Time Series Anomaly Detection in Smart Homes: A Deep Learning Approach

Zamani, Somayeh; Talebi, Hamed; Stevens, Gunnar

doi:10.48550/arxiv.2302.14781

Cited by 1 publication

(1 citation statement)

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“…Nonetheless, these approaches exhibit certain limitations. For example, in visual activity recognition, the privacy of the resident is compromised, while in body position and action identification, some devices should be attached to the resident for recording his/her body position and actions, such as standing, sitting, and walking, which is not always convenient [9][10][11]. In addition, most research on detecting anomalies in human activity considers a single resident.…”

Section: Introductionmentioning

confidence: 99%

A Comprehensive Review of Machine Learning Approaches for Anomaly Detection in Smart Homes: Experimental Analysis and Future Directions

Rahman,

Gupta,

Bhatt

et al. 2024

Future Internet

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Detecting anomalies in human activities is increasingly crucial today, particularly in nuclear family settings, where there may not be constant monitoring of individuals’ health, especially the elderly, during critical periods. Early anomaly detection can prevent from attack scenarios and life-threatening situations. This task becomes notably more complex when multiple ambient sensors are deployed in homes with multiple residents, as opposed to single-resident environments. Additionally, the availability of datasets containing anomalies representing the full spectrum of abnormalities is limited. In our experimental study, we employed eight widely used machine learning and two deep learning classifiers to identify anomalies in human activities. We meticulously generated anomalies, considering all conceivable scenarios. Our findings reveal that the Gated Recurrent Unit (GRU) excels in accurately classifying normal and anomalous activities, while the naïve Bayes classifier demonstrates relatively poor performance among the ten classifiers considered. We conducted various experiments to assess the impact of different training–test splitting ratios, along with a five-fold cross-validation technique, on the performance. Notably, the GRU model consistently outperformed all other classifiers under both conditions. Furthermore, we offer insights into the computational costs associated with these classifiers, encompassing training and prediction phases. Extensive ablation experiments conducted in this study underscore that all these classifiers can effectively be deployed for anomaly detection in two-resident homes.

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Section: Introductionmentioning

confidence: 99%