Elderly Fall Detection Systems: A Literature Survey

Abstract

Falling is among the most damaging event elderly people may experience. With the ever-growing aging population, there is an urgent need for the development of fall detection systems. Thanks to the rapid development of sensor networks and the Internet of Things (IoT), human-computer interaction using sensor fusion has been regarded as an effective method to address the problem of fall detection. In this paper, we provide a literature survey of work conducted on elderly fall detection using sensor networks and IoT. Although there are various existing studies which focus on the fall detection with individual sensors, such as wearable ones and depth cameras, the performance of these systems are still not satisfying as they suffer mostly from high false alarms. Literature shows that fusing the signals of different sensors could result in higher accuracy and lower false alarms, while improving the robustness of such systems. We approach this survey from different perspectives, including data collection, data transmission, sensor fusion, data analysis, security, and privacy. We also review the benchmark data sets available that have been used to quantify the performance of the proposed methods. The survey is meant to provide researchers in the field of elderly fall detection using sensor networks with a summary of progress achieved up to date and to identify areas where further effort would be beneficial.

Pre-Impact Fall Detection with CNN-Based Class Activation Mapping Method

“…Accelerometer-based fall detection systems are widely used in all fall stages, including pre-impact, impact, and post-impact [ 2 ]. However, it is difficult to distinguish human activities by only using a single acceleration sensor, because different human activities may generate similar acceleration data [ 3 ]. Therefore, some studies used a gyroscope along with an accelerometer integrated as a portable inertial sensor, whose data contains posture information, to detect a fall before impact [ 4 ].…”

An Introduction to IoT Based Posture Detection and Underlying Sensor Technology

“…Machine algorithms are applied to the captured image by applying image processing. Generally, Convolutional neural networks are trained on different datasets of images to get more accuracy [14]. The non-wearable system provides more accurate details in abnormal conditions via images or video.…”

An Introduction to IoT Based Posture Detection and Underlying Sensor Technology

“…Other parameters that are monitored by the wearable device-based system are Electrocardiogram (ECG), oxygen saturation of blood, heart rate variability (HRV). The data reported by sensors is then passed to a machine learning algorithm or checked against a threshold value to classify and detect fall [14].…”