A Benchmark Database and Baseline Evaluation for Fall Detection Based on Wearable Sensors for the Internet of Medical Things Platform

Falling is a common and dangerous event for the elderly population. Fall detection is an interesting scientific problem and an ill-defined process that can be solved through various methods. In this paper, various methods and detection algorithms based on wearable sensors are introduced. A benchmark database, namely, a fall detection database, is presented to evaluate the performance of detection algorithms. This database collects sample data from 26 males and 24 females performing 15 kinds of activities, including falls and activities of daily life, such as walking, running, and walking upstairs. The subjects comprise 50 males and females ranging from 21 to 60 years of age, 1.55 to 1.90 m in height, and 40 to 85 kg in weight. A full comparison between the existing databases and the proposed database is presented. Four baseline algorithms (the artificial neutral network, k nearest neighbor, support vector machine, and kernel Fisher discriminant) are used to evaluate the databases' reliabilities. Evaluation protocols based on the fall detection database are discussed, and the performance of the four algorithms as a baseline are presented for the following objectives: 1) to elementarily assess the database through the fall detection algorithms; 2) to identify the strengths and weaknesses of the common algorithms; and 3) to obtain the evaluation results for two classes and multiple classes based on the database. In hospitals, it is helpful to build a fall detection system for some patients who are unconscious and unable to call for help after falling down. Therefore, this paper has implications for the construction of a medical IOT platform.