Interview with Zahra Ghorrati: creating frameworks for human exercise recognition utilizing wearable sensors

On this interview sequence, we’re assembly a few of the AAAI/SIGAI Doctoral Consortium individuals to seek out out extra about their analysis. Zahra Ghorrati is creating frameworks for human exercise recognition utilizing wearable sensors. We caught up with Zahra to seek out out extra about this analysis, the facets she has discovered most attention-grabbing, and her recommendation for potential PhD college students.

Inform us a bit about your PhD – the place are you finding out, and what’s the matter of your analysis?

I’m pursuing my PhD at Purdue College, the place my dissertation focuses on creating scalable and adaptive deep studying frameworks for human exercise recognition (HAR) utilizing wearable sensors. I used to be drawn to this matter as a result of wearables have the potential to rework fields like healthcare, aged care, and long-term exercise monitoring. Not like video-based recognition, which may elevate privateness issues and requires mounted digicam setups, wearables are transportable, non-intrusive, and able to steady monitoring, making them excellent for capturing exercise knowledge in pure, real-world settings.

The central problem my dissertation addresses is that wearable knowledge is usually noisy, inconsistent, and unsure, relying on sensor placement, motion artifacts, and system limitations. My objective is to design deep studying fashions that aren’t solely computationally environment friendly and interpretable but in addition strong to the variability of real-world knowledge. In doing so, I intention to make sure that wearable HAR techniques are each sensible and reliable for deployment exterior managed lab environments.

This analysis has been supported by the Polytechnic Summer time Analysis Grant at Purdue. Past my dissertation work, I contribute to the analysis group as a reviewer for conferences similar to CoDIT, CTDIAC, and IRC, and I’ve been invited to overview for AAAI 2026. I used to be additionally concerned in group constructing, serving as Native Organizer and Security Chair for the twenty fourth Worldwide Convention on Autonomous Brokers and Multiagent Programs (AAMAS 2025), and persevering with as Security Chair for AAMAS 2026.

May you give us an summary of the analysis you’ve carried out up to now throughout your PhD?

To date, my analysis has targeted on creating a hierarchical fuzzy deep neural community that may adapt to various human exercise recognition datasets. In my preliminary work, I explored a hierarchical recognition strategy, the place less complicated actions are detected at earlier ranges of the mannequin and extra complicated actions are acknowledged at larger ranges. To reinforce each robustness and interpretability, I built-in fuzzy logic rules into deep studying, permitting the mannequin to higher deal with uncertainty in real-world sensor knowledge.

A key energy of this mannequin is its simplicity and low computational value, which makes it significantly effectively suited to real-time exercise recognition on wearable gadgets. I’ve rigorously evaluated the framework on a number of benchmark datasets of multivariate time sequence and systematically in contrast its efficiency towards state-of-the-art strategies, the place it has demonstrated each aggressive accuracy and improved interpretability.

Is there a side of your analysis that has been significantly attention-grabbing?

Sure, what excites me most is discovering how completely different approaches could make human exercise recognition each smarter and extra sensible. As an illustration, integrating fuzzy logic has been fascinating, as a result of it permits the mannequin to seize the pure uncertainty and variability of human motion. As a substitute of forcing inflexible classifications, the system can cause when it comes to levels of confidence, making it extra interpretable and nearer to how people truly suppose.

I additionally discover the hierarchical design of my mannequin significantly attention-grabbing. Recognizing easy actions first, after which constructing towards extra complicated behaviors, mirrors the way in which people typically perceive actions in layers. This construction not solely makes the mannequin environment friendly but in addition gives insights into how completely different actions relate to 1 one other.

Past methodology, what motivates me is the real-world potential. The truth that these fashions can run effectively on wearables means they may finally help personalised healthcare, aged care, and long run exercise monitoring in individuals’s on a regular basis lives. And for the reason that methods I’m creating apply broadly to time sequence knowledge, their impression might prolong effectively past HAR, into areas like medical diagnostics, IoT monitoring, and even audio recognition. That sense of each depth and flexibility is what makes the analysis particularly rewarding for me.

What are your plans for constructing in your analysis up to now through the PhD – what facets will you be investigating subsequent?

Transferring ahead, I plan to additional improve the scalability and flexibility of my framework in order that it could possibly successfully deal with massive scale datasets and help real-time functions. A serious focus can be on bettering each the computational effectivity and interpretability of the mannequin, guaranteeing it’s not solely highly effective but in addition sensible for deployment in real-world eventualities.

Whereas my present analysis has targeted on human exercise recognition, I’m excited to broaden the scope to the broader area of time sequence classification. I see nice potential in making use of my framework to areas similar to sound classification, physiological sign evaluation, and different time-dependent domains. It will enable me to display the generalizability and robustness of my strategy throughout various functions the place time-based knowledge is vital.

In the long run, my objective is to develop a unified, scalable mannequin for time sequence evaluation that balances adaptability, interpretability, and effectivity. I hope such a framework can function a basis for advancing not solely HAR but in addition a broad vary of healthcare, environmental, and AI-driven functions that require real-time, data-driven decision-making.

What made you need to examine AI, and specifically the world of wearables?

My curiosity in wearables started throughout my time in Paris, the place I used to be first launched to the potential of sensor-based monitoring in healthcare. I used to be instantly drawn to how discreet and non-invasive wearables are in comparison with video-based strategies, particularly for functions like aged care and affected person monitoring.

Extra broadly, I’ve all the time been fascinated by AI’s skill to interpret complicated knowledge and uncover significant patterns that may improve human well-being. Wearables supplied the right intersection of my pursuits, combining cutting-edge AI methods with sensible, real-world impression, which naturally led me to focus my analysis on this space.

What recommendation would you give to somebody considering of doing a PhD within the area?

A PhD in AI calls for each technical experience and resilience. My recommendation could be:

• Keep curious and adaptable, as a result of analysis instructions evolve shortly, and the flexibility to pivot or discover new concepts is invaluable.
• Examine combining disciplines. AI advantages vastly from insights in fields like psychology, healthcare, and human-computer interplay.
• Most significantly, select an issue you might be actually captivated with. That zeal will maintain you thru the inevitable challenges and setbacks of the PhD journey.

Approaching your analysis with curiosity, openness, and real curiosity could make the PhD not only a problem, however a deeply rewarding expertise.

May you inform us an attention-grabbing (non-AI associated) reality about you?

Exterior of analysis, I’m captivated with management and group constructing. As president of the Purdue Tango Membership, I grew the group from simply 2 college students to over 40 lively members, organized weekly courses, and hosted massive occasions with internationally acknowledged instructors. Extra importantly, I targeted on making a welcoming group the place college students really feel linked and supported. For me, tango is greater than dance, it’s a approach to carry individuals collectively, bridge cultures, and stability the depth of analysis with creativity and pleasure.

I additionally apply these expertise in tutorial management. For instance, I function Native Organizer and Security Chair for the AAMAS 2025 and 2026 conferences, which has given me hands-on expertise managing occasions, coordinating groups, and creating inclusive areas for researchers worldwide.

About Zahra

Zahra Ghorrati is a PhD candidate and educating assistant at Purdue College, specializing in synthetic intelligence and time sequence classification with functions in human exercise recognition. She earned her undergraduate diploma in Laptop Software program Engineering and her grasp’s diploma in Synthetic Intelligence. Her analysis focuses on creating scalable and interpretable fuzzy deep studying fashions for wearable sensor knowledge. She has introduced her work at main worldwide conferences and journals, together with AAMAS, PAAMS, FUZZ-IEEE, IEEE Entry, System and Utilized Comfortable Computing. She has served as a reviewer for CoDIT, CTDIAC, and IRC, and has been invited to overview for AAAI 2026. Zahra additionally contributes to group constructing as Native Organizer and Security Chair for AAMAS 2025 and 2026.