Development and pilot study of a bed-exit alarm based on a body-worn accelerometer

Wolf, Klaus-Hendrik; Hetzer, K.; Schwabedissen, H. Meyer zu; Wiese, Birgitt; Marschollek, Michael

doi:10.1007/s00391-013-0560-2

Cited by 27 publications

(50 citation statements)

References 16 publications

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“…To realize these advantages, the RFID sensor platform does require an initial investment for the RFID infrastructure compared to battery-powered wireless sensors used in [35], [42] and [15]. For instance, RoomSet2 configuration cost is approximately 1200 USD, which is for a 4 port RFID reader and 3 RFID antennas.…”

Section: Discussionmentioning

confidence: 98%

“…RFID infrastructure). Since W 2 ISP is inexpensive (3 USD per unit [21], [44]) and maintenance-free compared to battery-powered wireless sensors (≈300 USD per unit) [15], [35], [42], a lower recurrent cost is expected. Unlike other wireless sensor deployments, the RFID infrastructure can also be used in other application scenarios such as patient tracking and hospital asset management.…”

Section: Discussionmentioning

confidence: 99%

“…Unlike methods employed in previous studies [8], [12], [15] where the sensor was strapped to a patient, we considered a more comfortable attachment method where the sensor was more likely to be acceptable by older people [21]. In our approach, the sensor was attached to a loosely worn garment over the sternum.…”

Section: Introductionmentioning

confidence: 96%

“…Recently, Wolf et al [15] carried out a small (underpowered) clinical trial in a geriatric ward and demonstrated that these kinds of kinematic sensors (body-worn, battery-powered with threshold-based classification techniques) can be used to recognize bed-egress movements when strapped to the patient's thighs with bandages. Unfortunately no quantitative data was provided regarding the performance of the sensor system.…”

Section: Introductionmentioning

confidence: 98%

“…Response delays have been eliminated in some studies [9], [14] based on machine learning classification techniques, but none has specifically investigated bed-egress recognition. Furthermore, most of these studies [8]- [12], [14], [15] have utilized heavy battery-powered sensors strapped to a patient's body. Additionally, some researchers have considered using multiple sensors in the same platform [8] and sensors attached to different parts of a participant's body [9], [14] to collect rich sets of data for activity recognition.…”

Section: Introductionmentioning

confidence: 99%

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Sequence Learning with Passive RFID Sensors for Real-Time Bed-Egress Recognition in Older People

Wickramasinghe

Ranasinghe

Visvanathan

2017

IEEE J. Biomed. Health Inform.

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Getting out of bed and ambulating without supervision is identified as one of the major causes of patient falls in hospitals and nursing homes. Therefore, increased supervision is proposed as a key strategy toward falls prevention. An emerging generation of batteryless, lightweight, and wearable sensors are creating new possibilities for ambulatory monitoring, where the unobtrusive nature of such sensors makes them particularly adapted for monitoring older people. In this study, we investigate the use of a batteryless radio-frequency identification (RFID) tag response to analyze bed-egress movements. We propose a bed-egress movement detection framework that includes a novel sequence learning classifier with a set of features derived from bed-egress motion analysis. We analyzed data from 14 healthy older people (66-86 years old) who wore a wearable embodiment of a batteryless accelerometer integrated RFID sensor platform loosely attached over their clothes at sternum level, and undertook a series of activities including bed-egress in two clinical room settings. The promising results indicate the efficacy of our batteryless bed-egress monitoring framework.

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Section: Discussionmentioning

confidence: 98%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 96%

Section: Introductionmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Sequence Learning with Passive RFID Sensors for Real-Time Bed-Egress Recognition in Older People

Wickramasinghe

Ranasinghe

Visvanathan

2017

IEEE J. Biomed. Health Inform.

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Interventions for preventing falls in older people in care facilities and hospitals

Cameron

Dyer²,

Panagoda

et al. 2018

Cochrane Database of Systematic Reviews

355

370

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Background Falls in care facilities and hospitals are common events that cause considerable morbidity and mortality for older people. This is an update of a review first published in 2010 and updated in 2012. Objectives To assess the effects of interventions designed to reduce the incidence of falls in older people in care facilities and hospitals. Search methods We searched the Cochrane Bone, Joint and Muscle Trauma Group Specialised Register (August 2017); Cochrane Central Register of Controlled Trials (2017, Issue 8); and MEDLINE, Embase, CINAHL and trial registers to August 2017. Selection criteria Randomised controlled trials of interventions for preventing falls in older people in residential or nursing care facilities, or hospitals. Data collection and analysis One review author screened abstracts; two review authors screened full‐text articles for inclusion. Two review authors independently performed study selection, 'Risk of bias' assessment and data extraction. We calculated rate ratios (RaR) with 95% confidence intervals (CIs) for rate of falls and risk ratios (RRs) and 95% CIs for outcomes such as risk of falling (number of people falling). We pooled results where appropriate. We used GRADE to assess the quality of evidence. Main results Thirty‐five new trials (77,869 participants) were included in this update. Overall, we included 95 trials (138,164 participants), 71 (40,374 participants; mean age 84 years; 75% women) in care facilities and 24 (97,790 participants; mean age 78 years; 52% women) in hospitals. The majority of trials were at high risk of bias in one or more domains, mostly relating to lack of blinding. With few exceptions, the quality of evidence for individual interventions in either setting was generally rated as low or very low. Risk of fracture and adverse events were generally poorly reported and, where reported, the evidence was very low‐quality, which means that we are uncertain of the estimates. Only the falls outcomes for the main comparisons are reported here. Care facilities Seventeen trials compared exercise with control (typically usual care alone). We are uncertain of the effect of exercise on rate of falls (RaR 0.93, 95% CI 0.72 to 1.20; 2002 participants, 10 studies; I² = 76%; very low‐quality evidence). Exercise may make little or no difference to the risk of falling (RR 1.02, 95% CI 0.88 to 1.18; 2090 participants, 10 studies; I² = 23%; low‐quality evidence). There is low‐quality evidence that general medication review (tested in 12 trials) may make little or no difference to the rate of falls (RaR 0.93, 95% CI 0.64 to 1.35; 2409 participants, 6 studies; I² = 93%) or the risk of falling (RR 0.93, 95% CI 0.80 to 1.09; 5139 participants, 6 studies; I² = 48%). There is moderate‐quality evidence that vitamin D supplementation (4512 particip...

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Approaches for Falls Prevention in Hospitals and Nursing Home Settings

Zanker

Duque

2019

Falls and Cognition in Older Persons

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Development and pilot study of a bed-exit alarm based on a body-worn accelerometer

Cited by 27 publications

References 16 publications

Sequence Learning with Passive RFID Sensors for Real-Time Bed-Egress Recognition in Older People

Sequence Learning with Passive RFID Sensors for Real-Time Bed-Egress Recognition in Older People

Interventions for preventing falls in older people in care facilities and hospitals

Approaches for Falls Prevention in Hospitals and Nursing Home Settings

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