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Deep-learning-based human activity recognition for Alzheimer’s patients’ daily life activities assistance

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Abstract

Alzheimer’s disease is considered as one of the most well-known illnesses in the elderly. It is a neurodegenerative and irreversible brain disorder that slowly destroys memory, thinking ability, and ultimately the ability to perform even basic daily tasks. In fact, people suffering from this disorder have difficulty remembering events, recognizing objects and faces, remembering the meaning of words, and developing judgment. As a result, their cognitive abilities are impaired and they are unable to perform activities of daily living independently. Therefore, patients need constant support to carry out their daily activities. In this study, we propose a new support system to support patients with Alzheimer’s disease to carry out their daily tasks independently. The proposed assistance systems are composed of two parts. The first is a human activity recognition (HAR) module to monitor the patient behaviour. Here, we proposed two HAR systems. The first is based on 2D skeleton data and convolution neural network, and the second is based on 3D skeleton and transformers. The second part of the assistance systems consists of a support module that recognizes the patient’s behavioural abnormalities and issues appropriate warnings. Here, we also proposed two methods. The first is based on a simple conditional structure, and the second is based on a reinforcement learning technique. As a result, we obtain four different assistance systems for Alzheimer’s patients. Finally, a comparative study between the four systems was carried out in terms of performance and time complexity using the DemCare dataset.

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Notes

  1. https://colab.research.google.com/.

  2. https://www.python.org/.

  3. https://keras.io/.

  4. https://demcare.eu/datasets/.

  5. https://github.com/ibaiGorordo/ONNX-Mobile-Human-Pose-3D.

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Acknowledgements

The authors would like to acknowledge the financial support of this work by grants from General Direction of Scientific Research (DGRST), Tunisia, under the ARUB program.

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  1. Research Team in Intelligent Machines (RTIM), National Engineering School of Gabes (ENIG), University of Gabes, 6029, Gabès, Tunisia

    Ahmed Snoun, Tahani Bouchrika & Olfa Jemai

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  1. Ahmed Snoun
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All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by AS. The first draft of the manuscript was written by AS, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Ahmed Snoun.

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Snoun, A., Bouchrika, T. & Jemai, O. Deep-learning-based human activity recognition for Alzheimer’s patients’ daily life activities assistance. Neural Comput & Applic 35, 1777–1802 (2023). https://doi.org/10.1007/s00521-022-07883-1

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