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XAI Framework for Fall Detection in an AAL System

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Optimization, Learning Algorithms and Applications (OL2A 2024)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 2280))

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Abstract

The Ambient Assisted Living (AAL) systems are human-centered and designed to prioritize the needs of elderly individuals, providing them with assistance in case of emergencies or unexpected situations. These systems involve caregivers or selected individuals who can be alerted and provide the necessary help when needed. To ensure effective assistance, it is crucial for caregivers to understand the reasons behind alarm triggers and the nature of the danger. This is where an explainability module comes into play. In this paper, we introduce an explainability module that offers visual explanations for the fall detection module. Our framework involves generating anchor boxes using the K-means algorithm to optimize object detection and using YOLOv8 for image inference. Additionally, we employ two well-known XAI (Explainable Artificial Intelligence) algorithms, LIME (Local Interpretable Model) and Grad-CAM (Gradient-weighted Class Activation Mapping), to provide visual explanations.

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Acknowledgements

The cooperation was supported by the HORIZON-WIDERA-2021-ACCESS-03-01 STEP - STEM Research and Equality, Diversity and Inclusion Project, under Grant Agreement No. 101078933. The authors were supported by national funds through FCT/MCTES (PIDDAC): CeDRI, UIDB/05757/2020 (DOI: 10.54499/UIDB/05757/2020) UIDP/05757/2020 (DOI: 10.54499/UIDP/05757/2020); SusTEC, LA/P/0007/2020 (DOI: 10.54499/LA/P/0007/2020); and VASA Chatbot (VASA 22PRC210).

Author information

Authors and Affiliations

  1. Lab-I* UR 4474, Université de Reims Champagne-Ardenne, 51100, Reims, France

    Chaima Messaoudi & Zahia Guessoum

  2. Research Centre in Digitalization and Intelligent Robotics (CeDRI), Laboratório Associado para a Sustentabilidade e Tecnologia em Regiões de Montanha (SusTEC), Instituto Politécnico de Bragança, Campus de Santa Apolónia, 5300-253, Bragança, Portugal

    Rebeca B. Kalbermatter, José Lima & Ana I. Pereira

Authors
  1. Chaima Messaoudi
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  2. Rebeca B. Kalbermatter
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  3. José Lima
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  4. Ana I. Pereira
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  5. Zahia Guessoum
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Corresponding author

Correspondence to Zahia Guessoum .

Editor information

Editors and Affiliations

  1. Instituto Politécnico de Bragança, Bragança, Portugal

    Ana I. Pereira

  2. Instituto Politécnico de Bragança, Bragança, Portugal

    Florbela P. Fernandes

  3. Escola Superior de Tecnologia e Gestão, Instituto Politécnico de Bragança, Bragança, Portugal

    João P. Coelho

  4. Instituto Politécnico de Bragança, Bragança, Portugal

    João P. Teixeira

  5. Department of Electrical Engineering, Instituto Politécnico de Bragança, Bragança, Portugal

    José Lima

  6. Instituto Politécnico de Bragança, Bragança, Portugal

    Maria F. Pacheco

  7. Instituto Politécnico de Bragança, Bragança, Portugal

    Rui P. Lopes

  8. Dept ISAATC, Universidad de La Laguna, San Cristóbal de La Laguna, Spain

    Santiago T. Álvarez

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Messaoudi, C., Kalbermatter, R.B., Lima, J., Pereira, A.I., Guessoum, Z. (2024). XAI Framework for Fall Detection in an AAL System. In: Pereira, A.I., et al. Optimization, Learning Algorithms and Applications. OL2A 2024. Communications in Computer and Information Science, vol 2280. Springer, Cham. https://doi.org/10.1007/978-3-031-77426-3_1

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