Name
Abstract | ||
|---|---|---|
In this paper, we present one of the winning solutions of an international human activity recognition challenge organized by Driven Data 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. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1007/978-3-319-69179-4_58 | ADVANCED DATA MINING AND APPLICATIONS, ADMA 2017 |
Field | DocType | Volume |
Brier score,Activity recognition,Activities of daily living,Computer science,Wearable computer,Accelerometer,Home automation,Data type,Knowledge extraction,Artificial intelligence,Machine learning | Conference | 10604 |
ISSN | Citations | PageRank |
0302-9743 | 0 | 0.34 |
References | Authors | |
11 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tam T. Nguyen | 1 | 78 | 6.79 |
| Daniel Fernandez | 2 | 0 | 0.34 |
| Quy T. K. Nguyen | 3 | 0 | 0.34 |
| Ebrahim Bagheri | 4 | 1185 | 99.20 |