Harish Haresamudram

PhD student in the Computational Behavior Analysis Lab, at Georgia Institute of Technology

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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

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.
Sep 01, 2023 How Much Unlabeled Data is Really Needed for Effective Self-Supervised Human Activity Recognition? has been accepted to ISWC 2023!
Sep 01, 2023 Organized a tutorial called Solving the Sensor-based Activity Recognition Problem (SOAR) in Ubicomp 2023!
Mar 01, 2023 Our paper: Investigating enhancements to contrastive predictive coding for human activity recognition has been accepted to the 2023 IEEE International Conference on Pervasive Computing and Communications (PerCom)!

Selected publications

  1. nls_plot.png
    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
  2. discretization.png
    Towards Learning Discrete Representations via Self-Supervision for Wearables-Based Human Activity Recognition
    Harish Haresamudram, Irfan Essa, and Thomas Ploetz
    Sensors, 2024
  3. enhanced_cpc.png
    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
  4. 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
  5. assessment.png
    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