Getting out of bed and ambulating without supervision is identified as one of the major causes of patient falls in hospitals and nursing homes. Therefore, increased supervision is proposed as a key strategy toward falls prevention. An emerging generation of batteryless, lightweight, and wearable sensors are creating new possibilities for ambulatory monitoring, where the unobtrusive nature of such sensors makes them particularly adapted for monitoring older people. In this study, we investigate the use of a batteryless radio-frequency identification (RFID) tag response to analyze bed-egress movements. We propose a bed-egress movement detection framework that includes a novel sequence learning classifier with a set of features derived from bed-egress motion analysis. We analyzed data from 14 healthy older people (66-86 years old) who wore a wearable embodiment of a batteryless accelerometer integrated RFID sensor platform loosely attached over their clothes at sternum level, and undertook a series of activities including bed-egress in two clinical room settings. The promising results indicate the efficacy of our batteryless bed-egress monitoring framework.
Measuring multiple physical quantities are increasingly being demanded in commercial, biomedical and generally in ubiquitous applications. Although the recent emergence of passive sensor enabled RFID tags (sensor tags) provide new opportunities for these types of applications mainly due to the extended operational life and the small form factor, the energy harvesting nature of sensor tags hinders the use of multiple sensors in a single platform because of the requirement of additional energy to operate multiple sensors and subsequent reduction in the throughput. In this paper, we propose three, fast and energy efficient multi-sensor data retrieval approaches to obtain sensor data from sensor tags. We implemented a sensor tag with two sensors, an accelerometer and a barometer. Our extensive experiments on power consumption, operational range and throughput using the developed sensor tag revealed that, the proposed approaches can successfully be used for multi-sensor data retrieval and indicates that they can effectively be used in a range of real-world ubiquitous sensing applications such as fall prevention and food safety monitoring.
The combination of large open data sources with machine learning approaches presents a potentially powerful way to predict events such as protest or social unrest. However, accounting for uncertainty in such models, particularly when using diverse, unstructured datasets such as social media, is essential to guarantee the appropriate use of such methods. Here we develop a Bayesian method for predicting social unrest events in Australia using social media data. This method uses machine learning methods to classify individual postings to social media as being relevant, and an empirical Bayesian approach to calculate posterior event probabilities. We use the method to predict events in Australian cities over a period in 2017/18.
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