Next Article in Journal
Specific Resonant Properties of Non-Symmetrical Microwave Antennas
Next Article in Special Issue
IoTCrawler: Challenges and Solutions for Searching the Internet of Things
Previous Article in Journal
Gear Shape Measurement Potential of Laser Triangulation and Confocal-Chromatic Distance Sensors
Previous Article in Special Issue
Recognizing Context-Aware Human Sociability Patterns Using Pervasive Monitoring for Supporting Mental Health Professionals
Article

A Machine Learning Multi-Class Approach for Fall Detection Systems Based on Wearable Sensors with a Study on Sampling Rates Selection

1
Institute of Complex Systems (iCoSys), School of Engineering and Architecture of Fribourg Switzerland, HES-SO University of Applied Sciences and Arts Western Switzerland, 1700 Fribourg, Switzerland
2
Centre for Health Technologies (CHT), School of Electronics and Computer Science, University of Southampton, Southampton SO17 1BJ, UK
3
Department of Digital Technologies, Faculty of Business, Law and Digital Technologies, University of Winchester, Winchester SO22 4NR, UK
*
Authors to whom correspondence should be addressed.
This paper is an extended version of the conference paper: Zurbuchen, N.; Bruegger, P. and Wilde, A. A Comparison of Machine Learning Algorithms for Fall Detection using Wearable Sensors. In Proceedings of the 2020 International Conference on Artificial Intelligence in Information and Communication (ICAIIC), Fukuoka, Japan, 19–21 February 2020; pp. 427–431, doi: 10.1109/ICAIIC48513.2020.9065205.
Academic Editor: Klaus Moessner
Sensors 2021, 21(3), 938; https://doi.org/10.3390/s21030938
Received: 22 December 2020 / Revised: 25 January 2021 / Accepted: 26 January 2021 / Published: 30 January 2021
(This article belongs to the Special Issue Sensor Networks: Physical and Social Sensing in the IoT)
Falls are dangerous for the elderly, often causing serious injuries especially when the fallen person stays on the ground for a long time without assistance. This paper extends our previous work on the development of a Fall Detection System (FDS) using an inertial measurement unit worn at the waist. Data come from SisFall, a publicly available dataset containing records of Activities of Daily Living and falls. We first applied a preprocessing and a feature extraction stage before using five Machine Learning algorithms, allowing us to compare them. Ensemble learning algorithms such as Random Forest and Gradient Boosting have the best performance, with a Sensitivity and Specificity both close to 99%. Our contribution is: a multi-class classification approach for fall detection combined with a study of the effect of the sensors’ sampling rate on the performance of the FDS. Our multi-class classification approach splits the fall into three phases: pre-fall, impact, post-fall. The extension to a multi-class problem is not trivial and we present a well-performing solution. We experimented sampling rates between 1 and 200 Hz. The results show that, while high sampling rates tend to improve performance, a sampling rate of 50 Hz is generally sufficient for an accurate detection. View Full-Text
Keywords: fall detection; wearable sensors; sampling rate; data preprocessing; feature extraction; Machine Learning fall detection; wearable sensors; sampling rate; data preprocessing; feature extraction; Machine Learning
Show Figures

Figure 1

MDPI and ACS Style

Zurbuchen, N.; Wilde, A.; Bruegger, P. A Machine Learning Multi-Class Approach for Fall Detection Systems Based on Wearable Sensors with a Study on Sampling Rates Selection. Sensors 2021, 21, 938. https://doi.org/10.3390/s21030938

AMA Style

Zurbuchen N, Wilde A, Bruegger P. A Machine Learning Multi-Class Approach for Fall Detection Systems Based on Wearable Sensors with a Study on Sampling Rates Selection. Sensors. 2021; 21(3):938. https://doi.org/10.3390/s21030938

Chicago/Turabian Style

Zurbuchen, Nicolas, Adriana Wilde, and Pascal Bruegger. 2021. "A Machine Learning Multi-Class Approach for Fall Detection Systems Based on Wearable Sensors with a Study on Sampling Rates Selection" Sensors 21, no. 3: 938. https://doi.org/10.3390/s21030938

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop