Identification of Soft Fall based on Falling State Occurrences

Indonesian Journal of Electrical Engineering and Computer Science

Falls are dangerous and contribute to over 80% of injury-related hospitalization especially amongst the elderly. Hence, fall detection is important for preventing severe injuries and accidental deaths. Meanwhile, recognizing human activity is important for monitoring health status and quality of life as it can be applied in geriatric care and healthcare in general. This research presents the development of a fall detection and human activity recognition system using Threshold Based Method (TBM) and Neural Network (NN). Intentional forward fall and six other activities of daily living (ADLs), which include running, jumping, walking, sitting, lying, and standing are performed by 15 healthy volunteers in a series of experiments. There are four important stages involved in fall detection and ADL recognition, which are signal filtering, segmentation, features extraction and classification. For classification, TBM achieved an accuracy of 98.41% and 95.40% for fall detection and activity r...

This study focuses on the performance of a fall detection method using data coming from real falls performed by relatively young people and the application of this technique in the case of an elder person. Although the vast majority of studies concerning fall detection place the sensory on the waist, in this research the wearable device must be placed on the wrist because it's usability. A first pre-processing stage is carried out as stated in [1, 17]; this stage detects the most relevant points to label. This study analyzes the suitability of different models in solving this classification problem: a feed-forward Neural Network and a rule based system generated with the C5.0 algorithm. A discussion about the results and the deployment issues is included.

IEEE Pervasive Computing, 2000

Gait & Posture, 2007

We dedicate this paper to the memory of our late colleague and co-author, Jacinta O'Brien, who died suddenly on February 3, 2004:

Information and Emerging …

The risk of sustaining heavy injuries through accidental falls creates a major medical problem for elderly people. This paper conducts a survey of the various automatic techniques and methods proposed to detect falls and anomalies in movements of the elderly, through monitoring of their daily life activities. These methods can be broadly divided into three main categories: 1) Video Analysis Based; 2) Acoustic and Ambience Sensor Based; and 3) Kinematic Sensor Based. This paper critically analyzes the various proposed methodologies, comparing their strengths and weaknesses. We further propose our own technique for fall detection and monitoring of common daily life activities (walking, running, sitting, standing, and lying down), through a novel approach that provides a low cost solution and ensures the safety and security of the elderly without restricting them to confined surroundings.

International Journal for Research in Applied Science & Engineering Technology (IJRASET), 2021

Fall is a significant national health issue for the elderly people, generally resulting in severe injuries when the person lies down on the floor over an extended period without any aid after experiencing a great fall. Thus, elders need to be cared very attentively. A supervised-machine learning based fall detection approach with accelerometer, gyroscope is devised. The system can detect falls by grouping different actions as fall or non-fall events and the care taker is alerted immediately as soon as the person falls. The public dataset SisFall with efficient class of features is used to identify fall. The Random Forest (RF) and Support Vector Machine (SVM) machine learning algorithms are employed to detect falls with lesser false alarms. The SVM algorithm obtain a highest accuracy of 99.23% than RF algorithm.

Falling accidents, including slipping, tripping and falling, are the primary reason of injury related to death not only for elderly, but for young people or worker happening at workplace also. If falling accident can be early detected in pre-fall or critical fall phase, called pre-impact fall detection, it will be very useful such as conducting airbag inflation. Furthermore, various detection methods, with an uncomplicated threshold detection method, do maximizing the true positive prediction values but the lead-time, time before subject impacts to the floor, will likely increases the chance of false alarms. Consequently the researcher found that the using of adaptive threshold may reduce false alarms. In this paper, the dynamic threshold method, automatically adjustable threshold for pre-impact fall detection in wearable device, has been proposed and experimented. For our evaluation, 192 instances of several kinds of activity of daily living and falling, were captured. All activities were performed by 6 different young healthy volunteers, 4 males and 2 females, aged between 19 and 21. The several experiments were conducted for performance evaluation including sensitivity, specificity and accuracy measurements. The results of proposed method can detect the pre-impact fall from normal activities of daily living with 99.48% sensitivity, 95.31% specificity and 97.40% accuracy with 365.12 msec of lead time. The results confirm that our proposed method with automatically adjustable threshold based on motion history, is suitable for using in pre-impact fall detection system than fixed threshold based method.

International Journal on Smart Sensing and Intelligent Systems, 2013

It is of little surprise that falling is often accepted as a natural part of the aging process. In fact, it is the impact rather than the occurrence of falls in the elderly, which is of most concern. Aging people are typically frailer, more unsteady, and have slower reactions, thus are more likely to fall and be injured than younger individuals. Typically, research and industry presented various practical solutions for assisting the elderly and their caregivers against falls via detecting falls and triggering notification alarms calling for help as soon as falls occur in order to diminish fall consequences. Furthermore, fall likelihood prediction systems have been emerged lately based on the manipulation of the medical and behavioral history of elderly patients in order to predict the possibility of falls occurrence. Accordingly, response from caregivers may be triggered prior to most fall occurrences and accordingly prevent falls from taking place. This paper presents an extensive ...

Recent Advances in Computer Science and Communications, 2022

Background: The biggest challenge in our technologically advanced society is the healthy being of aging individuals and differently-abled people in our society. The leading cause for significant injuries and early death in senior citizens and differently-abled people is due to falling off. The possibility to automatically detect falls has increased demand for such devices, and the high detection rate is achieved using the wearable sensors, this technology has a quite social and monetary impact on society. So even for the daily activity in the life of aged people, an automatically fall detecting system and vital signs examining system become a necessity. Objectives: This research work aims at helping aged people and every other necessary human by monitoring their vital signs and fall prediction. A fall detection VitaFALL (Vital Signs and Fall Monitoring) device, could analyze the measurement in all three orthogonal directions using a triple-axis accelerometer, and Vital Signs Paramet...

Sensors

Falls are the primary contributors of accidents in elderly people. An important factor of fall severity is the amount of time that people lie on the ground. To minimize consequences through a short reaction time, the motion sensor “AIDE-MOI” was developed. “AIDE-MOI” senses acceleration data and analyzes if an event is a fall. The threshold-based fall detection algorithm was developed using motion data of young subjects collected in a lab setup. The aim of this study was to improve and validate the existing fall detection algorithm. In the two-phase study, twenty subjects (age 86.25 ± 6.66 years) with a high risk of fall (Morse > 65 points) were recruited to record motion data in real-time using the AIDE-MOI sensor. The data collected in the first phase (59 days) was used to optimize the existing algorithm. The optimized second-generation algorithm was evaluated in a second phase (66 days). The data collected in the two phases, which recorded 31 real falls, was split-up into one-...