Publications

@inproceedings{2020-Haresamudram-MRBSHAR,

title = {Masked reconstruction based self-supervision for human activity recognition},

author = {Harish Haresamudram and Apoorva Beedu and Varun Agrawal and Patrick L Grady and Irfan Essa and Judy Hoffman and Thomas Plötz},

url = {https://dl.acm.org/doi/10.1145/3410531.3414306

https://harkash.github.io/publication/masked-reconstruction

https://arxiv.org/abs/2202.12938},

doi = {10.1145/3410531.3414306},

year  = {2020},

date = {2020-09-01},

urldate = {2020-09-01},

booktitle = {Proceedings of the International Symposium on Wearable Computers (ISWC)},

pages = {45--49},

abstract = {The ubiquitous availability of wearable sensing devices has rendered large scale collection of movement data a straightforward endeavor. Yet, annotation of these data remains a challenge and as such, publicly available datasets for human activity recognition (HAR) are typically limited in size as well as in variability, which constrains HAR model training and effectiveness. We introduce masked reconstruction as a viable self-supervised pre-training objective for human activity recognition and explore its effectiveness in comparison to state-of-the-art unsupervised learning techniques. In scenarios with small labeled datasets, the pre-training results in improvements over end-to-end learning on two of the four benchmark datasets. This is promising because the pre-training objective can be integrated "as is" into state-of-the-art recognition pipelines to effectively facilitate improved model robustness, and thus, ultimately, leading to better recognition performance.



},

keywords = {activity recognition, ISWC, machine learning, wearable computing},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{2015-Castro-PDAFEIUDL,

title = {Predicting Daily Activities from Egocentric Images Using Deep Learning},

author = {Daniel Castro and Steven Hickson and Vinay Bettadapura and Edison Thomaz and Gregory Abowd and Henrik Christensen and Irfan Essa},

url = {https://dl.acm.org/doi/10.1145/2802083.2808398

https://arxiv.org/abs/1510.01576

http://www.cc.gatech.edu/cpl/projects/dailyactivities/

},

doi = {10.1145/2802083.2808398},

year  = {2015},

date = {2015-09-01},

urldate = {2015-09-01},

booktitle = {Proceedings of International Symposium on Wearable Computers (ISWC)},

abstract = {We present a method to analyze images taken from a passive egocentric wearable camera along with contextual information, such as time and day of the week, to learn and predict the everyday activities of an individual. We collected a dataset of 40,103 egocentric images over 6 months with 19 activity classes and demonstrate the benefit of state-of-the-art deep learning techniques for learning and predicting daily activities. Classification is conducted using a Convolutional Neural Network (CNN) with a classification method we introduce called a late fusion ensemble. This late fusion ensemble incorporates relevant contextual information and increases our classification accuracy. Our technique achieves an overall accuracy of 83.07% in predicting a person's activity across the 19 activity classes. We also demonstrate some promising results from two additional users by fine-tuning the classifier with one day of training data.},

keywords = {activity recognition, computer vision, ISWC, machine learning, wearable computing},

pubstate = {published},

tppubtype = {inproceedings}

}