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XLAAM: explainable LSTM-based activity and anomaly monitoring in a fog environment

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Abstract

Study of activities of daily life is gaining wide attention in today’s smart world powered by advanced sensing technologies. It is particularly significant in context of health applications useful for monitoring of elderly living alone and checking on patients in isolation or suffering from chronic diseases. Any significant deviation from an individual’s routine behaviour, such as a fall or ill-health, can be identified as an anomaly. This paper proposes XLAAM, an eXplainable LSTM-based framework to classify the activities of daily life and detect anomalies within a fog-enhanced smart home. Data from sensors in a smart home are forwarded to fog nodes where the classification and anomaly detection tasks are carried out. In case of abnormal activity detection, an alarm is raised or a notification is sent to a health worker or family. Entire data are also streamed to a cloud-based server where eXplainable Artificial Intelligence (XAI) tools are used to interpret explanations of the LSTM model decisions. This is crucial considering the impact of the framework on health and lives of patients. Interpretation of the model and its decision increases the reliability of the model for the patients or users as well as the health practitioners. We have evaluated the proposed approach on a standard dataset to demonstrate its application and feasibility in real-world applications.

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  1. Jaypee Institute of Information Technology, Noida, India

    Mradula Sharma & Parmeet Kaur

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  1. Mradula Sharma
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  2. Parmeet Kaur
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Sharma, M., Kaur, P. XLAAM: explainable LSTM-based activity and anomaly monitoring in a fog environment. J Reliable Intell Environ 9, 463–477 (2023). https://doi.org/10.1007/s40860-022-00185-2

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  • DOI: https://doi.org/10.1007/s40860-022-00185-2

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