A versatile ankle-mounted fall detection device based on attitude heading systems

In this paper we propose a wireless wearable device for real-time fall detection attached to the ankle user. This device can also be embedded inside the footwear with similar performance and a lower invasiveness of use. It contains a 3-axis accelerometer, a 3-axis gyroscope and a 3-axis magnetometer realizing an Attitude Heading Reference System (AHRS). The AHRS provides the correct 3D orientation through an implemented data fusion algorithm. On sensor board is also implemented the automatic fall detection algorithm handling the orientation data from AHRS and the acceleration data from the triaxial accelerometer. The fall is a critical event that can cause physical and psychological effects particularly among older people. The elderly timely assistance after a fall could prevent serious health care consequences. For this reason the alarms generated by fall detection algorithm are sent to a smartphone via the Bluetooth module integrated into the device. In the smartphone runs a background application that sends sms alert to preset phone numbers or makes prerecorded phone calls after receiving alarms from the sensor node. In order to evaluate the performance of the proposed fall detection system, the developed device has been tested according to an experimental protocol in which volunteers perform simulated falls, simulated falls with recovery and activities of daily living (ADL). Our ankle-mounted devise shows a significant improvement in performance compared to other devices proposed in literature. In fact, the results of the experimental protocol show a sensitivity of 97% and a specificity of 100%

Development of a Fall Detection System Based on Neural Network Featuring IoT-Technology

Accidental falls are considered a major cause of accidents that could lead to serious injuries, paralysis, psychological damage, and even deaths, especially for the elderly. Therefore in this project, a neural network-based fall detection system that could automatically detect a fall event is proposed. The system is enhanced with Internet-of-Things (IoT) features that could reduce the response time and efficiently improve the prognosis of fall victims. A 10 Degree of Freedom (DOF) Inertial Measurement Unit (IMU) module is connected to an Intel Edison with Mini Breakout board and mounted on a wearable waist-worn device to continuously record body movements. A backpropagation neural network algorithm has been developed to accurately distinguish falls from different postural transitions during activities of daily living (ADL). A body temperature and heart-pulse monitoring device were developed for this system to provide the medical personnel additional information on the body condition of the fall victim. Using the latest IoT-technology, the system can be connected to the internet and provides a continuous and real-time monitoring capability. Once a fall accident happens, the system will be automatically triggered. This will activate an Android App through the Wi-Fi network that will then send an emergency SMS with the actual location and body conditions of the victim to a recipient. A series of falls and ADL simulations were performed by a group of subjects to test and validate the performance of the system. The experiment results showed that the proposed system could obtain a sensitivity of 95.5%, specificity of 96.4%, and accuracy of 96.3%

Development of a fall detection system based on neural network featuring IoT-Technology

Accidental falls are considered a major cause of accidents that could lead to serious injuries, paralysis, psychological damage, and even deaths, especially for the elderly. Therefore in this project, a neural network-based fall detection system that could automatically detect a fall event is proposed. The system is enhanced with Internet-ofThings (IoT) features that could reduce the response time and efficiently improve the prognosis of fall victims. A 10 Degree of Freedom (DOF) Inertial Measurement Unit (IMU) module is connected to an Intel Edison with Mini Breakout board and mounted on a wearable waist-worn device to continuously record body movements. A backpropagation neural network algorithm has been developed to accurately distinguish falls from different postural transitions during activities of daily living (ADL). A body temperature and heartpulse monitoring device were developed for this system to provide the medical personnel additional information on the body condition of the fall victim. Using the latest IoT-technology, the system can be connected to the internet and provides a continuous and real-time monitoring capability. Once a fall accident happens, the system will be automatically triggered. This will activate an Android App through the Wi-Fi network that will then send an emergency SMS with the actual location and body conditions of the victim to a recipient. A series of falls and ADL simulations were performed by a group of subjects to test and validate the performance of the system. The experiment results showed that the proposed system could obtain a sensitivity of 95.5%, specificity of 96.4%, and accuracy of 96.3%