Survey on Fall Detection System CNN based Fall Detection and Health Monitoring System using IOT

Abstract

Falls are the common problem faced by elderly population. Fall may happen due to fainting. The reason for faint involves sudden changes in the heart rate. Falls are the common cause of traumatic brain injuries in elderly people and also cause severe injuries such as fracture of the hip. This kind of injuries can create negative impact on their quality of life. In most of the cases the elderly who lay on the floors for more than an hour after falls usually results in serious trauma, also leads to death of the individuals. There are some systems exist for detecting the falls of the elderly people. These ambient sensors involve video-based methods for fall detection using rule based and machine learning based methods. The wearable sensor mainly include accelerometer, gyroscope, barometric pressure sensor, infrared sensor and kinetic RGBD camera. The existing systems are based upon the ambient sensors faces some risks such as storage and processing of video is a complex task and it also affects the privacy of the people. It is necessary to propose a system that can overcome the above drawbacks. The system proposed involves capturing the motion of the elderly people using accelerometer sensors. Those captured data via sensors are processed using CNN, which involve steps such as convolution, it results in feature map then it has to be down sampled using the max pooling methods and based on the resultant layers, the captured motion can be determined whether it is a day to day activities or a fall. Once the fall is detected the information will be sent to the family members and neighbours. Additionally, it is provided with sensor to monitor the health conditions of the elderly people, when heart rate deteriorates, an alert message will be sent to neighbours and family members to provide medical assistance.

ABM-SpConv-SIMD: Accelerating Convolutional Neural Network Inference for Industrial IoT Applications on Edge Devices

“…Based on its powerful feature extraction and data analysis capabilities, CNNs are also widely used in many IIoT applications. For example, CNN models have shown outstanding results for human activity Manuscript received on October- 30, 2021. recognition in a smart home environment [7], surface defect inspection in intelligent industrial production [8], and health symptoms monitoring such as elderly fall detection [9] and chronic diseases management [10] in smart healthcare. Besides, CNN can also be used as a fundamental model in IIoT applications based on other artificial intelligence algorithms, such as federated learning-based architecture for detecting Android malware applications [11].…”