scholarly journals eHomeSeniors Dataset: An Infrared Thermal Sensor Dataset for Automatic Fall Detection Research

Sensors ◽  
2019 ◽  
Vol 19 (20) ◽  
pp. 4565 ◽  
Author(s):  
Fabián Riquelme ◽  
Cristina Espinoza ◽  
Tomás Rodenas ◽  
Jean-Gabriel Minonzio ◽  
Carla Taramasco
Keyword(s):  
Older Adults ◽  
Young People ◽  
Elderly Care ◽  
Fall Detection ◽  
The Elderly ◽  
Research Area ◽  
Wearable Devices ◽  
Learning Approaches ◽  
Negative Consequences ◽  
Public Datasets

Automatic fall detection is a very active research area, which has grown explosively since the 2010s, especially focused on elderly care. Rapid detection of falls favors early awareness from the injured person, reducing a series of negative consequences in the health of the elderly. Currently, there are several fall detection systems (FDSs), mostly based on predictive and machine-learning approaches. These algorithms are based on different data sources, such as wearable devices, ambient-based sensors, or vision/camera-based approaches. While wearable devices like inertial measurement units (IMUs) and smartphones entail a dependence on their use, most image-based devices like Kinect sensors generate video recordings, which may affect the privacy of the user. Regardless of the device used, most of these FDSs have been tested only in controlled laboratory environments, and there are still no mass commercial FDS. The latter is partly due to the impossibility of counting, for ethical reasons, with datasets generated by falls of real older adults. All public datasets generated in laboratory are performed by young people, without considering the differences in acceleration and falling features of older adults. Given the above, this article presents the eHomeSeniors dataset, a new public dataset which is innovative in at least three aspects: first, it collects data from two different privacy-friendly infrared thermal sensors; second, it is constructed by two types of volunteers: normal young people (as usual) and performing artists, with the latter group assisted by a physiotherapist to emulate the real fall conditions of older adults; and third, the types of falls selected are the result of a thorough literature review.

scholarly journals Should the family or the state provide support for the elderly people? Findings from a two-generational Finnish study

2019 ◽  
Vol 39 (1/2) ◽  
pp. 138-155 ◽  
Author(s):  
Antti O. Tanskanen ◽  
Johanna Kallio ◽  
Mirkka Danielsbacka
Keyword(s):  
Older Adults ◽  
Elderly People ◽  
Elderly Care ◽  
The Elderly ◽  
The State ◽  
Younger Adults ◽  
Content Type ◽  
Public Opinions ◽  
The Family ◽  
Nationally Representative

Purpose The purpose of this paper is to investigate public opinions towards elderly care. The authors analysed respondents’ opinions towards financial support, practical help and care for elderly people. Design/methodology/approach The authors used nationally representative data collected in Finland in 2012. Respondents represent an older generation (born between 1945 and 1950, n=1,959) and their adult children (born between 1962 and 1993, n=1,652). Findings First, the authors compared the opinions of older and younger Finns but did not find that older adults were more likely than younger adults support the state responsibility, or vice versa. It was also when only actual parent-child dyads (n=779) from same families were included. Next, the authors found that several socioeconomic and family-related variables were associated with public opinions of elderly care in both generations. For instance, in both generations lower-income individuals supported the state’s responsibility more compared to their better-off counterparts. Originality/value The study provides important knowledge on attitudes towards elderly care using unique two-generational data of younger and older adults.

scholarly journals A Simulator to Support Machine Learning-Based Wearable Fall Detection Systems

Electronics ◽  
2020 ◽  
Vol 9 (11) ◽  
pp. 1831
Author(s):  
Armando Collado-Villaverde ◽  
Mario Cobos ◽  
Pablo Muñoz ◽  
David F. Barrero
Keyword(s):  
Machine Learning ◽  
Elderly Population ◽  
Real Data ◽  
Fall Detection ◽  
Wearable Devices ◽  
Accelerometer Data ◽  
Detection Systems ◽  
Detection Software ◽  
Health Complications ◽  
Public Datasets

People’s life expectancy is increasing, resulting in a growing elderly population. That population is subject to dependency issues, falls being a problematic one due to the associated health complications. Some projects are trying to enhance the independence of elderly people by monitoring their status, typically by means of wearable devices. These devices often feature Machine Learning (ML) algorithms for fall detection using accelerometers. However, the software deployed often lacks reliable data for the models’ training. To overcome such an issue, we have developed a publicly available fall simulator capable of recreating accelerometer fall samples of two of the most common types of falls: syncope and forward. Those simulated samples are like real falls recorded using real accelerometers in order to use them later as input for ML applications. To validate our approach, we have used different classifiers over both simulated falls and data from two public datasets based on real data. Our tests show that the fall simulator achieves a high accuracy for generating accelerometer data from a fall, allowing to create larger datasets for training fall detection software in wearable devices.

scholarly journals Elderly Fall Detection with an Accelerometer Using Lightweight Neural Networks

Electronics ◽  
2019 ◽  
Vol 8 (11) ◽  
pp. 1354 ◽  
Author(s):  
Gaojing Wang ◽  
Qingquan Li ◽  
Lei Wang ◽  
Yuanshi Zhang ◽  
Zheng Liu
Keyword(s):  
Neural Networks ◽  
Computational Cost ◽  
Fall Detection ◽  
The Elderly ◽  
Research Problem ◽  
Wearable Devices ◽  
The Past ◽  
Machine Learning Methods ◽  
Open Research ◽  
Different Types

Falls have been one of the main threats to people’s health, especially for the elderly. Detecting falls in time can prevent the long lying time, which is extremely fatal. This paper intends to show the efficacy of detecting falls using a wearable accelerometer. In the past decade, the fall detection problem has been extensively studied. However, since the hardware resources of wearable devices are limited, designing highly accurate embeddable models with feasible computational cost remains an open research problem. In this paper, different types of shallow and lightweight neural networks, including supervised and unsupervised models are explored to improve the fall detection results. Experiment results on a large open dataset show that the lightweight neural networks proposed have obtained much better results than machine learning methods used in previous work. Moreover, the storage and computation requirements of these lightweight models are only a few hundredths of deep neural networks in literature. In tested lightweight neural networks, the best one is proved to be the supervised convolutional neural network (CNN) that can achieve an accuracy beyond 99.9% with only 441 parameters. Its storage and computation requirements are only 1.2 KB and 0.008 MFLOPs, which make it more suitable to be implemented in wearable devices with restricted memory size and computation power.

scholarly journals Attitude of Medical Sciences Students toward Older Adults in Iran, 2017

2019 ◽  
Vol 12 (1) ◽  
pp. 40-45 ◽  
Author(s):  
Yadollah Abolfathi Momtaz ◽  
Fatemeh Mousavi-Shirazi ◽  
Parisa Mollaei ◽  
Ahmad Delbari
Keyword(s):  
Older Adults ◽  
Health Care Services ◽  
Elderly Care ◽  
The Elderly ◽  
Medical Sciences ◽  
Field Of Study ◽  
Research Activity ◽  
Cross Sectional ◽  
Effective Interventions ◽  
The Mean

Background: Studies show as people age, demand for health care services rises. One of the most important factors that significantly affect the quality of elderly care is the attitude towards older adults. Objective: The current study aimed to assess the attitude of medical sciences students towards older adults in Iran. Methods: A cross-sectional design study using a multistage proportional random sampling method was employed to obtain a sample of 583 Iranian medical sciences students in 2017. The data were measured using the Kogan's Attitudes Towards Older People Scale (KAOPS). The SPSS 23.0 for Windows (IBM SPSS Statistics 23.0) was used to analyze the data. Results: Out of the 583 respondents, around 44% were female and a little more than one-fourth was medical students. The mean age was 21.98 (SD=3.63) years. The mean score of the attitude towards the elderly was found to be 56.90 (SD=8.04). Aging health students scored a more positive attitude towards elderly people than other medical sciences students. Results of the bivariate analyses revealed that field of study (F (7, 575) = 2.66, P<0.01), participating in gerontology and geriatrics research (t (581) =2.80, p<0.01), and attending in gerontology and geriatrics congress (t (581) =1.96, p<0.05) significantly associated with attitude toward older adults. Conclusions: The findings from the current study show that Iranian medical sciences students have moderate positive attitudes towards older adults and vary by students’ field of study and their research activity in gerontology and geriatrics field. Therefore, effective interventions for enhancing the attitudes of medical sciences students towards older adults should be developed and implemented.

scholarly journals Medgeneracijsko sodelovanje: interes mladih za delo in pomoč starostnikom

2021 ◽  
Author(s):  
Goran Vukovič ◽  
Andrej Raspor ◽  
Nuša Erman ◽  
Bojan Macuh
Keyword(s):  
Young People ◽  
Older People ◽  
Elderly Care ◽  
Institutional Care ◽  
The Elderly ◽  
Ageing Population ◽  
Social Gerontology ◽  
Professional Career ◽  
Homes For The Elderly ◽  
Trained Staff

The aim of the research is to present an interest of young people in giving help to the elderly through institutional and non-institutional care. We live in a time when global and consequently also Slovenian society became strongly aware of importance of the elderly as one of its consisting part. So, it has to be stressed that additional study programmes should be introduced which will bring education in various fields of social gerontology. This need was particularly emphasized during the COVID-19 epidemic, when all homes for the elderly faced the lack of trained staff. The aim of the paper is examination of a topic summarized in a questionnaire which was used to find out how well present and future students know problems of older people and their ways of life. We also asked them, whether they would be willing to dedicate their professional career to dealing with ageing population. We realised that young people know that work with the elderly is strenous. They are acquainted with problems of ageing and ways of older people living. Furthermore, they are aware that dealing with the elderly requires much benevolence, empathy and personal respect to other people. It is recommended that offer of education in a field of elderly care gets improved and upgraded. It would lead to a higher number of young people who would decide to enrol into educational programmes of social gerontology.

scholarly journals Playability and Player Experience in Digital Games for Elderly: A Systematic Literature Review

Sensors ◽  
2020 ◽  
Vol 20 (14) ◽  
pp. 3958
Author(s):  
Antonio Rienzo ◽  
Claudio Cubillos
Keyword(s):  
Older Adults ◽  
Literature Review ◽  
Systematic Literature Review ◽  
Healthy Aging ◽  
Digital Games ◽  
The Elderly ◽  
Research Area ◽  
Psychomotor Skills ◽  
Player Experience ◽  
Exercise Promotion

A higher number of people increasingly uses digital games. This growing interest in games, with different objectives, justifies the investigation of some aspects and concepts involved, such as product quality (game), usability, playability, and user or player experience, topics investigated by the multidisciplinary area called Human–Computer Interaction (HCI). Although the majority of users of these games are children and young people, an increasing number of older adults join technology and use different types of digital games. Several studies establish the increase in learning, socialization and exercise promotion, and cognitive and psychomotor skills improvement, all within the context of active and healthy aging. The objective of this work is to carry out a systematic literature review investigating the player experience of the elderly in digital games. The work allowed answering five research questions that were formulated. The evolution and maturity level of the research area are studied together with the research methods used. The factors that motivate adults to play were also analyzed; what are the recommended technical characteristics for games and some tools and metrics with which games are evaluated for older adults? Research gaps were detected in the area; there are not many specific studies on playability and player experience applied to the older adult, nor are there proven tools and metrics to evaluate them. Particular techniques for assessing and designing games focused on older adults are lacking, and quantitative studies that better identify the factors that affect the playability and experience of older adults in digital games.

scholarly journals Fall Detection System-Based Posture-Recognition for Indoor Environments

2021 ◽  
Vol 7 (3) ◽  
pp. 42
Author(s):  
Abderrazak Iazzi ◽  
Mohammed Rziza ◽  
Rachid Oulad Haj Thami
Keyword(s):  
Detection System ◽  
Head Injuries ◽  
Fall Detection ◽  
The Elderly ◽  
Support Vector ◽  
Svm Classifier ◽  
Indoor Environments ◽  
Risk Of Death ◽  
Senior Population ◽  
Public Datasets

The majority of the senior population lives alone at home. Falls can cause serious injuries, such as fractures or head injuries. These injuries can be an obstacle for a person to move around and normally practice his daily activities. Some of these injuries can lead to a risk of death if not handled urgently. In this paper, we propose a fall detection system for elderly people based on their postures. The postures are recognized from the human silhouette which is an advantage to preserve the privacy of the elderly. The effectiveness of our approach is demonstrated on two well-known datasets for human posture classification and three public datasets for fall detection, using a Support-Vector Machine (SVM) classifier. The experimental results show that our method can not only achieves a high fall detection rate but also a low false detection.

scholarly journals A Novel Artificial Intelligence Based Internet of Things for Fall Detection of Elderly Care Monitoring

2021 ◽  
pp. 18-31
Author(s):  
Gopinath Gopinath ◽  
◽  
◽  
◽  
◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Elderly Care ◽  
Recognition Rate ◽  
Model Performance ◽  
Fall Detection ◽  
The Elderly ◽  
Severe Injuries ◽  
Independent Life ◽  
Early Fall ◽  
Post Traumatic

A fall of an older adult often leads to severe injuries and is found to be a significant reason for the death due to post-traumatic complications. Many falls happen in the home atmosphere and prevail unrecognized. Thus, the need for reliable early fall detection is necessary for fast help. Lately, the emergence of wearables, smartphones, IoT, etc., made it possible to develop systems fall detection which aids in the remote monitoring of the elderly. The goal is to allow intelligent algorithms and smartphones to detect falls for elderly care and to monitor them regularly. This work presents the Artificial Intelligence of Things for Fall Detection (AIOTFD) system using a slime mould algorithm (SMA) to optimize the final data. The features extracted using SqueezeNet further CNN based SMA used for data optimization. The validation of the AIOTFD model performance is evaluated through the Multiple Cameras Fall Dataset (MCFD) and UR Fall Detection dataset (URFD). The empirical results accentuated the assuring realization of the model compared to other state-of the art methods.The obtained results shows our proposed AIOTFD attains accuracy of 99.82% and 99.79% and databases can be used for additional investigation and optimizations to increase the recognition rate to enhance the independent life of the elderly.

scholarly journals The Impact of Age Difference on the Efficacy and Safety of COVID-19 Vaccines: A Systematic Review and Meta-Analysis

2021 ◽  
Vol 12 ◽  
Author(s):  
Jiting Wang ◽  
Yue Tong ◽  
Duo Li ◽  
Jun Li ◽  
Yaling Li
Keyword(s):  
Older Adults ◽  
Young Adults ◽  
Young People ◽  
Publication Bias ◽  
Meta Analysis ◽  
Geometric Mean ◽  
The Elderly ◽  
Age Difference ◽  
Efficacy And Safety ◽  
The Impact

ObjectiveThis meta-analysis compared the efficacy and safety of five kinds of COVID-19 vaccines in different age groups (young adults and older adults), aiming to analyze the difference of adverse events (AEs) rate and virus geometric mean titer (GMT) values between young and older people, in order to find a specific trend, and explore the causes of this trend through meta-analysis.MethodMeta-analysis was used to analyze the five eligible articles. The modified Jadad scoring scale was used to evaluate the quality of eligible literature with a scoring system of 1 to 7. The primary endpoint of the effectiveness index was GMT. The primary endpoints of the safety index were the incidence of local AEs and systemic AEs. Stata 12.0 software was used for meta-analysis. Revman 5.0 software was used to map the risk of publication bias, and Egger’s test was used to analyze publication bias.ResultsThe GMT values of young adults were higher than older adults (SMD = 1.40, 95% CI (0.79, 2.02), P&lt;0.01). There was a higher incidence of local and systemic AEs in young people than in the elderly (OR = 1.10, 95% CI (1.08, 1.12), P&lt;0.01; OR = 1.18, 95% CI (1.14, 1.22), P&lt;0.01).ConclusionThe immune effect of young people after being vaccinated with COVID-19 vaccines was better than that of the elderly, but the safety was worse than that of old people, the most common AEs were fever, rash, and local muscle pain, which were tolerable for young people. As the AEs of the elderly were lower, they can also be vaccinated safely; the reason for the low level of GMT in the elderly was related to Immunosenescence. The vaccine tolerance of people of different ages needs to be studied continuously.

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