scholarly journals Fall detection in the older people: from laboratory to real-life

2014 ◽  
Vol 63 (3) ◽  
pp. 253 ◽  
Author(s):  
T Jämsä ◽  
M Kangas ◽  
I Vikman ◽  
L Nyberg ◽  
R Korpelainen
Keyword(s):  
Older People ◽  
Real Life ◽  
Fall Detection

scholarly journals Real-Life/Real-Time Elderly Fall Detection with a Triaxial Accelerometer

Sensors ◽  
2018 ◽  
Vol 18 (4) ◽  
pp. 1101 ◽  
Author(s):  
Angela Sucerquia ◽  
José López ◽  
Jesús Vargas-Bonilla
Keyword(s):  
Real Time ◽  
Real Life ◽  
Fall Detection ◽  
Triaxial Accelerometer

Primary care management of older people with mental health problems

2013 ◽  
Author(s):  
Louise Robinson ◽  
Carolyn Chew-Graham
Keyword(s):  
Mental Health ◽  
Primary Care ◽  
Older People ◽  
Care Management ◽  
Mental Health Problems ◽  
Age Groups ◽  
Real Life ◽  
Health Problems ◽  
Care Systems ◽  
Primary Care Management

This chapter discusses the presentation and primary care management of the commonest mental health problems in older people; these include delirium, delusions, depression and anxiety, and dementia. Primary care is on the front line in dealing with older people who have mental health problems, supporting their families to care for them and managing people with complex co-morbidities in addition to mental health issues. Older people consult their GP almost twice as often as other age groups and up to 40% may have a mental health problem. Cases drawn from the authors’ real-life practice are presented firstly to represent clinical presentations and management within primary care and secondly to demonstrate how primary care links with secondary care and the wider services. The management of patients is discussed largely within reference to UK primary care systems and policy, but the international readership should find parallels within their own healthcare systems.

scholarly journals Involvement of older people in the development of fall detection systems: a scoping review

2016 ◽  
Vol 16 (1) ◽  
Author(s):  
Friederike JS Thilo ◽  
Barbara Hürlimann ◽  
Sabine Hahn ◽  
Selina Bilger ◽  
Jos MGA Schols ◽  
...  
Keyword(s):  
Older People ◽  
Scoping Review ◽  
Fall Detection ◽  
Detection Systems

Comparison of real-life accidental falls in older people with experimental falls in middle-aged test subjects

2012 ◽  
Vol 35 (3) ◽  
pp. 500-505 ◽  
Author(s):  
M. Kangas ◽  
I. Vikman ◽  
L. Nyberg ◽  
R. Korpelainen ◽  
J. Lindblom ◽  
...  
Keyword(s):  
Older People ◽  
Accidental Falls ◽  
Real Life ◽  
Middle Aged

scholarly journals Real-Life/Real-Time Elderly Fall Detection with a Triaxial Accelerometer

2018 ◽  
Author(s):  
Angela Sucerquia ◽  
Jose David López ◽  
Francisco Vargas-Bonilla
Keyword(s):  
False Positive Rate ◽  
Sampling Rate ◽  
Elderly Person ◽  
Real Life ◽  
Medical Personnel ◽  
Fall Detection ◽  
Target Population ◽  
Elderly Persons ◽  
Triaxial Accelerometer ◽  
Classification Feature

The consequences of a fall on an elderly person can be reduced if the accident is attended by medical personnel within the first hour. Independent elderly people use to stay alone for long periods of time, being in more risk if they suffer a fall. The literature offers several approaches for detecting falls with embedded devices or smartphones using a triaxial accelerometer. Most of these approaches were not tested with the target population, or are not feasible to be implemented in real-life conditions. In this work, we propose a fall detection methodology based on a non-linear classification feature and a Kalman filter with a periodicity detector to reduce the false positive rate. This methodology requires a sampling rate of only 25 Hz; it does not require large computations or memory and it is robust among devices. We test our approach with the SisFall dataset achieving 99.4% of accuracy. Then, we validate it with a new round of simulated activities with young adults and an elderly person. Finally, we give the devices to three elderly persons for full-day validations. They continued with their normal life and the devices behaved as expected.

scholarly journals A Case Study of Ageing People: Real Life Scenario and Future Challenges

2018 ◽  
Vol 5 (01) ◽  
Author(s):  
Vinmalar J ◽  
T. Joseph
Keyword(s):  
Older People ◽  
Detailed Analysis ◽  
Total Population ◽  
Life Support ◽  
Real Life ◽  
Aged People ◽  
Future Challenges

With people having longer lives, the share of older people in the total population is expanding rapidly, leading many retirees to face a challengeable life. Support from government and family are being reduced for the old aged people and they are left alone when they get too old. This study concentrates on real life cases of a retired person and other person who is yet to retire. A detailed analysis in this study brings out the need and precautions to be considered to have a secure retirement life for the upcoming retirees. The habit of saving is been given more importance and the time to initiate such habit has been established in this study. The study reveals out how one should be aware of upcoming events and plan accordingly to lead a peaceful retirement life.

scholarly journals Temporal patterns in multi-modal social interaction between elderly users and service robot

2019 ◽  
Vol 20 (1) ◽  
pp. 4-24 ◽  
Author(s):  
Ning Wang ◽  
Alessandro Di Nuovo ◽  
Angelo Cangelosi ◽  
Ray Jones
Keyword(s):  
Social Interaction ◽  
Older People ◽  
Social Engagement ◽  
Real Life ◽  
Human Robot Interaction ◽  
User Preference ◽  
Service Robot ◽  
Human Beings ◽  
Human Partner ◽  
Older Users

Abstract Social interaction, especially for older people living alone is a challenge currently facing human-robot interaction (HRI). There has been little research on user preference towards HRI interfaces. In this paper, we took both objective observations and participants’ opinions into account in studying older users with a robot partner. The developed dual-modal robot interface offered older users options of speech or touch screen to perform tasks. Fifteen people aged from 70 to 89 years old, participated. We analyzed the spontaneous actions of the participants, including their attentional activities and conversational activities, the temporal characteristics of these social behaviours, as well as questionnaires. It has been revealed that social engagement with the robot demonstrated by older people was no different from what might be expected towards a human partner. This study is an early attempt to reveal the social connections between human beings and a personal robot in real life.

scholarly journals A smartphone-based online system for fall detection with alert notifications and contextual information of real-life falls

2021 ◽  
Vol 18 (1) ◽  
Author(s):  
Yaar Harari ◽  
Nicholas Shawen ◽  
Chaithanya K. Mummidisetty ◽  
Mark V. Albert ◽  
Konrad P. Kording ◽  
...  
Keyword(s):  
Contextual Information ◽  
Real Life ◽  
Fall Detection ◽  
Risk Of Falling ◽  
Online System ◽  
Related Data ◽  
Event Time ◽  
Cloud Server ◽  
Fall Risks ◽  
A Prospective Study

Abstract Background Falls are a leading cause of accidental deaths and injuries worldwide. The risk of falling is especially high for individuals suffering from balance impairments. Retrospective surveys and studies of simulated falling in lab conditions are frequently used and are informative, but prospective information about real-life falls remains sparse. Such data are essential to address fall risks and develop fall detection and alert systems. Here we present the results of a prospective study investigating a proof-of-concept, smartphone-based, online system for fall detection and notification. Methods The system uses the smartphone’s accelerometer and gyroscope to monitor the participants’ motion, and falls are detected using a regularized logistic regression. Data on falls and near-fall events (i.e., stumbles) is stored in a cloud server and fall-related variables are logged onto a web portal developed for data exploration, including the event time and weather, fall probability, and the faller’s location and activity before the fall. Results In total, 23 individuals with an elevated risk of falling carried the phones for 2070 days in which the model classified 14,904,000 events. The system detected 27 of the 37 falls that occurred (sensitivity = 73.0 %) and resulted in one false alarm every 46 days (specificity > 99.9 %, precision = 37.5 %). 42.2 % of the events falsely classified as falls were validated as stumbles. Conclusions The system’s performance shows the potential of using smartphones for fall detection and notification in real-life. Apart from functioning as a practical fall monitoring instrument, this system may serve as a valuable research tool, enable future studies to scale their ability to capture fall-related data, and help researchers and clinicians to investigate real-falls.

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