group discussions and presentations not only enhances understanding but also fosters critical thinking among students. Let’s discuss your experience with student evaluation. Could you outline how you assess their performance, particularly in laboratory-based courses or research-driven assignments?

See a research driven assignments basically.

These assignments.

Can we kind of design questions?

All kind of mini projects if it is a the same kind of question.

The answer may be multiple.

Answer can be different for each student, like there are different options. So their own port process, how they are thinking, how they are applying their creativity, how they are applying the basic knowledge to answer.

The problem, whatever is given to them, that is what is analyzed.

That is what is checked and evaluated and given marks.

Not just one answer for one question like there will be multiple answers, multiple possibilities, even sometimes some answers, some points maybe may not be really correct, but a student maybe have a thought process behind that if that thought process is there.

Then we can view some Marks and we can evaluate them accordingly.

If student thought process direction is correct.

That’s a thoughtful approach, Professor. Evaluating students on their reasoning and creative applications reflects an understanding of varied learning paths. Transitioning now, could you discuss how you guide student projects and research, especially in interdisciplinary areas such as nanotechnology and healthcare?

Student projects. Basically UG projects when we get them.

They have either they need to come to us before 6 semester or by 6 semester.

And they had to start working in the lab along with our PhDs and PD students.

Because the last semester if they start working.

When the understanding the problem literature review and will take some time.

And if they're coming in the last semester?

Then we'll be giving a problem which is already defamed and they have to execute that.

Problem basically that to search activity. So in my laboratory we have to search students, search Coates.

Below that we have PG students and then we be taking UG students under them.

So for unique parents it is like that and PG's were a little bit applied like little bit higher side they have one year time.

So they can have more literature review and they can have a more solid, they can conduct more solid business work. And these works usually I will conduct this is a continuation from the previous semester to current semester, previous batch to current batch.

So it is. It's a continuous process.

Thank you, Professor. It seems you’ve created a structured mentorship hierarchy in your lab, allowing undergraduate and postgraduate students to progressively contribute to ongoing research. This fosters growth and continuity in projects. Let’s now verify your communication skills and teaching approach. How do you ensure your lectures or lab sessions remain clear and accessible to students with varied levels of understanding?

In my classes.

I will start from the basics.

And I will encourage students to ask questions.

And it is not like I am standing on a podium and talking. I'll be going to to the students. I will be walking around the students and talking to them.

And interacting with each group of students, maybe backbenchers or students sitting in the front.

Asking questions and.

Provoking them to ask questions.

Along with that.

I used to share.

My teaching materials.

To them.

Which is creator before the class because like a teams group with my old students.

Using maybe Teams group?

So I'll be sharing the things to them.

And so that they can go back and review the.

Presentation, presentation details and class whatever I took in the class.

Your interactive teaching method, combined with accessible materials, ensures both engagement and understanding across diverse student groups. Let us verify your credentials. You have a PhD in nanoscience from IIT Bombay. Can you share specific aspects of your doctoral research that align with your expertise in wearable sensors or healthcare applications?

So my PhD work.

Please continue.

Yeah, my PhD work I have worked on UH.

Nanoparticles, luminous and nanoparticle which is which are embedded in polymer metrics like basically nanocomposites.

It is actually a part of a DRDO project defense project.

So we were preparing an anacomposite, preparing anacomposites for defense that application that those nanoparticles where functionalized oleic acid or fatty acids and then we embedded that in the nano polymer matrix.

Which are luminescent and near infrared.

And we studied how these particles are getting stabilized.

Polymer metrics, but how the interface is?

Your doctoral research demonstrates a strong foundation in nanocomposites and functional nanoparticle applications, which clearly ties into your current expertise in wearable sensors and interdisciplinary healthcare projects. Now, let's discuss your publication track record. Could you highlight a few of your research papers that you believe best showcase your contributions to AI signal processing or wearable healthcare technologies?

A signal processing whatever publications coming in my.

So my.

My.

My list is actually a collaborative list. It is in all this.

AAA papers. My major work is in wearable sensors where there are two major works are there?

1 is about the porous nanocomposites, Cryogel based nanocomposites, how it is used for sensing the movement.

Another is using Polygon nanocomposites.

Uh, where carbon additives are.

Used as fillers using for movement analysis. So these two major publications are there in their direction along with almost, uh.

8 to 10 patents in polio nanocomposite based the wearable sensors filed and remaining other patents.

On biomaterials, total of almost 15 patents.

5.

Your work on wearable sensors and nanocomposites, combined with an impressive portfolio of patents, demonstrates significant contributions to the field. Let’s now verify the final skill: your experience with industry projects or consultancy. Could you share details of any collaborations or consultancy work with healthcare organizations or industries that utilize wearable technologies?

I haven't started any industry collaboration as such.

But we are in discussion with.

Some startups.

And some some basic.

Some companies where we can use this technologies.

But not started till now as a collaboration.

Thank you, Professor. Though direct industry collaborations haven't been initiated yet, it's promising that you're actively engaging with startups and companies for potential applications of your wearable sensor technologies. That concludes the verification phase of your technical skill set. Let us proceed silently.

Professor, could you share which introductory course within your field you find most fulfilling to teach and explain why?

One of the course may be material characterization techniques.

For healthcare students.

And another maybe introduction to biomedical nanotechnology.

Ah, interesting

Make student understand where this characteristic techniques are used like scanning electron microscope, atomic force microscopy.

And these two codes one the first 21.

Or spectrophotometer, spectrofluorometer, etcetera.

And the second one, very nanotechnology is used how? What are the applications of nanotechnology or nanomaterials in different healthcare situations or domains?

Understood, Professor. The focus on material characterization techniques, such as scanning electron microscopy and atomic force microscopy, provides fundamental hands-on knowledge for students to understand how to analyze and interpret materials on both micro- and nanoscale levels. This approach is essential for healthcare students, as it bridges concepts with tangible applications in diagnostics and therapeutic design. Similarly, introducing biomedical nanotechnology demonstrates both an interdisciplinary approach and practical relevance by guiding students through nanomaterial applications in healthcare—from targeted drug delivery systems to biomimetic devices. It’s a compelling way to highlight the transformative potential of research in advancing medical treatments and technologies. Your emphasis on applicability and domain-specific examples ensures student engagement and understanding. Thank you for sharing.

Dr. Namboothiri, let’s explore how your academic research work connects to your teaching capabilities. Your work on **wearable nanocomposite sensors for post-stroke rehabilitation** is intriguing. Could you walk me through the core challenges tackled in this research and explain how you would simplify its concepts for students in a classroom setting?

See post code.

Yeah, tell me.

Could you elaborate on the core challenges addressed in your research concerning wearable nanocomposite sensors for post-stroke rehabilitation? Additionally, how would you translate such advanced concepts into engaging and understandable material for students?

Students, what's the stroke? What What is the this? What is this stroke?

So how this stock questions how they are getting affected by stroke, their movements are getting affected, they cannot move, they are paralyzed.

And then we can say that when they are parallelized after that, when they are recovering, when that rehabilitation phase.

What doctorates usually do.

And they have to train their muscles, their movements, their hands or their legs to the previous.

Condition.

Bring back them to the previous healthy condition so they had to train their body parts for different kind of movement. Exercise will be given to that.

So then their bodyguards are moving.

So how currently the evaluation happens?

Now doctors are there, they are watching their movements, they're trying to understand how they're moving the hand or leg. They'll be giving an exercise. They're seeing and understanding it OK, with some difficulty to dispatch degree. The hand is moving.

But there is no quantitative method.

Generally used and these things can be done always in a hospital setup with the presence of a doctor.

But then comes the importance of the sensor.

So sensor should measure the movement.

The when sensor changes when we attach the sensor to the part where the movement happens.

How to measure the moment?

Then.

As movement happens, the sensor stretches or.

Changes its structure.

Then we need to convert that change to electrical quantity like voltage. How?

A sensor if it is changing the resistance we can use what I debater.

Provision's bridge and we can convert that into voltage quantity and give it to the device.

Data accessory system where it can actually process it and send to a laptop for processing for the processing for a mobile or send it to a cloud and send it to the clinical clinician side.

So this is how so each movement in such a way using a variable sensor we can measure.

How much it is movement is happening? How the progress of.

Uh, rehabilitation is happening for these talk patients. So all those things we can measure. So with these things we can.

Inform the students or.

Teach student in a simple way by showing them the sensor attached to their knee and asking them to move the hands.

So asking one student to come out and see show demonstrated.

Using a sensor device system.

That was a very detailed explanation, Dr. Namboothiri, showcasing the practical applications and significance of your research. I appreciate the thoughtfulness in how you would simplify such a complex topic for students, pairing theoretical understanding with hands-on demonstrations. To delve deeper, how do you ensure students grasp the interdisciplinary aspects involved—such as material science, signal processing, and biomechanics—when teaching this subject?

Yes, when we teach such subjects, we need to divide into different.

Domains.

Human body is moving, hand is moving, there are muscles which are moving and there are muscles which movement is getting affected.

See the sensor is there.

Already affected.

Strength.

Using a sensor, so sensor is just capturing that.

But when sensor is capturing that movement of the muscle or body part.

There are other things also getting getting caught, getting captured by the sensor, there are no issues.

50 Hertz noise from the power supply.

Or other movements, unwanted movements. The wires connected to the sensor can just move here and there and just create a noise.

So all those things.

Or when the sensor data is coming to the.

The microcontroller some data may be missing because we are transferring the sensor data wirelessly to a laptop.

So all those things we have to.

Use a signal processing methods like filtering.

Uh, compensating those data points with averaging method or some other methods where the data or signals are missed?

Etcetera. So signal processing, but also we need to explain, we can explain like that.

That makes sense, Dr. Namboothiri. Breaking it down into domains such as biomechanics, sensor functionality, and signal processing will undoubtedly help students understand the interdisciplinary nature of this work. Finally, in teaching this topic, how do you balance the theoretical grounding with the practical hands-on experience required for students to truly grasp the applications?

I'm not getting the.

Can you repeat please?

Certainly. How do you balance teaching theoretical concepts, like biomechanics and signal processing, with practical hands-on experiences, such as using sensors or analyzing real-world data, to ensure students fully understand the applications of your research?

Instead of just giving some signals.

And asking them to just filter or process it.

We can actually give a sensor to them, can ask them to actually the labs can be.

Coupled together materials lab, signal processing lab, et cetera, so that when a sensor is made, they can use that sensor to capture the data.

Then they'll get a feel that OK, they are making a sense, that we are making a sense, Sir.

And we are getting, we are connecting the wires and we are getting the signal out. But then.

The signals are having noise and other problems.

So how to do it? Then they have to actually use signal processing methods.

And then we have, then we, they have a lot of signals they are getting.

They have to analyze their signals after signal processing. Basic preprocessing they can use.

That's

Machine learning algorithms.

To understand, to train and understand the signals. What are they?

Obtained from the sensors. So this activity can be combined. The syllabus can be combined together or a connected labs not self separately materials lab doing materials only single processing only single processing lab machine learning only machine learning things. So if you can connect these labs.

Students will be understanding, so they'll be getting a holistic understanding about the domain.

Then.

Very insightful, Dr. Namboothiri. Integrating connected labs that encompass materials science, signal processing, and machine learning will indeed provide students with a comprehensive and practical understanding of the domain. Thank you for elaborating on this effective interdisciplinary approach to pedagogy.

Professor, could you outline your 3-year research roadmap, particularly focusing on projects that could drive institutional rankings and attract external funding?

So after I join if I I'm getting an opportunity to join VIT.

What I want to do is understand a variable sensor.

And healthcare technology lab.

Where research on wherever senses of different types of wearable sensors for different movement analysis.

Applications and other application like sweat sensing.

Will be carried out.

So.

Previously I have done.

Basic development of this wearable sensors for basic gait analysis.

For for Pakistan's disease patients and post rock rehabilitation.

And basic respiratory movement analysis.

So I want to continue this domain where uh.

I want to move beyond old. I want to continue all this research. Along with that I want to move beyond this.

Understanding how the speech can be get the speech analysis can you do the speech analysis using a wearable sensors you from the throat signals.

And developing electrodes, Wearable electrodes.

Like EEG electrodes, ECG electrodes.