Advanced search
1 file | 2.75 MB Add to list

Unlocking the potential of smartphone and ambient sensors for ADL detection

Marija Stojchevska (UGent) , Mathias De Brouwer (UGent) , Martijn Courteaux (UGent) , Bram Steenwinckel (UGent) , Sofie Van Hoecke (UGent) and Femke Ongenae (UGent)
Author
Organization
Abstract
The detection of Activities of Daily Living (ADL) holds significant importance in a range of applications, including elderly care and health monitoring. Our research focuses on the relevance of ADL detection in elderly care, highlighting the importance of accurate and unobtrusive monitoring. In this paper, we present a novel approach that that leverages smartphone data as the primary source for detecting ADLs. Additionally, we investigate the possibilities offered by ambient sensors installed in smart home environments to complement the smartphone data and optimize the ADL detection. Our approach uses a Long Short-Term Memory (LSTM) model. One of the key contributions of our work is defining ADL detection as a multilabeling problem, allowing us to detect different activities that occur simultaneously. This is particularly valuable since in real-world scenarios, individuals can perform multiple activities concurrently, such as cooking while watching TV. We also made use of unlabeled data to further enhance the accuracy of our model. Performance is evaluated on a real-world collected dataset, strengthening reliability of our findings. We also made the dataset openly available for further research and analysis. Results show that utilizing smartphone data alone already yields satisfactory results, above 50% true positive rate and balanced accuracy for all activities, providing a convenient and non-intrusive method for ADL detection. However, by incorporating ambient sensors, as an additional data source, one can improve the balanced accuracy of the ADL detection by 7% and 8% of balanced accuracy and true positive rate respectively, on average.
Keywords
HUMAN ACTIVITY RECOGNITION, WEARABLE SENSOR, CONTEXT, FUSION, Human activity recognition (HAR), Real-world data, Smartphone sensor data, Ambient sensor data

Downloads

  • DS735.pdf
    • full text (Published version)
    • |
    • open access
    • |
    • PDF
    • |
    • 2.75 MB

Citation

Please use this url to cite or link to this publication:

MLA
Stojchevska, Marija, et al. “Unlocking the Potential of Smartphone and Ambient Sensors for ADL Detection.” SCIENTIFIC REPORTS, vol. 14, no. 1, 2024, doi:10.1038/s41598-024-56123-0.
APA
Stojchevska, M., De Brouwer, M., Courteaux, M., Steenwinckel, B., Van Hoecke, S., & Ongenae, F. (2024). Unlocking the potential of smartphone and ambient sensors for ADL detection. SCIENTIFIC REPORTS, 14(1). https://doi.org/10.1038/s41598-024-56123-0
Chicago author-date
Stojchevska, Marija, Mathias De Brouwer, Martijn Courteaux, Bram Steenwinckel, Sofie Van Hoecke, and Femke Ongenae. 2024. “Unlocking the Potential of Smartphone and Ambient Sensors for ADL Detection.” SCIENTIFIC REPORTS 14 (1). https://doi.org/10.1038/s41598-024-56123-0.
Chicago author-date (all authors)
Stojchevska, Marija, Mathias De Brouwer, Martijn Courteaux, Bram Steenwinckel, Sofie Van Hoecke, and Femke Ongenae. 2024. “Unlocking the Potential of Smartphone and Ambient Sensors for ADL Detection.” SCIENTIFIC REPORTS 14 (1). doi:10.1038/s41598-024-56123-0.
Vancouver
1.
Stojchevska M, De Brouwer M, Courteaux M, Steenwinckel B, Van Hoecke S, Ongenae F. Unlocking the potential of smartphone and ambient sensors for ADL detection. SCIENTIFIC REPORTS. 2024;14(1).
IEEE
[1]
M. Stojchevska, M. De Brouwer, M. Courteaux, B. Steenwinckel, S. Van Hoecke, and F. Ongenae, “Unlocking the potential of smartphone and ambient sensors for ADL detection,” SCIENTIFIC REPORTS, vol. 14, no. 1, 2024.
@article{01HTPKKVSETZXZ3HAZ61DF2XR0,
  abstract     = {{The detection of Activities of Daily Living (ADL) holds significant importance in a range of applications, including elderly care and health monitoring. Our research focuses on the relevance of ADL detection in elderly care, highlighting the importance of accurate and unobtrusive monitoring. In this paper, we present a novel approach that that leverages smartphone data as the primary source for detecting ADLs. Additionally, we investigate the possibilities offered by ambient sensors installed in smart home environments to complement the smartphone data and optimize the ADL detection. Our approach uses a Long Short-Term Memory (LSTM) model. One of the key contributions of our work is defining ADL detection as a multilabeling problem, allowing us to detect different activities that occur simultaneously. This is particularly valuable since in real-world scenarios, individuals can perform multiple activities concurrently, such as cooking while watching TV. We also made use of unlabeled data to further enhance the accuracy of our model. Performance is evaluated on a real-world collected dataset, strengthening reliability of our findings. We also made the dataset openly available for further research and analysis. Results show that utilizing smartphone data alone already yields satisfactory results, above 50% true positive rate and balanced accuracy for all activities, providing a convenient and non-intrusive method for ADL detection. However, by incorporating ambient sensors, as an additional data source, one can improve the balanced accuracy of the ADL detection by 7% and 8% of balanced accuracy and true positive rate respectively, on average.}},
  articleno    = {{5392}},
  author       = {{Stojchevska, Marija and De Brouwer, Mathias and Courteaux, Martijn and Steenwinckel, Bram and Van Hoecke, Sofie and Ongenae, Femke}},
  issn         = {{2045-2322}},
  journal      = {{SCIENTIFIC REPORTS}},
  keywords     = {{HUMAN ACTIVITY RECOGNITION,WEARABLE SENSOR,CONTEXT,FUSION,Human activity recognition (HAR),Real-world data,Smartphone sensor data,Ambient sensor data}},
  language     = {{eng}},
  number       = {{1}},
  pages        = {{21}},
  title        = {{Unlocking the potential of smartphone and ambient sensors for ADL detection}},
  url          = {{http://doi.org/10.1038/s41598-024-56123-0}},
  volume       = {{14}},
  year         = {{2024}},
}

Altmetric
View in Altmetric
Web of Science
Times cited: