scholarly journals A Wearable Fall Detection System based on LoRa LPWAN Technology

2020 ◽  
Vol 16 (3) ◽  
pp. 232-242
Author(s):  
Elma Zanaj ◽  
Deivis Disha ◽  
Susanna Spinsante ◽  
Ennio Gambi
Keyword(s):  
Communication Technology ◽  
Elderly Population ◽  
Detection System ◽  
Cost Effective ◽  
Fall Detection ◽  
The Elderly ◽  
Experimental Results ◽  
Computational Burden ◽  
Research Activities ◽  
Instrumented Shoes

The fall problem affects approximately one third of people aged over 65 years. Falls and fall-related injuries are one of the major causes of morbidity and mortality in the elderly population. Since many years, research activities have been targeted towards the development of technological solutions for the automatic detection and notification of falls. Among them, wearable based systems offer the advantage of being available ideally everywhere and cost-effective in terms of economy and computational burden. However, their use poses different challenges, from acceptability to battery usage. The choice of the communication technology, in particular, plays a fundamental role in the realization of a suitable solution, able to meet the target users’ needs. In this paper, we present a fall detection system, based on a pair of instrumented shoes. They communicate the alarming events to a supervising system through the LoRa LPWAN technology, without the need of a portable gateway. Experimental results demonstrate the effectiveness of the chosen communication technology and fall detection reliability.

Effective Falls Detection Method Using Two Tri-Axial Accelerometers

2013 ◽  
Vol 647 ◽  
pp. 854-860
Author(s):  
Gye Rok Jeon ◽  
Young Jae Kim ◽  
Ah Young Jeon ◽  
Sang Hoon Lee ◽  
Jae Hyung Kim ◽  
...  
Keyword(s):  
Activities Of Daily Living ◽  
Daily Living ◽  
Detection Rate ◽  
Elderly Population ◽  
Detection Method ◽  
Detection System ◽  
Fall Detection ◽  
The Elderly ◽  
Detection Systems

Falls detection systems have been developed in recent years because falls are detrimental events that can have a devastating effect on health of the elderly population. Current fall detecting methods mainly employ accelerometer to discriminate falls from activities of daily living (ADL). However, this makes it difficult to distinguish real falls from certain fall-like activities such as jogging and jumping. In this paper, an accurate fall detection system was implemented using two tri-axial accelerometers. By attaching the accelerometers on the chest and the abdomen, our system can effectively differentiate between falls and non-fall events.The Diff_Z and Sum_diff_Z parameter resulted in falls detection rate of 100%, respectively.

scholarly journals Preimpact Fall Detection for Elderly Based on Fractional Domain

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Ning Liu ◽  
Dedi Zhang ◽  
Zhong Su ◽  
Tianrun Wang
Keyword(s):  
Social Cost ◽  
Detection System ◽  
Fall Detection ◽  
The Elderly ◽  
Selection Method ◽  
Excellent Performance ◽  
Motion Data ◽  
Algorithm Framework ◽  
Warning Effect ◽  
Mean Filtering

The aging population has become a growing worldwide problem. Every year, deaths and injuries caused by elderly people's falls bring huge social costs. To reduce the rate of injury and death caused by falls among the elderly and the following social cost, the elderly must be monitored. In this context, falls detecting has become a hotspot for many research institutions and enterprises at home and abroad. This paper proposes an algorithm framework to prealarm the fall based on fractional domain, using inertial data sensor as motion data collection devices, preprocessing the data by axis synthesis and mean filtering, and using fractional-order Fourier transform to convert the collected data from time domain to fractional domain. Based on the above, a multilayer dichotomy classifier is designed, and each node parameter selection method is given, which constructed a preimpact fall detection system with excellent performance. The experiment result demonstrates that the algorithm proposed in this paper can guarantee better warning effect and classification accuracy with fewer features.

scholarly journals Machine Learning based Human Fall Detection System

2021 ◽  
Vol 9 (VI) ◽  
pp. 2677-2682
Author(s):  
Nishanth P
Keyword(s):  
Machine Learning ◽  
Detection System ◽  
Wearable Sensors ◽  
Fall Detection ◽  
The Elderly ◽  
World Health ◽  
Support Vector ◽  
Data Output ◽  
Health Organization ◽  
Context Features

Falls have become one of the reasons for death. It is common among the elderly. According to World Health Organization (WHO), 3 out of 10 living alone elderly people of age 65 and more tend to fall. This rate may get higher in the upcoming years. In recent years, the safety of elderly residents alone has received increased attention in a number of countries. The fall detection system based on the wearable sensors has made its debut in response to the early indicator of detecting the fall and the usage of the IoT technology, but it has some drawbacks, including high infiltration, low accuracy, poor reliability. This work describes a fall detection that does not reliant on wearable sensors and is related on machine learning and image analysing in Python. The camera's high-frequency pictures are sent to the network, which uses the Convolutional Neural Network technique to identify the main points of the human. The Support Vector Machine technique uses the data output from the feature extraction to classify the fall. Relatives will be notified via mobile message. Rather than modelling individual activities, we use both motion and context information to recognize activities in a scene. This is based on the notion that actions that are spatially and temporally connected rarely occur alone and might serve as background for one another. We propose a hierarchical representation of action segments and activities using a two-layer random field model. The model allows for the simultaneous integration of motion and a variety of context features at multiple levels, as well as the automatic learning of statistics that represent the patterns of the features.

scholarly journals Implementation and Application of Fall Detection on Mobile Perception Robot

2021 ◽  
Vol 2136 (1) ◽  
pp. 012053
Author(s):  
Zeyu Chen
Keyword(s):  
Elderly Population ◽  
Fall Detection ◽  
The Elderly ◽  
Detection Algorithm ◽  
Data Sets ◽  
Construction Technology ◽  
Robot Operating System ◽  
Map Construction ◽  
Robot Perception ◽  
Window Method

Abstract With the rapid increase in the number of people living in the elderly population, reducing and dealing with the problem of falls in the elderly has become the focus of research for decades. It is impossible to completely eliminate falls in daily life and activities. Detecting a fall in time can protect the elderly from injury as much as possible. This article uses the Turtlebot robot and the ROS robot operating system, combined with simultaneous positioning and map construction technology, Monte Carlo positioning, A* path planning, dynamic window method, and indoor map navigation. The YOLO network is trained using the stance and fall data sets, and the YOLOv4 target detection algorithm is combined with the robot perception algorithm to finally achieve fall detection on the turtlebot robot, and use the average precision, precision, recall and other indicators to measure.

The Role of Novel Bone Forming Agents in the Treatment of Osteoporosis

2020 ◽  
pp. 089719002096122
Author(s):  
Hansita B. Patel ◽  
Lynsie J. Lyerly ◽  
Cheryl K. Horlen
Keyword(s):  
Elderly Population ◽  
Cost Effective ◽  
The Elderly ◽  
Bisphosphonate Therapy ◽  
Evidence Based ◽  
First Line ◽  
Treatment Of Osteoporosis ◽  
Cost Effective Treatment

Osteoporosis is a growing epidemic that leads to significant morbidity and mortality among the elderly population due to associated fractures that lead to disabilities and reduced quality of life. Bisphosphonates are well-established as a first-line and cost-effective treatment for osteoporosis. Unfortunately, clinicians are often uncertain as to how to select treatments when bisphosphonates are ineffective as initial treatment or contraindicated. Romosozumab and abaloparatide are 2 alternative agents that have been recently FDA approved for the treatment of osteoporosis in postmenopausal women at high risk for fracture or patients who have failed or are intolerant to other osteoporosis therapies. Currently, the National Osteoporosis Foundation (NOF) has no formal recommendations in regard to these 2 novel agents. The purpose of this review is to help guide pharmacists on how to ensure appropriate utilization of these 2 novel bone-forming agents as potential alternatives to bisphosphonate therapy by providing evidence-based recommendations according to the current literature and key counseling points.

scholarly journals Fall Detection System for the Elderly Based on the Classification of Shimmer Sensor Prototype Data

2017 ◽  
Vol 23 (3) ◽  
pp. 147 ◽  
Author(s):  
Moiz Ahmed ◽  
Nadeem Mehmood ◽  
Adnan Nadeem ◽  
Amir Mehmood ◽  
Kashif Rizwan
Keyword(s):  
Detection System ◽  
Fall Detection ◽  
The Elderly

scholarly journals IR-UWB Sensor Based Fall Detection Method Using CNN Algorithm

Sensors ◽  
2020 ◽  
Vol 20 (20) ◽  
pp. 5948
Author(s):  
Taekjin Han ◽  
Wonho Kang ◽  
Gyunghyun Choi
Keyword(s):  
Activities Of Daily Living ◽  
Daily Living ◽  
Privacy Preservation ◽  
Detection System ◽  
Fall Detection ◽  
The Elderly ◽  
Ultra Wideband ◽  
Detection Accuracy ◽  
Secondary Damage ◽  
Behavior Types

Falls are the leading cause of fatal injuries in the elderly such as fractures, and secondary damage from falls can lead to death. As such, fall detection is a crucial topic. However, due to the trade-off relationship between privacy preservation, user convenience, and fall detection performance, it is generally difficult to develop a fall detection system that simultaneously satisfies all conditions. The main goal of this study is to build a practical fall detection framework that can effectively classify the various behavior types into “Fall” and “Activities of daily living (ADL)” while securing privacy preservation and user convenience. For this purpose, signal data containing the motion information of objects was collected using a non-contact, unobtrusive, and non-restraint impulse-radio ultra wideband (IR-UWB) radar. These data were then applied to a convolutional neural network (CNN) algorithm to create an object behavior type classifier that can classify the behavior types of objects into “Fall” and “ADL.” The data were collected by actually performing various activities of daily living, including falling. The performance of the classifier yielded satisfactory results. By combining an IR-UWB and CNN algorithm, this study demonstrates the feasibility of building a practical fall detection system that exceeds a certain level of detection accuracy while also ensuring privacy preservation and user convenience.

Sign in / Sign up

Export Citation Format

Share Document