Abstract
Falls in hospitals are common, therefore strategies to minimize the impact of these events in older patients and needs to be examined. In this pilot study, we investigate a movement monitoring sensor system for identifying bed and chair exits using a wireless wearable sensor worn by hospitalized older patients. We developed a movement monitoring sensor system that recognizes bed and chair exits. The system consists of a machine learning based activity classifier and a bed and chair exit recognition process based on an activity score function. Twenty-six patients, aged 71 to 93 years old, hospitalized in the Geriatric Evaluation and Management Unit participated in the supervised trials. They wore over their attire a battery-less, lightweight and wireless sensor and performed scripted activities such as getting off the bed and chair. We investigated the system performance in recognizing bed and chair exits in hospital rooms where RFID antennas and readers were in place. The system’s acceptability was measured using two surveys with 0–10 likert scales. The first survey measured the change in user perception of the system before and after a trial; the second survey, conducted only at the end of each trial, measured user acceptance of the system based on a multifactor sensor acceptance model. The performance of the system indicated an overall recall of 81.4%, precision of 66.8% and F-score of 72.4% for joint bed and chair exit recognition. Patients demonstrated improved perception of the system after use with overall score change from 7.8 to 9.0 and high acceptance of the system with score ≥ 6.7 for all acceptance factors. The present pilot study suggests the use of wireless wearable sensors is feasible for detecting bed and chair exits in a hospital environment.
Highlights
Falls are the leading cause of preventable injuries in hospitalized older people, especially those with dementia or delirium, where 30% of falls result in injury, and 4–6% in serious injury including death [1]
We compare with our study in [36], where we used a dynamically weighted CRF model [53] to classify activities for the recognition of bed and chair exits as in Fig 4(b), using the features developed in this research to make a fair comparison
We have determined that the median delay for the recognition of bed and chair exits is of 4.09 s; the mode of all delays, rounded to the nearest second, is of 4 s
Summary
Falls are the leading cause of preventable injuries in hospitalized older people, especially those with dementia or delirium, where 30% of falls result in injury, and 4–6% in serious injury including death [1]. Falls are reported to commonly occur in patients’ rooms (84%) including those around the bed and chair [6, 7] or in the toilet (11%); in terms of activities at the time of the fall, most falls occur when ambulating (19%), especially without the necessary walking aid [1]. Best practice standards for falls prevention in hospitals and nursing homes include the use of safe footwear, review of medications or use of bed and chair exit alarm systems for patients at risk of falling to provide timely alerts to staff to lend assistance to patients attempting to ambulate unsupervised [10]. Falls rates remain high [11,12,13]
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