Harish Haresamudram

I am a final year PhD student with the School of Electrical and Computer Engineering (ECE) at Georgia Institute of Techology, Atlanta. I am advised by Prof. Thomas Ploetz and Prof. Irfan Essa. I received my Master’s degree in May 2019 from Georgia Tech, where my thesis studied the role of representations in human activity recognition using wearables. I was advised by Prof. Thomas Ploetz and Prof. David Anderson. I have been supported by funding from the National Science Foundation (through the AI-CARING Institute), Optum AI, and Google.

Research

My research broadly involves learning representations for time-series data, with a special focus on developing techniques that require minimal supervision. I develop unsupervised and self-supervised learning algorithms for data from wearable sensors, including accelerometers, gyroscopes and intertial measurement units (IMUs). Subsequently, I use such representations to analyse human behavior, through movements and activities..

News

Jun 18, 2025	Past, Present, and Future of Sensor-based Human Activity Recognition Using Wearables: A Surveying Tutorial on a Still Challenging Task has been published in the IMWUT journal! It presents the journey of the nearly three-decade old sensor-based HAR task, from its origins in handcrafting heuristics to the current trend of integrating LLMs into the recognition process.
Dec 04, 2024	Our paper: Limitations in Employing Natural Language Supervision for Sensor-Based Human Activity Recognition–And Ways to Overcome Them has been accepted to AAAI 2025! Looking forward to presenting it in Philadelphia!
Nov 11, 2024	Based on our two Ubicomp tutorials on HAR, we release a new paper: Past, Present, and Future of Sensor-based Human Activity Recognition using Wearables: A Surveying Tutorial on a Still Challenging Task on ArXiv! It contains the history of HAR from heuristic-based features to the current trend of multimodal training, accompanied by a code walkthrough of standard self-supervised methods!
Oct 06, 2024	Organized the second tutorial on Solving the Sensor-based Activity Recognition Problem (SOAR) in Ubicomp 2024!
Aug 21, 2024	New paper is out on ArXiv! We show that zero shot prediction of activities through CLIP-like training on a large background dataset results in poor HAR! See: Limitations in Employing Natural Language Supervision for Sensor-Based Human Activity Recognition–And Ways to Overcome Them

Selected publications

Past, present, and future of sensor-based human activity recognition using wearables: A surveying tutorial on a still challenging task

Harish Haresamudram, Chi Ian Tang, Sungho Suh, and 2 more authors

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 2025

Bib HTML PDF

@article{haresamudram2025past,
  title = {Past, present, and future of sensor-based human activity recognition using wearables: A surveying tutorial on a still challenging task},
  author = {Haresamudram, Harish and Tang, Chi Ian and Suh, Sungho and Lukowicz, Paul and Ploetz, Thomas},
  journal = {Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies},
  volume = {9},
  number = {2},
  pages = {1--44},
  year = {2025},
  publisher = {ACM New York, NY, USA},
  url = {https://dl.acm.org/doi/pdf/10.1145/3729467},
}

Limitations in Employing Natural Language Supervision for Sensor-Based Human Activity Recognition-And Ways to Overcome Them

Harish Haresamudram, Apoorva Beedu, Mashfiqui Rabbi, and 3 more authors

In Proceedings of the AAAI Conference on Artificial Intelligence, 2025

Bib HTML PDF

@inproceedings{haresamudram2025limitations,
  title = {Limitations in Employing Natural Language Supervision for Sensor-Based Human Activity Recognition-And Ways to Overcome Them},
  author = {Haresamudram, Harish and Beedu, Apoorva and Rabbi, Mashfiqui and Saha, Sankalita and Essa, Irfan and Ploetz, Thomas},
  booktitle = {Proceedings of the AAAI Conference on Artificial Intelligence},
  volume = {39},
  number = {1},
  pages = {273--281},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/32004},
}

Layout-agnostic human activity recognition in smart homes through textual descriptions of sensor triggers (TDOST)

Megha Thukral, Sourish Gunesh Dhekane, Shruthi K Hiremath, and 2 more authors

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 2025

Bib HTML PDF

@article{thukral2025layout,
  title = {Layout-agnostic human activity recognition in smart homes through textual descriptions of sensor triggers (TDOST)},
  author = {Thukral, Megha and Dhekane, Sourish Gunesh and Hiremath, Shruthi K and Haresamudram, Harish and Ploetz, Thomas},
  journal = {Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies},
  volume = {9},
  number = {1},
  pages = {1--38},
  year = {2025},
  publisher = {ACM New York, NY, USA},
  url = {https://dl.acm.org/doi/pdf/10.1145/3712278},
}

Towards Learning Discrete Representations via Self-Supervision for Wearables-Based Human Activity Recognition

Harish Haresamudram, Irfan Essa, and Thomas Ploetz

Sensors, 2024

Bib

@article{haresamudram2024towards,
  title = {Towards Learning Discrete Representations via Self-Supervision for Wearables-Based Human Activity Recognition},
  author = {Haresamudram, Harish and Essa, Irfan and Ploetz, Thomas},
  journal = {Sensors},
  volume = {24},
  number = {4},
  pages = {1238},
  year = {2024},
  publisher = {Multidisciplinary Digital Publishing Institute},
}