Automated assessment of mobility in bedridden patients

Abstract

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.

In-Bed Mobility Monitoring Using Pressure Sensors

“…Lying-to-lying has been examined before in terms of pressure point relief, as it is here, but this paper expands upon this by adding 25 data sets. The lying-to-sitting algorithms are novel [25].…”

Reliable, Real-Time Acceleration Measurement Toward Automated Monitoring of Driver Behavior

“…Of particular interest of late is the idea that change in balance and mobility has been shown to be an important predictor of a change in health status, and that this effect holds even people who are confined to bed [19]. Currently, work is ongoing with the aim of discovering a method for predicting falls [91], for scoring the Hierarchical Assessment of Balance and Mobility (HABAM) [92], and for monitoring sleep quality and sleep apnea [93].…”

The Assessment Of In-Bed Mobility Using Pressure Sensitive Mats

“…The second contribution was the set of algorithms constituting the primary stage of the overall system, referred to as DIP. This stage used feature extraction and pattern classification to determine between one of two initial volunteer postures; a supine lying position and a sitting position [22], [23]. The third contribution was the set of algorithms constituting the first of two secondary stages of the overall system, referred to as DBLS.…”