Arduino Based Fall Detection and Alert System

Falling down is among the major causes of medical problem that are faced by the elderly people. Elderly people tend to injured themselves from falling down more often especially when they are living alone. When a falling event occurred, medical attention need to be provided immediately in order to reduce the risk of faller from getting severe injuries which may lead to death. Several technologies have been developed which some utilized webcams to monitor the activities of elderly people. However, the cost of operation and installation is expensive and only applicable for indoor environment. Some user also worried about their privacy issues. Current commercialized device required user to wear wireless emergency transmitter in form of pendant and wristband. This method will restrict the user movement and produce high false alarm due to frequent swinging and movement of the device. This project proposed a fall detection system which is cost effective and reliable to detect fall and alert nearby healthcare center or relatives for help and support. For fall detection, accelerometer and gyroscope was used to detect acceleration and body tilt angle of the faller respectively

Arduino Based Fall Detection and Alert System

Falling down is among the major causes of medical problem that are faced by the elderly people. Elderly people tend to injured themselves from falling down more often especially when they are living alone. When a falling event occurred, medical attention need to be provided immediately in order to reduce the risk of faller from getting severe injuries which may lead to death. Several technologies have been developed which some utilized webcams to monitor the activities of elderly people. However, the cost of operation and installation is expensive and only applicable for indoor environment. Some user also worried about their privacy issues. Current commercialized device required user to wear wireless emergency transmitter in form of pendant and wristband. This method will restrict the user movement and produce high false alarm due to frequent swinging and movement of the device. This project proposed a fall detection system which is cost effective and reliable to detect fall and alert nearby healthcare center or relatives for help and support. For fall detection, accelerometer and gyroscope was used to detect acceleration and body tilt angle of the faller respectively

Real-time human ambulation, activity, and physiological monitoring:taxonomy of issues, techniques, applications, challenges and limitations

Automated methods of real-time, unobtrusive, human ambulation, activity, and wellness monitoring and data analysis using various algorithmic techniques have been subjects of intense research. The general aim is to devise effective means of addressing the demands of assisted living, rehabilitation, and clinical observation and assessment through sensor-based monitoring. The research studies have resulted in a large amount of literature. This paper presents a holistic articulation of the research studies and offers comprehensive insights along four main axes: distribution of existing studies; monitoring device framework and sensor types; data collection, processing and analysis; and applications, limitations and challenges. The aim is to present a systematic and most complete study of literature in the area in order to identify research gaps and prioritize future research directions

Development of a human fall detection system based on depth maps

Assistive care related products are increasingly in demand with the recent developments in health sector associated technologies. There are several studies concerned in improving and eliminating barriers in providing quality health care services to all people, especially elderly who live alone and those who cannot move from their home for various reasons such as disable, overweight. Among them, human fall detection systems play an important role in our daily life, because fall is the main obstacle for elderly people to live independently and it is also a major health concern due to aging population. The three basic approaches used to develop human fall detection systems include some sort of wearable devices, ambient based devices or non-invasive vision based devices using live cameras. Most of such systems are either based on wearable or ambient sensor which is very often rejected by users due to the high false alarm and difficulties in carrying them during their daily life activities. Thus, this study proposes a non-invasive human fall detection system based on the height, velocity, statistical analysis, fall risk factors and position of the subject using depth information from Microsoft Kinect sensor. Classification of human fall from other activities of daily life is accomplished using height and velocity of the subject extracted from the depth information after considering the fall risk level of the user. Acceleration and activity detection are also employed if velocity and height fail to classify the activity. Finally position of the subject is identified for fall confirmation or statistical analysis is conducted to verify the fall event. From the experimental results, the proposed system was able to achieve an average accuracy of 98.3% with sensitivity of 100% and specificity of 97.7%. The proposed system accurately distinguished all the fall events from other activities of daily life

A wearable real-time system for physical activity recognition and fall detection

This thesis work designs and implements a wearable system to recognize physical activities and detect fall in real time. Recognizing people’s physical activity has a broad range of applications. These include helping people maintaining their energy balance by developing health assessment and intervention tools, investigating the links between common diseases and levels of physical activity, and providing feedback to motivate individuals to exercise. In addition, fall detection has become a hot research topic due to the increasing population over 65 throughout the world, as well as the serious effects and problems caused by fall. In this work, the Sun SPOT wireless sensor system is used as the hardware platform to recognize physical activity and detect fall. The sensors with tri-axis accelerometers are used to collect acceleration data, which are further processed and extracted with useful information. The evaluation results from various algorithms indicate that Naive Bayes algorithm works better than other popular algorithms both in accuracy and implementation in this particular application. This wearable system works in two modes: indoor and outdoor, depending on user’s demand. Naive Bayes classifier is successfully implemented in the Sun SPOT sensor. The results of evaluating sampling rate denote that 20 Hz is an optimal sampling frequency in this application. If only one sensor is available to recognize physical activity, the best location is attaching it to the thigh. If two sensors are available, the combination at the left thigh and the right thigh is the best option, 90.52% overall accuracy in the experiment. For fall detection, a master sensor is attached to the chest, and a slave sensor is attached to the thigh to collect acceleration data. The results show that all falls are successfully detected. Forward, backward, leftward and rightward falls have been distinguished from standing and walking using the fall detection algorithm. Normal physical activities are not misclassified as fall, and there is no false alarm in fall detection while the user is wearing the system in daily life