MAPI: Active interface pressure sensor integrated into a seat

Meffre, R.; Géhin, Claudine; Dittmar, A.

doi:10.1109/iembs.2007.4352550

Cited by 17 publications

(16 citation statements)

References 11 publications

(15 reference statements)

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“…Feature extraction from pressure maps is a common practice, e.g., pursuant to some estimation of pressure ulcer risk [13][14][15][16]. Indeed, these variables have thorough representation in the literature, including studies invoking both the linear [17][18][19] and cubic [20][21][22] interpolation. Furthermore, data can be interpolated to any degree: once (e.g., 16-31 data points per row), twice (e.g., to 61 data points), or three times (e.g., 121 × 121 data points) are all typical.…”

Section: Introductionmentioning

confidence: 99%

Effect of interpolation on parameters extracted from seating interface pressure arrays

Wininger

Crane

2014

J Rehabil Res Dev

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Abstract-Interpolation is a common data processing step in the study of interface pressure data collected at the wheelchair seating interface. However, there has been no focused study on the effect of interpolation on features extracted from these pressure maps, nor on whether these parameters are sensitive to the manner in which the interpolation is implemented. Here, two different interpolation paradigms, bilinear versus bicubic spline, are tested for their influence on parameters extracted from pressure array data and compared against a conventional low-pass filtering operation. Additionally, analysis of the effect of tandem filtering and interpolation, as well as the interpolation degree (interpolating to 2, 4, and 8 times sampling density), was undertaken. The following recommendations are made regarding approaches that minimized distortion of features extracted from the pressure maps: (1) filter prior to interpolate (strong effect); (2) use cubic interpolation versus linear (slight effect); and (3) ensure nominal difference between interpolation orders of 2, 4, and 8 times (negligible effect). We invite other investigators to perform similar benchmark analyses on their own data in the interest of establishing a community consensus of best practices in pressure array data processing.

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Section: Introductionmentioning

confidence: 99%

Effect of interpolation on parameters extracted from seating interface pressure arrays

Wininger

Crane

2014

J Rehabil Res Dev

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“…Particular to impaired mobility and immobility, much work has been completed with accelerometers or pressure sensors. This is inclusive of: monitoring and detecting sleep patterns [10], [11], monitoring and detecting gait patterns [12], [13], pressure ulcer prevention [14], [15], and 'smart home' environments [16], [17] among other research.…”

Section: Introductionmentioning

confidence: 99%

Automated assessment of mobility in bedridden patients

Bennett

Goubran

Rockwood

et al. 2013

2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

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Immobility in older patients is a costly problem for both patients and healthcare workers. The Hierarchical Assessment of Balance and Mobility (HABAM) is a clinical tool able to assess immobile patients and predict morbidity, yet could become more reliable and informative through automation. This paper proposes an algorithm to automatically determine which of three enacted HABAM scores (associated with bedridden patients) had been performed by volunteers. A laptop was used to gather pressure data from three mats placed on a standard hospital bed frame while five volunteers performed three enactments each. A system of algorithms was created, consisting of three subsystems. The first subsystem used mattress data to calculate individual sensor sums and eliminate the weight of the mattress. The second subsystem established a baseline pressure reading for each volunteer and used percentage change to identify and distinguish between two enactments. The third subsystem used calculated weight distribution ratios to determine if the data represented the remaining enactment. The system was tested for accuracy by inputting the volunteer data and recording the assessment output (a score per data set). The system identified 13 of 15 sets of volunteer data as expected. Examination of these results indicated that the two sets of data were not misidentified; rather, the volunteers had made mistakes in performance. These results suggest that this system of algorithms is effective in distinguishing between the three HABAM score enactments examined here, and emphasizes the potential for pervasive computing to improve traditional healthcare.

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“…When designing a wheelchair cushion, there is a great deal of data in support of the importance of reducing pressure over the ischial tuberosities as much as possible by redistributing it more homogenously over the buttocks through maximizing the contact surface at the buttockeseat interface [14,24,29,45,62,63].…”

Section: Introductionmentioning

confidence: 99%