SIMADL: Simulated Activities of Daily Living Dataset

Talal Alshammari, Nasser Alshammari, Mohamed Sedky and Chris Howard
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Talal Alshammari: Staffordshire University, College Road, ST4 2DE Stoke-on-Trent, UK
Nasser Alshammari: Staffordshire University, College Road, ST4 2DE Stoke-on-Trent, UK
Mohamed Sedky: Staffordshire University, College Road, ST4 2DE Stoke-on-Trent, UK
Chris Howard: Staffordshire University, College Road, ST4 2DE Stoke-on-Trent, UK

Data, 2018, vol. 3, issue 2, 1-13

Abstract: With the realisation of the Internet of Things (IoT) paradigm, the analysis of the Activities of Daily Living (ADLs), in a smart home environment, is becoming an active research domain. The existence of representative datasets is a key requirement to advance the research in smart home design. Such datasets are an integral part of the visualisation of new smart home concepts as well as the validation and evaluation of emerging machine learning models. Machine learning techniques that can learn ADLs from sensor readings are used to classify, predict and detect anomalous patterns. Such techniques require data that represent relevant smart home scenarios, for training, testing and validation. However, the development of such machine learning techniques is limited by the lack of real smart home datasets, due to the excessive cost of building real smart homes. This paper provides two datasets for classification and anomaly detection. The datasets are generated using OpenSHS, (Open Smart Home Simulator), which is a simulation software for dataset generation. OpenSHS records the daily activities of a participant within a virtual environment. Seven participants simulated their ADLs for different contexts, e.g., weekdays, weekends, mornings and evenings. Eighty-four files in total were generated, representing approximately 63 days worth of activities. Forty-two files of classification of ADLs were simulated in the classification dataset and the other forty-two files are for anomaly detection problems in which anomalous patterns were simulated and injected into the anomaly detection dataset.

Keywords: smart home; simulation; dataset; internet of things; machine learning; classification; anomaly detection (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2018
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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