A battery-less and wireless wearable sensor system for identifying bed and chair exits in a pilot trial in hospitalized older people.

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.

Framework for preventing falls in acute hospitals using passive sensor enabled radio frequency identification technology.

We describe a distributed architecture for a real-time falls prevention framework capable of providing a technological intervention to mitigate the risk of falls in acute hospitals through the development of an AmbIGeM (Ambient Intelligence Geritatric Management system). Our approach is based on using a battery free, wearable sensor enabled Radio Frequency Identification device. Unsupervised classification of high risk falls activities are used to facilitate an immediate response from caregivers by alerting them of the high risk activity, the particular patient, and their location. Early identification of high risk falls activities through a longitudinal and unsupervised setting in real-time allows the preventative intervention to be administered in a timely manner. Furthermore, real-time detection allows emergency protocols to be deployed immediately in the event of a fall. Finally, incidents of high risk activities are automatically documented to allow clinicians to customize and optimize the delivery of care to suit the needs of patients identified as being at most risk.

Towards falls prevention: a wearable wireless and battery-less sensing and automatic identification tag for real time monitoring of human movements.

Falls related injuries among elderly patients in hospitals or residents in residential care facilities is a significant problem that causes emotional and physical trauma to those involved while presenting a rising healthcare expense in countries such as Australia where the population is ageing. Novel approaches using low cost and privacy preserving sensor enabled Radio Frequency Identification (RFID) technology may have the potential to provide a low cost and effective technological intervention to prevent falls in hospitals. We outline the details of a wearable sensor enabled RFID tag that is battery free, low cost, lightweight, maintenance free and can be worn continuously for automatic and unsupervised remote monitoring of activities of frail patients at acute hospitals or residents in residential care. The technological developments outlined in the paper forms part of an overall technological intervention developed to reduce falls at acute hospitals or in residential care facilities. This paper outlines the details of the technology, underlying algorithms and the results (where an accuracy of 94-100% was achieved) of a successful pilot trial.