An Accurate Fall Detection System for the Elderly People Using Smartphone Inertial Sensors

• Published in: Journal of physics. Conference series Vol. 1530; no. 1; pp. 12102 - 12116
• Main Authors: Kadhum, A A, Al-Libawy, H, Hussein, E A
• Format: Journal Article
• Language: English
• Published: Bristol IOP Publishing 01.05.2020
• Subjects: Accelerometers; Artificial neural networks; Classifiers; Fall detection; Falling; Inertial sensing devices; Older people; Sensors; Smartphones; Support vector machines
• ISSN: 1742-6588; 1742-6596
• DOI: 10.1088/1742-6596/1530/1/012102

In developed countries, the number of elderly people living alone is continuously increasing. These people are more vulnerable to serious health issues, such as falling down. A sensor-based system, augmented to mobile phones, can provide a much-needed prediction to the falls, where injuries and fracture possibilities can be significantly decreased. The purpose of this study is to develop a fall recognition system based on smartphone inertial sensors, which is a combination of accelerometer and gyroscope. The system can distinguish between falls and other activity daily livings (ADLs). The data output from the inertial sensor have been used by two different classifiers; artificial neural network (ANN) and support vector machine (SVM), where the objective is to find an accurate falling classifier using smartphone inertial sensors. Results show that SVM based classifier offers an accuracy of 99.27%, which outperforms the state of the art results that use smartphone data.