| Full text | |
| Author(s): |
Ranieri, Caetano Mazzoni
[1]
;
MacLeod, Scott
[2]
;
Dragone, Mauro
[2]
;
Vargas, Patricia Amancio
[2]
;
Romero, Roseli Aparecida Francelin
[1]
Total Authors: 5
|
| Affiliation: | [1] Univ Sao Paulo, Inst Math & Comp Sci, BR-13566590 Sao Carlos, SP - Brazil
[2] Heriot Watt Univ, Edinburgh Ctr Robot, Edinburgh EH14 4AS, Midlothian - Scotland
Total Affiliations: 2
|
| Document type: | Journal article |
| Source: | SENSORS; v. 21, n. 3 FEB 2021. |
| Web of Science Citations: | 0 |
| Abstract | |
Worldwide demographic projections point to a progressively older population. This fact has fostered research on Ambient Assisted Living, which includes developments on smart homes and social robots. To endow such environments with truly autonomous behaviours, algorithms must extract semantically meaningful information from whichever sensor data is available. Human activity recognition is one of the most active fields of research within this context. Proposed approaches vary according to the input modality and the environments considered. Different from others, this paper addresses the problem of recognising heterogeneous activities of daily living centred in home environments considering simultaneously data from videos, wearable IMUs and ambient sensors. For this, two contributions are presented. The first is the creation of the Heriot-Watt University/University of Sao Paulo (HWU-USP) activities dataset, which was recorded at the Robotic Assisted Living Testbed at Heriot-Watt University. This dataset differs from other multimodal datasets due to the fact that it consists of daily living activities with either periodical patterns or long-term dependencies, which are captured in a very rich and heterogeneous sensing environment. In particular, this dataset combines data from a humanoid robot's RGBD (RGB + depth) camera, with inertial sensors from wearable devices, and ambient sensors from a smart home. The second contribution is the proposal of a Deep Learning (DL) framework, which provides multimodal activity recognition based on videos, inertial sensors and ambient sensors from the smart home, on their own or fused to each other. The classification DL framework has also validated on our dataset and on the University of Texas at Dallas Multimodal Human Activities Dataset (UTD-MHAD), a widely used benchmark for activity recognition based on videos and inertial sensors, providing a comparative analysis between the results on the two datasets considered. Results demonstrate that the introduction of data from ambient sensors expressively improved the accuracy results. (AU) | |
| FAPESP's process: | 17/02377-5 - Machine Learning and Applications for Robotics in Smart Environments |
| Grantee: | Caetano Mazzoni Ranieri |
| Support Opportunities: | Scholarships in Brazil - Doctorate |
| FAPESP's process: | 18/25902-0 - Machine learning for help unveiling neural correlates of Parkinson's Disease |
| Grantee: | Caetano Mazzoni Ranieri |
| Support Opportunities: | Scholarships abroad - Research Internship - Doctorate |
| FAPESP's process: | 17/01687-0 - Architecture and applications for robotics in intelligent environments |
| Grantee: | Roseli Aparecida Francelin Romero |
| Support Opportunities: | Regular Research Grants |
| FAPESP's process: | 13/07375-0 - CeMEAI - Center for Mathematical Sciences Applied to Industry |
| Grantee: | José Alberto Cuminato |
| Support Opportunities: | Research Grants - Research, Innovation and Dissemination Centers - RIDC |