CBA Lab @Georgia Tech

address: CODA, 756 W Peachtree St NW, Atlanta, GA 30308

The Computational Behavior Analysis (CBA) lab at Georgia Tech is a Ubiquitous Computing and Applied Artificial Intelligence research group that develops, deploys, and studies methods and systems for the assessment of human behaviors that are based on physical movements and activities, and their contexts. Our primary application domain are health assessments and situated interventions to support people in their everyday lives. We utilize wearables, smart phones, and sensors integrated into the built environment and focus on inventing robust, secure, and usable machine learning methods for the analysis of multimodal sensor data streams thereby tackling challenging real-life scenarios.

news

Oct 5, 2025	CBA lab at Ubicomp / ISWC 2025
Sep 15, 2025	Congratulations Dr Haresamudram!
Mar 26, 2025	Congratulations Dr Hiremath!
Jan 13, 2025	Welcome to our new lab members!
Dec 12, 2024	Congrats to our graduates!

selected publications

ACM

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

Thukral, Megha, Dhekane, Sourish Gunesh, Hiremath, Shruthi K, Haresamudram, Harish, and Ploetz, Thomas

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

Bib

@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},
  bibtex_show = {true},
  selected = {true},
  abbr = {ACM}
}

ACM

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

Haresamudram, Harish, Tang, Chi Ian, Suh, Sungho, Lukowicz, Paul, and Ploetz, Thomas

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

Bib

@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},
  bibtex_show = {true},
  selected = {true},
  abbr = {ACM}
}

ACM

Transfer learning in sensor-based human activity recognition: A survey

Dhekane, Sourish Gunesh, and Ploetz, Thomas

ACM Computing Surveys 2025

Bib

@article{dhekane2025transfer,
  title = {Transfer learning in sensor-based human activity recognition: A survey},
  author = {Dhekane, Sourish Gunesh and Ploetz, Thomas},
  journal = {ACM Computing Surveys},
  volume = {57},
  number = {8},
  pages = {1--39},
  year = {2025},
  publisher = {ACM New York, NY},
  bibtex_show = {true},
  abbr = {ACM},
  selected = {true}
}

AAAI

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

Haresamudram, Harish, Beedu, Apoorva, Rabbi, Mashfiqui, Saha, Sankalita, Essa, Irfan, and Ploetz, Thomas

In Proceedings of the AAAI Conference on Artificial Intelligence 2025

Bib

@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},
  bibtex_show = {true},
  abbr = {AAAI},
  selected = {true}
}

ACM

Imugpt 2.0: Language-based cross modality transfer for sensor-based human activity recognition

Leng, Zikang, Bhattacharjee, Amitrajit, Rajasekhar, Hrudhai, Zhang, Lizhe, Bruda, Elizabeth, Kwon, Hyeokhyen, and Plötz, Thomas

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2024

Bib

@article{leng2024imugpt,
  title = {Imugpt 2.0: Language-based cross modality transfer for sensor-based human activity recognition},
  author = {Leng, Zikang and Bhattacharjee, Amitrajit and Rajasekhar, Hrudhai and Zhang, Lizhe and Bruda, Elizabeth and Kwon, Hyeokhyen and Pl{\"o}tz, Thomas},
  journal = {Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies},
  volume = {8},
  number = {3},
  pages = {1--32},
  year = {2024},
  publisher = {ACM New York, NY, USA},
  bibtex_show = {true},
  abbr = {ACM},
  selected = {true}
}

IMWUT
Assessing the State of Self-Supervised Human Activity Recognition Using Wearables

Haresamudram, Harish, Essa, Irfan, and Plötz, Thomas

Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. Sep 2022

Abs Bib HTML

The emergence of self-supervised learning in the field of wearables-based human activity recognition (HAR) has opened up opportunities to tackle the most pressing challenges in the field, namely to exploit unlabeled data to derive reliable recognition systems for scenarios where only small amounts of labeled training samples can be collected. As such, self-supervision, i.e., the paradigm of ’pretrain-then-finetune’ has the potential to become a strong alternative to the predominant end-to-end training approaches, let alone hand-crafted features for the classic activity recognition chain. Recently a number of contributions have been made that introduced self-supervised learning into the field of HAR, including, Multi-task self-supervision, Masked Reconstruction, CPC, and SimCLR, to name but a few. With the initial success of these methods, the time has come for a systematic inventory and analysis of the potential self-supervised learning has for the field. This paper provides exactly that. We assess the progress of self-supervised HAR research by introducing a framework that performs a multi-faceted exploration of model performance. We organize the framework into three dimensions, each containing three constituent criteria, such that each dimension captures specific aspects of performance, including the robustness to differing source and target conditions, the influence of dataset characteristics, and the feature space characteristics. We utilize this framework to assess seven state-of-the-art self-supervised methods for HAR, leading to the formulation of insights into the properties of these techniques and to establish their value towards learning representations for diverse scenarios.
@article{10.1145/3550299, abbr = {IMWUT}, bibtex_show = {true}, author = {Haresamudram, Harish and Essa, Irfan and Pl\"{o}tz, Thomas}, title = {Assessing the State of Self-Supervised Human Activity Recognition Using Wearables}, year = {2022}, issue_date = {September 2022}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, volume = {6}, number = {3}, url = {https://doi.org/10.1145/3550299}, doi = {10.1145/3550299}, journal = {Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.}, month = sep, articleno = {116}, numpages = {47}, keywords = {self-supervised learning, representation learning, human activity recognition}, html = {https://dl.acm.org/doi/10.1145/3550299}, selected = {true} }

ACM

Applying machine learning for sensor data analysis in interactive systems: Common pitfalls of pragmatic use and ways to avoid them

PlÖtz, Thomas

ACM Computing Surveys (CSUR) Sep 2021

Bib

@article{plotz2021applying,
  abbr = {ACM},
  bibtex_show = {true},
  title = {Applying machine learning for sensor data analysis in interactive systems: Common pitfalls of pragmatic use and ways to avoid them},
  author = {Pl{\"O}tz, Thomas},
  journal = {ACM Computing Surveys (CSUR)},
  volume = {54},
  number = {6},
  pages = {1--25},
  year = {2021},
  publisher = {ACM New York, NY, USA},
  selected = {true}
}