Evaluation of falls sensor technology in acute care


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

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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.

A hierarchical model for recognizing alarming states in a batteryless sensor alarm intervention for preventing falls in older people


Source: Pervasive and Mobile Computing

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Date of publication: 10th April 2017

 Publication type: Journal article

In a nutshell: Falls are common among older people, especially in hospitals and nursing homes. The combination of pervasive sensing and statistical learning methods is creating new possibilities for automatic monitoring of activities of hospitalized older people to provide targeted and timely supervision by clinical staff to reduce falls. In this paper we introduce a hierarchical conditional random fields model to predict alarming states (being out of the bed or chair) from a passive wearable embodiment of a sensor worn over garment to provide an intervention mechanism to reduce falls. Our approach predicts alarm states in real time and avoids the use of empirically determined heuristics methods alone or in combination with machine learning based models, or multiple cascaded classifiers for generating alarms from activity prediction streams. Instead, the proposed hierarchical approach predicts alarms based on learned relationships between alarms, sensor information and predicted low-level activities. We evaluate the performance of the approach with 14 healthy older people and 26 hospitalized older patients and demonstrate similar or better performance than machine learning based approaches combined with heuristics based methods.

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Sensors on hospital wards as patient falls increase


Source: Hereford Times

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Date of publication: 11th December 2014

Publication type: news article

In a nutshell: The use of sensors at Leominster Community Hospital for patients most at risk of falls.

Length of publication: one page

Developing a standard data format from body-sensor signals


Source: Zeitschrift für Gerontologie und Geriatrie, 2013, 46 (8) p. 720-726

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Date of publication: December 2013

Publication type: Journal article

In a nutshell: Bodily-worn sensor devices can provide real-time and objective data about falls in older people which will increase understanding of them and help technology to automatically recognise them. The FARSEEING consortium with associated partners is trying to build a meta-database using standardised data to combine different sources and guarantee data quality.

Length of publication: 6 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.