This paper addresses the problem of classifying motion signals acquired via wearable sensors for the recognition of human activity. Automatic and accurate classification of motion signals is important in facilitating the development of an effective automated health monitoring system for the elderlies. Thus, we gathered hip motion signals from two different waist mounted sensors and for each individual sensor, we converted the motion signal into spectral image sequence. We use these images as inputs to independently train two Convolutional Neural Networks (CNN), one for each of the generated image sequences from the two sensors. The outputs of the trained CNNs are then fused together to predict the final class of the human activity. We evaluate the performance of the proposed method using the cross-subjects testing approach. Our method achieves recognition accuracy (F1 score) of 0.87 on a publicly available real-world human activity dataset. This performance is superior to that reported by another state-of-the-art method on the same dataset.


    Zugriff

    Download


    Exportieren, teilen und zitieren



    Titel :

    Deep human activity recognition using wearable sensors


    Beteiligte:
    Lawal, IA (Autor:in) / Bano, S (Autor:in)

    Erscheinungsdatum :

    2019-06-05


    Anmerkungen:

    In: Proceedings of the 12th ACM International Conference on PErvasive Technologies Related to Assistive Environments. (pp. pp. 45-48). ACM: New York (NY), USA. (2019)


    Medientyp :

    Paper


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Klassifikation :

    DDC:    629



    Activity Recognition with Wearable Sensors on Loose Clothing

    Michael, Brendan / Howard, Matthew Jacob William | BASE | 2017

    Freier Zugriff

    Comparison of Different Sets of Features for Human Activity Recognition by Wearable Sensors

    Rosati, Samanta / Balestra, Gabriella / Knaflitz, Marco | BASE | 2018

    Freier Zugriff

    Probabilistic locomotion mode recognition with wearable sensors

    Martinez Hernandez, Uriel / Mahmood, Imran / Dehghani-Sanij, Abbas A. | BASE | 2017

    Freier Zugriff


    Enhancing Activity Recognition of Self-Localized Robot Through Depth Camera and Wearable Sensors

    Manzi, Alessandro / Moschetti, Alessandra / Limosani, Raffaele et al. | BASE | 2018

    Freier Zugriff