Self-powered fall detection system using pressure sensing triboelectric nanogenerators

17/10/2017

Source: Nano Energy, 2017, Vol. 41 p. 139-147

Follow this link for the abstract

Date of publication: November 2017

Publication type: Journal article

In a nutshell: Fall detection is becoming more important as the number of older people in society increases. People may fall at home where there is little timely help available, and falls themselves can cause injuries. Most fall detection technologies are inconvenient to wear, and visual or movement-based ones can be expensive and difficult to install. This study proposes a falls-detection system based on a pressure-sensing triboelectric nanogenerator array, which is cost-effective and ambient-based. It achieves a classification accuracy of 95.75% in identifying actual falls, and can be immediately installed due to low costs.

Length of publication: 8 pages

Some important notes: Please contact your local NHS Library for the full text of the article. Follow this link to find your local NHS Library.

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Evaluation of falls sensor technology in acute care

14/07/2017

Source: The Joint Commission Journal on Quality and Patient Safety, 2017, online

Follow this link for the abstract

Date of publication: June 2017

Publication type: Journal article

In a nutshell: Sensor technology that dynamically identifies hospitalized patients’ fall risk and detects and alerts nurses of high-risk patients’ early exits out of bed has potential for reducing fall rates and preventing patient harm. In this study, a sensor was evaluated on two inpatient medical units to study fall characteristics and then to assign patient fall probability. A fall detection sensor system affords a level of surveillance that standard fall alert systems do not have. Fall prevention remains a complex issue, but sensor technology is a viable fall prevention option.

Length of publication: 1 page

Some important notes: Please contact your local NHS Library for the full text of the article. Follow this link to find your local NHS Library.


The effectiveness of intervention programs for preventing patients from falls

16/06/2017

Source: Kontakt, 2017, online

Follow this link for the abstract

Date of publication: May 2017

Publication type: Journal article

In a nutshell: This is a review article to summarise the conclusions of different studies about the effectiveness of hospital fall prevention programmes from the last five years. Twelve studies made it into the final review, and the most mentioned strategy was education of patients and staff. Effectiveness depends on factors like compliance, leadership, team training and IT support, amongst others.

Length of publication: 1 page

Some important notes: Please contact your local NHS Library for the text of the article. Follow this link to find your local NHS Library.


Effects of an ICT-based fall-prevention system in community-dwelling older adults

16/06/2017

Source: International Journal of Human-Computer Studies, 2017, Vol 106 p. 10-25

Follow this link for the abstract

Date of publication: October 2017

Publication type: Journal article

In a nutshell: A sedentary lifestyle and low levels of physical activity are major factors in fall risk for older adults. ICT-based interventions could possibly counteract the risk for this group, as studies show that such interventions significantly reduce it. However, this population is heterogeneous, and several factors (such as gender, age, fitness and others) may influence the use of these systems. This study analyses the iStoppFalls system, testing effectiveness and usage indicators, among other things.

Length of publication: 15 pages

Some important notes: Please contact your local NHS Library for the full text of the article. Follow this link to find your local NHS Library.


A hierarchical alarm model for elderly fall prevention sensors

17/05/2017

Source: Pervasive and Mobile Computing, 2017, online

Follow this link for the abstract

Date of publication: April 2017

Publication type: Journal article

In a nutshell: New technologies allow for automatic monitoring of hospitalised older people, helping clinical staff to supervise to reduce falls. This paper introduces a hierarchical model to predict alarming states in a sensor worn over clothes. The hierarchy predicts levels of danger to warn clinical staff of possible fall danger.

Length of publication: 1 page

Some important notes: Please contact your local NHS Library for the full text of the article. Follow this link to  find your local NHS Library.


Semi-supervised near-miss fall detection for ironworkers with a wearable inertial measurement unit

21/06/2016

Source: Automation in Construction, 2016, Vol 68 p. 194-202

Follow this link for the abstract

Date of publication: August 2016

Publication type: Journal article

In a nutshell: Accidental falls are the leading cause of injury and death in construction work. Near misses can provide valuable data about the causes as a proactive prevention measure, but collecting information can be challenging. This study aims to develop a method to automatically collect such data using wearable inertial measurement units, which could ultimately prevent fall accidents.

Length of publication: 8 pages

Some important notes: Please contact your local NHS Library for the full text of the article. Follow this link to find your local NHS Library.