A Deep learning and multimodal ambient sensing framework for human activity recognition

• Autorzy: Yachir Ali , Amamra Abdenour , Djamaa Badis , Zerrouki Ali , khierEddine Amour Ahmed

Identyfikatory

DOI

10.15439/2019F50

• Konferencja: Federated Conference on Computer Science and Information Systems (14 ; 01-04.09.2019 ; Leipzig, Germany)
• Języki publikacji: EN

Abstrakty

EN

Human Activity Recognition (HAR) is an important area of research in ambient intelligence for various contexts such as ambient-assisted living. The existing HAR approaches are mostly based either on vision, mobile or wearable sensors. In this paper, we propose a hybrid approach for HAR by combining three types of sensing technologies, namely: smartphone accelerometer, RGB cameras and ambient sensors. Acceleration and video streams are analyzed using multiclass Support Vector Machine (SVM) and Convolutional Neural Networks, respectively. Such an analysis is improved with the ambient sensing data to assign semantics to human activities using description logic rules. For integration, we design and implement a Framework to address human activity recognition pipeline from the data collection phase until activity recognition and visualization. The various use cases and performance evaluations of the proposed approach show clearly its utility and efficiency in several everyday scenarios.

Słowa kluczowe

EN

accelerometers; assisted living; convolutional neural nets; description logic; feature extraction; image recognition; smartphone; support vector machine; video signal processing

PL

akcelerometry; splotowe sieci neuronowe; ekstrakcja cech; rozpoznawanie obrazu; smartfon; maszyna wektorów nośnych; przetwarzanie sygnału wideo

• Wydawca: Polskie Towarzystwo Informatyczne
• Czasopismo: Annals of Computer Science and Information Systems
• Rocznik: 2019
• Tom: Vol. 18
• Strony: 101--105
• Opis fizyczny: Bibliogr. 22 poz., tab., il.

Twórcy

autor

Yachir Ali

• Military Polytechnic School, PO BOX 17, Bordj-El-Bahri, 16111, Algiers, Algeria

autor

Amamra Abdenour

• Military Polytechnic School, PO BOX 17, Bordj-El-Bahri, 16111, Algiers, Algeria

autor

Djamaa Badis

• Military Polytechnic School, PO BOX 17, Bordj-El-Bahri, 16111, Algiers, Algeria

autor

Zerrouki Ali

• Military Polytechnic School, PO BOX 17, Bordj-El-Bahri, 16111, Algiers, Algeria

autor

khierEddine Amour Ahmed

• Military Polytechnic School, PO BOX 17, Bordj-El-Bahri, 16111, Algiers, Algeria

Bibliografia

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Uwagi

1. Track 1: Artificial Intelligence and Applications

2. Technical Session: 14th International Symposium Advances in Artificial Intelligence and Applications

3. Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).