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

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
Jan 01, 2024	We show that learning discrete representations of human movements leads to better sensor-based Human Activity Recognition (HAR) in Towards Learning Discrete Representations via Self-Supervision for Wearables-Based Human Activity Recognition.

Selected publications

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

arXiv preprint arXiv:2408.12023, 2024

Bib HTML PDF

@article{haresamudram2024limitations,
  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},
  journal = {arXiv preprint arXiv:2408.12023},
  year = {2024},
  url = {https://www.arxiv.org/pdf/2408.12023},
}

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},
}

Investigating enhancements to contrastive predictive coding for human activity recognition

Harish Haresamudram, Irfan Essa, and Thomas Plötz

In 2023 IEEE International Conference on Pervasive Computing and Communications (PerCom), 2023

Bib

@inproceedings{haresamudram2023investigating,
  title = {Investigating enhancements to contrastive predictive coding for human activity recognition},
  author = {Haresamudram, Harish and Essa, Irfan and Pl{\"o}tz, Thomas},
  booktitle = {2023 IEEE International Conference on Pervasive Computing and Communications (PerCom)},
  pages = {232--241},
  year = {2023},
  organization = {IEEE},
}

How Much Unlabeled Data is Really Needed for Effective Self-Supervised Human Activity Recognition?

Sourish Gunesh Dhekane, Harish Haresamudram, Megha Thukral, and 1 more author

In Proceedings of the 2023 ACM International Symposium on Wearable Computers, 2023

Bib

@inproceedings{dhekane2023much,
  title = {How Much Unlabeled Data is Really Needed for Effective Self-Supervised Human Activity Recognition?},
  author = {Dhekane, Sourish Gunesh and Haresamudram, Harish and Thukral, Megha and Pl{\"o}tz, Thomas},
  booktitle = {Proceedings of the 2023 ACM International Symposium on Wearable Computers},
  pages = {66--70},
  year = {2023},
}

Assessing the State of Self-Supervised Human Activity Recognition using Wearables

Harish Haresamudram, Irfan Essa, and Thomas Plötz

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 6.3 (2022), 2022

Bib

@article{haresamudram2022assessing,
  title = {Assessing the State of Self-Supervised Human Activity Recognition using Wearables},
  author = {Haresamudram, Harish and Essa, Irfan and Pl{\"o}tz, Thomas},
  journal = {Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 6.3 (2022)},
  year = {2022},
}