no code implementations • 29 May 2020 • Hyeokhyen Kwon, Catherine Tong, Harish Haresamudram, Yan Gao, Gregory D. Abowd, Nicholas D. Lane, Thomas Ploetz
The lack of large-scale, labeled data sets impedes progress in developing robust and generalized predictive models for on-body sensor-based human activity recognition (HAR).
no code implementations • 9 Dec 2020 • Harish Haresamudram, Irfan Essa, Thomas Ploetz
Our work focuses on effective use of small amounts of labeled data and the opportunistic exploitation of unlabeled data that are straightforward to collect in mobile and ubiquitous computing scenarios.
no code implementations • 22 Feb 2022 • Harish Haresamudram, Irfan Essa, Thomas Plötz
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.
no code implementations • 8 Nov 2022 • Hyeongju Choi, Apoorva Beedu, Harish Haresamudram, Irfan Essa
In this work, we propose a multi-modal framework that learns to effectively combine features from RGB Video and IMU sensors, and show its robustness for MMAct and UTD-MHAD datasets.
1 code implementation • 11 Nov 2022 • Harish Haresamudram, Irfan Essa, Thomas Ploetz
The dichotomy between the challenging nature of obtaining annotations for activities, and the more straightforward nature of data collection from wearables, has resulted in significant interest in the development of techniques that utilize large quantities of unlabeled data for learning representations.
no code implementations • 1 Jun 2023 • Harish Haresamudram, Irfan Essa, Thomas Ploetz
Based on an extensive experimental evaluation on a suite of wearables-based benchmark HAR tasks, we demonstrate the potential of our learned discretization scheme and discuss how discretized sensor data analysis can lead to substantial changes in HAR.
no code implementations • 22 Oct 2023 • Megha Thukral, Harish Haresamudram, Thomas Ploetz
Yet they can fail when the differences between source and target conditions are too large and/ or only few samples from a target application domain are available, each of which are typical challenges in real-world human activity recognition scenarios.