Postural management system for bedbound patients

Objectives To explore the potential effectiveness of postural management system considering peak contact pressure and user perceptions. Methods Fifteen healthy participants were screened using a modified Red Flags Screening tool. Conformat® system was used to analyze contact pressure under the shoulder and buttocks and was recorded for 10 minutes in supine and side-lying positions with and without a postural management system. Participants were asked about their comfort and restrictiveness using a numerical rating scale. Results In side-lying position, the peak contact pressure at greater trochanter was significantly lower when a postural management system was applied. In supine position, the peak contact pressure at shoulders was respectively lower. In turn, the peak contact pressure at ischial tuberosity was significantly higher lower when a postural management system was applied. The postural management system did not affect the level of perceived comfort. Participants reported that they felt more restricted with the intervention. Conclusions A postural management system reduced pressure at the shoulders in supine-lying position and at the greater trochanter in side-lying position lowering the risk of pressure injury formation. A postural management system may reduce the economic burden of health problems associated with poor positioning, enhance patient care, and reduce the risks associated with manual handling techniques when repositioning

Wearable accelerometer based extended sleep position recognition

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI linkSleep positions have an impact on sleep quality and therefore need to be further analyzed. Current research on position tracking includes only the four basic positions. In the context of wearable devices, energy efficiency is still an open issue. This research presents a way to detect eight positions with higher granularity under energy efficient constraints. Generalized Matrix Learning Vector Quantization is used, as it is a fast and appropriate method for environments with limited computation resources, and has not been seen for this kind of application before. The overall model trained on individuals performs with an averaged accuracy of 99.8%, in contrast to an averaged accuracy of 83.62% for grouped datasets. Real world application gives an accuracy of around 98%. The results show that energy efficiency will be feasible, as performance stays similar for lower sampling rate. This is a step towards a mobile solution which gives more insight in person's sleep behaviour