Search Results for author: Harish Haresamudram

Found 5 papers, 1 papers with code

Investigating Enhancements to Contrastive Predictive Coding for Human Activity Recognition

1 code implementation11 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.

Human Activity Recognition Time Series

Multi-Stage Based Feature Fusion of Multi-Modal Data for Human Activity Recognition

no code implementations8 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.

Human Activity Recognition

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

no code implementations22 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.

Domain Adaptation Human Activity Recognition +1

Contrastive Predictive Coding for Human Activity Recognition

no code implementations9 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.

Human Activity Recognition

IMUTube: Automatic Extraction of Virtual on-body Accelerometry from Video for Human Activity Recognition

no code implementations29 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).

Human Activity Recognition

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