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The MobiFall Dataset: Fall Detection and Classification with a Smartphone
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Author(s): George Vavoulas (Technological Educational Institute of Crete (TEIC), Biomedical Informatics and eHealth Laboratory (BMI Lab), Greece), Matthew Pediaditis (TEIC - BMI Lab and Foundation for Research and Technology – Hellas, Computational Medicine Laboratory, Institute of Computer Science, Greece), Charikleia Chatzaki (TEIC- BMI Lab and Foundation for Research and Technology – Hellas, Computational Medicine Laboratory, Institute of Computer Science, Greece), Emmanouil G. Spanakis (TEIC- BMI Lab and Foundation for Research and Technology – Hellas, Computational Medicine Laboratory, Institute of Computer Science, Greece)and Manolis Tsiknakis (TEIC- BMI Lab and Foundation for Research and Technology – Hellas, Computational Medicine Laboratory, Institute of Computer Science, Greece)
Copyright: 2017
Pages: 14
Source title:
Artificial Intelligence: Concepts, Methodologies, Tools, and Applications
Source Author(s)/Editor(s): Information Resources Management Association (USA)
DOI: 10.4018/978-1-5225-1759-7.ch048
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Abstract
Fall detection is receiving significant attention in the field of preventive medicine, wellness management and assisted living, especially for the elderly. As a result, several fall detection systems are reported in the research literature or exist as commercial systems. Most of them use accelerometers and/ or gyroscopes attached on a person's body as the primary signal sources. These systems use either discrete sensors as part of a product designed specifically for this task or sensors that are embedded in mobile devices such as smartphones. The latter approach has the advantage of offering well tested and widely available communication services, e.g. for calling emergency when necessary. Nevertheless, automatic fall detection continues to present significant challenges, with the recognition of the type of fall being the most critical. The aim of this work is to introduce a human fall and activity dataset to be used in testing new detection methods, as well as performing objective comparisons between different reported algorithms for fall detection and activity recognition, based on inertial-sensor data from smartphones. The dataset contains signals recorded from the accelerometer and gyroscope sensors of a latest technology smartphone for four different types of falls and nine different activities of daily living. Utilizing this dataset, the results of an elaborate evaluation of machine learning-based fall detection and fall classification are presented and discussed in detail.
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