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Pain and physical function are both essential indices of recovery in critically ill patients in the Intensive Care Units (ICU). Simultaneous monitoring of pain intensity and patient activity can be important for determining which analgesic interventions can optimize mobility and function, while minimizing opioid harm. Nonetheless, so far, our knowledge of the relation between pain and activity has been limited to manual and sporadic activity assessments. In recent years, wearable devices equipped with 3-axis accelerometers have been used in many domains to provide a continuous and automated measure of mobility and physical activity. In this study, we collected activity intensity data from 57 ICU patients, using the Actigraph GT3X device. We also collected relevant clinical information, including nurse assessments of pain intensity, recorded every 1-4 hours. Our results show the joint distribution and state transition of joint activity and pain states in critically ill patients.
Sleep has been shown to be an indispensable and important component of patients recovery process. Nonetheless, sleep quality of patients in the Intensive Care Unit (ICU) is often low, due to factors such as noise, pain, and frequent nursing care acti
Traditional methods for assessing illness severity and predicting in-hospital mortality among critically ill patients require time-consuming, error-prone calculations using static variable thresholds. These methods do not capitalize on the emerging a
Internet of Things (IoT) has triggered a rapid increase in the number of connected devices and new use cases of wireless communications. To meet the new demands, the fifth generation (5G) of wireless communication systems features native machine type
Human Activity Recognition (HAR), based on machine and deep learning algorithms is considered one of the most promising technologies to monitor professional and daily life activities for different categories of people (e.g., athletes, elderly, kids,
In this paper, we propose a turbo receiver for joint activity detection and data decoding in grant-free massive random access, which iterates between a detector and a belief propagation (BP)-based channel decoder. Specifically, responsible for user a