A FPGA threshold-based fall detection algorithm for elderly fall monitoring with verilog

Fall is one of the leading causes of accidental or unintentional injury deaths worldwide due to serious injuries such as head traumas and hip fractures. As life expectancy improved, the rapid increase in aging population implied the need for the development of vital sign detector such as fall detector to help elderly in seeking for medical attention. Immediate rescue could prevent victims from the risk of suspension trauma and reduce the mortality rate among elderly population due to fall accident effectively. This paper presents the development of FPGA-based fall detection algorithm using a threshold-based analytical method. The proposed algorithm is to minimize the rate of false positive fall detection proposed from other researchers by including the non-fall events in the data analysis. Based on the performance evaluation, the proposed algorithm successfully achieved a sensitivity of 97.45% and specificity of 97.38%. The proposed algorithm was able to differentiate fall events and non-fall events effectively, except for fast lying and fall that ending with sitting position. The proposed algorithm shows a good result and the performance of the proposed algorithm can be further improved by using an additional gyroscope to detect the posture of the lower body part.

Smart Walking Stick with Fall detector

There are many aged people in our surrounding. They can’t walk without the help of other people of the society. One has to ask guidance to reach their destination. They have to face more struggles in their daily life. Today technology is growing to a greater extent, however there is no cost-effective device for aged people. The history of walking stick can be traced far back when the simple wooden stick was used by human for support. James Biggs of Bristol claims to have invented the walking stick in the year 1921. For an aged person it becomes difficult to do his/her day-to-day activities, therefore Smart walking stick can help people in moving and allowing them to perform their work easily, during walking in the street, which makes it very dangerous. GPS is used which tells the user about his current location. Keywords: Fall detector, Stick, GPS, GSM Module, LDR sensor, Torch.

Smart Glove for Blind

Blind people's main problem is their loss of vision that cannot be corrected with glasses or contact lenses. They use sticks which will help to know their movement. But here there is a problem with navigation which leads to harm. To get rid of this problem we implemented a smart glove for blind using Ultrasonic sensor, GPS receiver, buzzer, vibrator, microcontroller, colour sensor, fall detector, and battery are all included in this system. Arduino UNO can be used to carry out this project. It can be connected to the sensors listed above. The ultrasonic sensor is useful for detecting impediments and guiding the visually handicapped. If the ultrasonic sensor encounters any difficulties along the road, a buzzer/vibrator is employed to alert the person. A colour sensor can aid in the detection of various coloured objects or items. If the person has an accident or something untoward occurs, the fall detector will notice it and notify their guardian. The device's overall goal is to give a simple and safe way for blind people to overcome their daily challenges.

Wearable Fall Detector for Elderly People

Now a days, most of the elderly person live in home alone. In their day to day living, some activities likely to have some accidents, such as fall. If the fall is unobserved for a longer period, it may lead to severe health trauma or even leads to death. According to the insights announced in National Safety Council, they are the subsequent driving reason for unexpected passing assessing 424000 passing worldwide. The objective of this paper is to develop a device that detects the fall event of the elderly person and intimate to the family members about the event. A call will be made automatically to the contact number provided in the fall detector application installed in the smart phone. It can accompany elderly people both indoors and outdoors in contrast to ambient devices. Computation of this method is simple and it is possible to implement in small size, so it can be easily carried by the elderly people. It can be afforded by

Wearable Fall Detector Using Recurrent Neural Networks

Falls have become a relevant public health issue due to their high prevalence and negative effects in elderly people. Wearable fall detector devices allow the implementation of continuous and ubiquitous monitoring systems. The effectiveness for analyzing temporal signals with low energy consumption is one of the most relevant characteristics of these devices. Recurrent neural networks (RNNs) have demonstrated a great accuracy in some problems that require analyzing sequential inputs. However, getting appropriate response times in low power microcontrollers remains a difficult task due to their limited hardware resources. This work shows a feasibility study about using RNN-based deep learning models to detect both falls and falls’ risks in real time using accelerometer signals. The effectiveness of four different architectures was analyzed using the SisFall dataset at different frequencies. The resulting models were integrated into two different embedded systems to analyze the execution times and changes in the model effectiveness. Finally, a study of power consumption was carried out. A sensitivity of 88.2% and a specificity of 96.4% was obtained. The simplest models reached inference times lower than 34 ms, which implies the capability to detect fall events in real-time with high energy efficiency. This suggests that RNN models provide an effective method that can be implemented in low power microcontrollers for the creation of autonomous wearable fall detection systems in real-time.