Ebrahim Bagheri – Publication Page

Location-aware Human Activity Recognition

Tam T. Nguyen and Daniel Fernandez and Quy T. K. Nguyen and Ebrahim Bagheri
Reference:
Tam T. Nguyen; Daniel Fernandez; Quy T. K. Nguyen and Ebrahim Bagheri Location-aware Human Activity Recognition. In The 13th International Conference on Advanced Data Mining and Applications (ADMA'17), 2017.
Links to Publication: [www]
Abstract:
In this paper, we present one of the winning solutions of an international human activity recognition challenge organized by DrivenData in conjunction with the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases. The objective of the challenge was to predict activities of daily living and posture or ambulation based on wrist-worn accelerometer, RGB-D camera, and passive environmental sensor data, which was collected from a smart home in the UK. Most of the state of the art research focus on one type of data, e.g., wearable sensor data, for making predictions and overlook the usefulness of user locations for this purpose. In our work, we propose a novel approach that leverages heterogeneous data types as well as user locations for building predictive models. Note that while we do not have actual location information but we build models to predict location using machine learning models and use the predictions in user activity recognition. Compared to the state of the art, our proposed approach is able to achieve a 38% improvement with a Brier score of 0.1346. This means that roughly 9 out of 10 predictions matched the human-labeled descriptions.
Bibtex Entry:
@inproceedings{adma2017, author = {Tam T. Nguyen and Daniel Fernandez and Quy T. K. Nguyen and Ebrahim Bagheri}, title = {Location-aware Human Activity Recognition}, booktitle = {The 13th International Conference on Advanced Data Mining and Applications (ADMA'17)}, year = {2017}, url = {http://www.adma2017.net}, abstract = {In this paper, we present one of the winning solutions of an international human activity recognition challenge organized by DrivenData in conjunction with the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases. The objective of the challenge was to predict activities of daily living and posture or ambulation based on wrist-worn accelerometer, RGB-D camera, and passive environmental sensor data, which was collected from a smart home in the UK. Most of the state of the art research focus on one type of data, e.g., wearable sensor data, for making predictions and overlook the usefulness of user locations for this purpose. In our work, we propose a novel approach that leverages heterogeneous data types as well as user locations for building predictive models. Note that while we do not have actual location information but we build models to predict location using machine learning models and use the predictions in user activity recognition. Compared to the state of the art, our proposed approach is able to achieve a 38% improvement with a Brier score of 0.1346. This means that roughly 9 out of 10 predictions matched the human-labeled descriptions.} }




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