Bed occupancy measurements using under mattress pressure sensors for long term monitoring of community-dwelling older adults

Taylor, Matthew J.; Grant, Theresa; Knoefel, Frank; Goubran, Rafik

doi:10.1109/memea.2013.6549721

Cited by 22 publications

(13 citation statements)

References 21 publications

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“…A key implication of our findings is that timing values obtained from pressure sensor technology will need to be interpreted in the context of distinct patterns over time and most likely in combination with other variables related to the STS movement. These variables could include time spent in bed 20 or the presence of movement patterns such as bouncing when preparing to exit the bed, which has been associated with mobility difficulties 21 .…”

Section: Discussionmentioning

confidence: 99%

“…A processing algorithm was developed in MAT-LAB version 7.12 (MathWorks, Inc. Massachusettes USA) to identify when bed exits occurred within the large sensor data set and is described in an earlier publication 20 . For the purposes of this study, the STS portion of the bed exit was calculated individually by viewing a processed pressure tracing in MATLAB.…”

Section: Pressure Sensor Processingmentioning

confidence: 99%

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Sit-To-Stand (STS) timing among older adults in their homes: Two measurement approaches

Grant¹,

Bryanton²,

Taylor³

et al. 2014

Gerontechnology

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, Sit-to-stand (STS) timing among older adults in their homes: Two measurement approaches. Gerontechnology 2014;12(3):153-158; doi:10.4017/gt.2014.12.3.006.00 Sit-to-stand (STS) times required by older adults exiting from their beds at home were compared simultaneously using under mattress pressure sensor technology and video analysis. Six older adults consented to participate in the study and had under mattress pressure technology installed in their homes. Participants were visited monthly over a period of 2-10 months by a researcher who asked them to perform three bed exits which were videotaped. The pressure sensor technology calculated a portion of STS time based on changes in pressure between the end of stable sitting on the edge of the bed and pressure leaving the bed. The video analysis approach calculated this corresponding initial phase of the same STS transfer by determining the difference between the first forward movement from the quiet sitting position and the initiation of vertical rise. The remaining period of vertical rise into a complete standing position was calculated using video analysis but not pressure sensor technology. A Spearman's rank order correlation analysis revealed that there was a moderate, positive correlation between the video and pressure-sensor measures of the initial phase of STS which was statistically significant (r s =0.50, n=97, p<0.001). Wilcoxon signed-rank tests indicated that pressure sensor times were longer (Z=-7.91, p<0.000) and more variable than video times (Z=-3.14, p=0.002). Analysis of video data revealed that this initial phase of STS represented between 15 to 81% of the total STS time among the 97 sampled STS in this study. Results highlight how the home environment presents a number of conditions affecting differences in STS values obtained by each of these measurement approaches.

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Section: Discussionmentioning

confidence: 99%

Section: Pressure Sensor Processingmentioning

confidence: 99%

Sit-To-Stand (STS) timing among older adults in their homes: Two measurement approaches

Grant¹,

Bryanton²,

Taylor³

et al. 2014

Gerontechnology

Self Cite

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“…A single day of recording created approximately 500MB of data. The algorithm used down sampling techniques to quickly scan the pressure recordings and detect activity as described in a previous paper [12]. For the purpose of this study the algorithm was designed to detect the first morning exit.…”

Section: A Technologymentioning

confidence: 99%

Measuring sit-to-stand timing variability over time using under mattress pressure sensor technology

Grant

Joshi

Taylor

et al. 2014

2014 IEEE International Symposium on Medical Measurements and Applications (MeMeA)

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Abstract-Getting out of bed is a fundamental activity of daily living and one that supports independent living. Under mattress pressure sensor technology represents a way to monitor changes in this basic but critical mobility task among older adults at risk of institutionalization. However, little is known about normal variations in this ability over time in the home context. This study used under mattress pressure sensors to measure and analyze the variability of sit-to-stand (STS) timing in a community-dwelling older adult. A pressure-sensitive mat was installed in the participant's home and left in place to collect information over a period of nine months. A processing algorithm was developed to extract the STS phase of the first morning bed exit from which STS time could be measured. STS timing data were visualized using a histogram and analyzed for trends over the extended period using nonparametric regression and wavelet analysis. Results indicate that the analytical methods used were able to identify trends in STS timing as well as highlight deviations. The ability to collect and analyze the variability of STS timing using this pressure sensitive technology combined with the analysis methodology provides clinicians with a way to assess mobility remotely in the home setting.

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“…Other than the clinical environment, PSM technology has applications in other domains such as ergonomics, automotive technology, and civil engineering [46]- [48]. In the medical sector, PSM have been used for gait analysis, concussion assessment, and patient monitoring [49]- [51]. [52].…”

Section: Pressure Sensitive Matsmentioning

confidence: 99%

Neonatal Respiratory Rate Monitoring Using a Pressure-Sensitive Mat

Bekele¹

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Pressure measurement using a Pressure Sensitive Mat (PSM) enables a non-contact approach for monitoring patient vital signs such as respiration rate (RR) and heart rate (HR). Non-contact patient monitoring in Neonatal Intensive Care Units (NICU) can improve the quality of patient care and reduce patient discomfort. This thesis investigates the applicability of PSM for monitoring of neonatal patient respiration rate in the NICU.A clinical trial was conducted with the Children's Hospital of Eastern Ontario (CHEO) collecting PSM data from 15 patients in three different bed types. Gold standard RR and annotations of patient movement and interventions were collected simultaneously.Algorithms for estimating RR were developed by optimizing spatial downsampling, reference signal selection, enhancement, and combining. The RR estimation results were evaluated against gold standard estimates from a commercial patient monitor. The best RR estimation method worked well when the patient is on ventilator support achieving a mean absolute error (MAE) of 3.9 +/-6.6 bpm on clean data without patient movements or interventions. For this patient, 89.7% of RR estimates were within the clinically acceptable range of +/-10 bpm, while 78.4% fell within the more stringent limits of +/-5 bpm.When the RR estimation algorithm is evaluated on all patients, RR estimation performance is mixed. In particular, very low mass neonates and patients laying on multiple blankets presented challenges for RR estimation. Taken together, a 95% confidence interval (CI) for agreement between the estimated and gold standard RR was estimated to be [-44.8, 51.9] bpm with a mean bias of 3.5 bpm, using a mixed effects limits of agreement (LoA)

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Bed occupancy measurements using under mattress pressure sensors for long term monitoring of community-dwelling older adults

Cited by 22 publications

References 21 publications

Sit-To-Stand (STS) timing among older adults in their homes: Two measurement approaches

Sit-To-Stand (STS) timing among older adults in their homes: Two measurement approaches

Measuring sit-to-stand timing variability over time using under mattress pressure sensor technology

Neonatal Respiratory Rate Monitoring Using a Pressure-Sensitive Mat

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