Deriving Multiple-Layer Information from a Motion-Sensing Mattress for Precision Care

Abstract

Bed is often the personal care unit in hospitals, nursing homes, and individuals’ homes. Rich care-related information can be derived from the sensing data from bed. Patient fall is a significant issue in hospitals, many of which are related to getting in and/or out of bed. To prevent bed falls, a motion-sensing mattress was developed for bed-exit detection. A machine learning algorithm deployed on the chip in the control box of the mattress identified the in-bed postures based on the on/off pressure pattern of 30 sensing areas to capture the users’ bed-exit intention. This study aimed to explore how sleep-related data derived from the on/off status of 30 sensing areas of this motion-sensing mattress can be used for multiple layers of precision care information, including wellbeing status on the dashboard and big data analysis for living pattern clustering. This study describes how multiple layers of personalized care-related information are further derived from the motion-sensing mattress, including real-time in-bed/off-bed status, daily records, sleep quality, prolonged pressure areas, and long-term living patterns. Twenty-four mattresses and the smart mattress care system (SMCS) were installed in a dementia nursing home in Taiwan for a field trial. Residents’ on-bed/off-bed data were collected for 12 weeks from August to October 2021. The SMCS was developed to display care-related information via an integrated dashboard as well as sending reminders to caregivers when detecting events such as bed exits and changes in patients’ sleep and living patterns. The ultimate goal is to support caregivers with precision care, reduce their care burden, and increase the quality of care. At the end of the field trial, we interviewed four caregivers for their subjective opinions about whether and how the SMCS helped their work. The caregivers’ main responses included that the SMCS helped caregivers notice the abnormal situation for people with dementia, communicate with family members of the residents, confirm medication adjustments, and whether the standard care procedure was appropriately conducted. Future studies are suggested to focus on integrated care strategy recommendations based on users’ personalized sleep-related data.

A Lifestyle Monitoring System for Older Adults Living Independently Using Low-Resolution Smart Meter Data

“…Sleep data are sourced from the user’s home using the WhizPad system [ 40 ], while vital sign data are obtained from dedicated monitoring devices transmitted to a gateway through BLE and stored in a database, with access to the data facilitated through APIs. Data collected from smart meters yield two indices: activity and regularity.…”

“…Data collected from smart meters yield two indices: activity and regularity. Sleep data encompass five indices, the total time in bed, sleep continuity, sleep efficiency, prolonged pressure time, and the total sleep time, which are compared to those of the two-week norm [ 40 ]. A vital sign system also provides indices for the heart rate, respiratory rate, blood pressure, and blood oxygen levels.…”

Motion Detectors as Additional Monitoring Devices in the Intensive Care Unit—A Proof-of-Concept Study

“…These mattresses are mainly used for geriatric or nursing home patients. Although smart mattresses may also have other indications at the ICU, such as preventing pressure ulcers [39,40], feasibility and cost issues may limit their use in the ICU. As many patients are not delirious at admission but develop delirium at the ICU [41], affected individuals would need a bed or mattress change, which may be more labor-intensive in an uncooperative patient than the attachment of a portable motion detector underneath the bed or pillow.…”