Monitoring for Elderly Care: The Role of Wearable Sensors in Fall Detection and Fall Prediction Research

2015 ◽  
pp. 619-652 ◽  
Author(s):  
Kejia Wang ◽  
Stephen Redmond ◽  
Nigel Lovell
Keyword(s):  
Elderly Care ◽  
Wearable Sensors ◽  
Fall Detection ◽  
Fall Prediction

VitaFALL: Advanced multi-threshold based reliable fall detection system

2020 ◽  
Vol 13 ◽  
Author(s):  
Warish D. Patel ◽  
Chirag Patel ◽  
Monal Patel
Keyword(s):  
Detection System ◽  
Wearable Sensors ◽  
Research Work ◽  
Text Message ◽  
Vital Signs ◽  
Early Death ◽  
Fall Detection ◽  
High Detection Rate ◽  
Aged People ◽  
Fall Prediction

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 Parameters (Heartrate, Heartbeat, and Temperature monitoring) for the aged and differently-abled people. Methods: Comparison with Present Algorithms, there are various benefits regarding privacy, success rate, and design of devices upgraded using an implemented algorithm over the ubiquitous algorithm. Results: As concluded from the experimental outcomes, the accuracy achieved is up to 94%, ADXL335 is a 3-Axial Accelerometer Module that collects the accelerations of aged people from a VitaFALL device. A guardian can be notified by sending a text message via GSM and GPRS module so that aged can be helped. Conclusion: However, a delay in the time can be noticed while comparing the gradient and minimum value to predetermine the state of the older person. The experiment results show the adequacy of the proposed approach.

scholarly journals IoT Wearable Sensors and Devices in Elderly Care: A Literature Review

Sensors ◽  
2020 ◽  
Vol 20 (10) ◽  
pp. 2826 ◽  
Author(s):  
Thanos G. Stavropoulos ◽  
Asterios Papastergiou ◽  
Lampros Mpaltadoros ◽  
Spiros Nikolopoulos ◽  
Ioannis Kompatsiaris
Keyword(s):  
Assisted Living ◽  
Elderly Care ◽  
Wearable Sensors ◽  
Ambient Assisted Living ◽  
Fall Detection ◽  
Individual Case ◽  
Current State ◽  
Sensing Technology ◽  
Promising Solution ◽  
Global Population

The increasing ageing global population is causing an upsurge in ailments related to old age, primarily dementia and Alzheimer’s disease, frailty, Parkinson’s, and cardiovascular disease, but also a general need for general eldercare as well as active and healthy ageing. In turn, there is a need for constant monitoring and assistance, intervention, and support, causing a considerable financial and human burden on individuals and their caregivers. Interconnected sensing technology, such as IoT wearables and devices, present a promising solution for objective, reliable, and remote monitoring, assessment, and support through ambient assisted living. This paper presents a review of such solutions including both earlier review studies and individual case studies, rapidly evolving in the last decade. In doing so, it examines and categorizes them according to common aspects of interest such as health focus, from specific ailments to general eldercare; IoT technologies, from wearables to smart home sensors; aims, from assessment to fall detection and indoor positioning to intervention; and experimental evaluation participants duration and outcome measures, from acceptability to accuracy. Statistics drawn from this categorization aim to outline the current state-of-the-art, as well as trends and effective practices for the future of effective, accessible, and acceptable eldercare with technology.

Engineering Human Gait and the Potential Role of Wearable Sensors to Monitor Falls

2019 ◽  
pp. 401-426
Author(s):  
Ervin Sejdić ◽  
Alan Godfrey ◽  
William McIlroy ◽  
Manuel Montero-Odasso
Keyword(s):  
Potential Role ◽  
Wearable Sensors ◽  
Human Gait

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 UP-Fall Detection Dataset: A Multimodal Approach

Sensors ◽  
2019 ◽  
Vol 19 (9) ◽  
pp. 1988 ◽  
Author(s):  
Lourdes Martínez-Villaseñor ◽  
Hiram Ponce ◽  
Jorge Brieva ◽  
Ernesto Moya-Albor ◽  
José Núñez-Martínez ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Community ◽  
Wearable Sensors ◽  
Fall Detection ◽  
Machine Learning Techniques ◽  
Elderly Persons ◽  
Negative Consequences ◽  
Detection Systems ◽  
Important Health ◽  
Experimental Use

Falls, especially in elderly persons, are an important health problem worldwide. Reliable fall detection systems can mitigate negative consequences of falls. Among the important challenges and issues reported in literature is the difficulty of fair comparison between fall detection systems and machine learning techniques for detection. In this paper, we present UP-Fall Detection Dataset. The dataset comprises raw and feature sets retrieved from 17 healthy young individuals without any impairment that performed 11 activities and falls, with three attempts each. The dataset also summarizes more than 850 GB of information from wearable sensors, ambient sensors and vision devices. Two experimental use cases were shown. The aim of our dataset is to help human activity recognition and machine learning research communities to fairly compare their fall detection solutions. It also provides many experimental possibilities for the signal recognition, vision, and machine learning community.

scholarly journals Freezing of gait and fall detection in Parkinson’s disease using wearable sensors: a systematic review

2017 ◽  
Vol 264 (8) ◽  
pp. 1642-1654 ◽  
Author(s):  
Ana Lígia Silva de Lima ◽  
Luc J. W. Evers ◽  
Tim Hahn ◽  
Lauren Bataille ◽  
Jamie L. Hamilton ◽  
...  
Keyword(s):  
Systematic Review ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Wearable Sensors ◽  
Freezing Of Gait ◽  
Fall Detection
Sign in / Sign up

Export Citation Format

Share Document