Eye-CU: Sleep Pose Classification for Healthcare using Multimodal Multiview Data

Manual analysis of body poses of bed-ridden patients requires staff to continuously track and record patient poses. Two limitations in the dissemination of pose-related therapies are scarce human resources and unreliable automated systems. This work addresses these issues by introducing a new method and a new system for robust automated classification of sleep poses in an Intensive Care Unit (ICU) environment. The new method, coupled-constrained Least-Squares (cc-LS), uses multimodal and multiview (MM) data and finds the set of modality trust values that minimizes the difference between expected and estimated labels. The new system, Eye-CU, is an affordable multi-sensor modular system for unobtrusive data collection and analysis in healthcare. Experimental results indicate that the performance of cc-LS matches the performance of existing methods in ideal scenarios. This method outperforms the latest techniques in challenging scenarios by 13% for those with poor illumination and by 70% for those with both poor illumination and occlusions. Results also show that a reduced Eye-CU configuration can classify poses without pressure information with only a slight drop in its performance.Comment: Ten-page manuscript including references and ten figure

Multimodal Analytics for Healthcare

The ailing healthcare system demands effective autonomous solutions to improve service and provide individualize care, while reducing the burden on the scarce healthcare workforce. Most of these solutions require a multidisciplinary approach that combines healthcare with computational abilities. The work presented in this thesis introduces a multimodal multiview network along with methods and solutions that leverage inexpensive visual sensors and computers to monitor healthcare. One of the most prominent outcomes of this work includes enabling the medical analysis of ICU conditions such as sleep disorders, decubitus ulcerations, and hospital acquired infections, which are preventable and negatively affect patients' health population. The problems tackled include patient pose classification, pose motion analysis and summarization, role representation and identification, and activity and event logging in natural hospital settings. These problems are addressed via a non-intrusive non-disruptive multimodal multiview sensor network (Medical Internet-of-Things). The multimodal data is combined with coupled-optimization to estimate source weights and accurately classify patient poses. Pose patterns such as pose transitions are represented using deep convolutional features and pose duration is modelled via segments. The proposed techniques serve to differentiate between poses and pseudo-poses (transitory poses) and create effective motion summaries. The role representation is tackled using novel appearance and semantic interaction maps to assign generic labels to individuals (doctor, nurse, visitor, etc) without using identifiable information (e.g., facetracking or badges), which is prohibited in healthcare applications. Finally, activity and event analysis is tackled using a new contextual aspect frames where aspect bases and weights are learned and then used to reconstruct activities. The objective of this thesis is to enable the development, evaluation, and optimization of individualized therapies, standards-of-care, room infrastructural designs, and clinical workflows and procedures

State of the art of audio- and video based solutions for AAL

Working Group 3. Audio- and Video-based AAL ApplicationsIt is a matter of fact that Europe is facing more and more crucial challenges regarding health and social care due to the demographic change and the current economic context. The recent COVID-19 pandemic has stressed this situation even further, thus highlighting the need for taking action. Active and Assisted Living (AAL) technologies come as a viable approach to help facing these challenges, thanks to the high potential they have in enabling remote care and support. Broadly speaking, AAL can be referred to as the use of innovative and advanced Information and Communication Technologies to create supportive, inclusive and empowering applications and environments that enable older, impaired or frail people to live independently and stay active longer in society. AAL capitalizes on the growing pervasiveness and effectiveness of sensing and computing facilities to supply the persons in need with smart assistance, by responding to their necessities of autonomy, independence, comfort, security and safety. The application scenarios addressed by AAL are complex, due to the inherent heterogeneity of the end-user population, their living arrangements, and their physical conditions or impairment. Despite aiming at diverse goals, AAL systems should share some common characteristics. They are designed to provide support in daily life in an invisible, unobtrusive and user-friendly manner. Moreover, they are conceived to be intelligent, to be able to learn and adapt to the requirements and requests of the assisted people, and to synchronise with their specific needs. Nevertheless, to ensure the uptake of AAL in society, potential users must be willing to use AAL applications and to integrate them in their daily environments and lives. In this respect, video- and audio-based AAL applications have several advantages, in terms of unobtrusiveness and information richness. Indeed, cameras and microphones are far less obtrusive with respect to the hindrance other wearable sensors may cause to one’s activities. In addition, a single camera placed in a room can record most of the activities performed in the room, thus replacing many other non-visual sensors. Currently, video-based applications are effective in recognising and monitoring the activities, the movements, and the overall conditions of the assisted individuals as well as to assess their vital parameters (e.g., heart rate, respiratory rate). Similarly, audio sensors have the potential to become one of the most important modalities for interaction with AAL systems, as they can have a large range of sensing, do not require physical presence at a particular location and are physically intangible. Moreover, relevant information about individuals’ activities and health status can derive from processing audio signals (e.g., speech recordings). Nevertheless, as the other side of the coin, cameras and microphones are often perceived as the most intrusive technologies from the viewpoint of the privacy of the monitored individuals. This is due to the richness of the information these technologies convey and the intimate setting where they may be deployed. Solutions able to ensure privacy preservation by context and by design, as well as to ensure high legal and ethical standards are in high demand. After the review of the current state of play and the discussion in GoodBrother, we may claim that the first solutions in this direction are starting to appear in the literature. A multidisciplinary 4 debate among experts and stakeholders is paving the way towards AAL ensuring ergonomics, usability, acceptance and privacy preservation. The DIANA, PAAL, and VisuAAL projects are examples of this fresh approach. This report provides the reader with a review of the most recent advances in audio- and video-based monitoring technologies for AAL. It has been drafted as a collective effort of WG3 to supply an introduction to AAL, its evolution over time and its main functional and technological underpinnings. In this respect, the report contributes to the field with the outline of a new generation of ethical-aware AAL technologies and a proposal for a novel comprehensive taxonomy of AAL systems and applications. Moreover, the report allows non-technical readers to gather an overview of the main components of an AAL system and how these function and interact with the end-users. The report illustrates the state of the art of the most successful AAL applications and functions based on audio and video data, namely (i) lifelogging and self-monitoring, (ii) remote monitoring of vital signs, (iii) emotional state recognition, (iv) food intake monitoring, activity and behaviour recognition, (v) activity and personal assistance, (vi) gesture recognition, (vii) fall detection and prevention, (viii) mobility assessment and frailty recognition, and (ix) cognitive and motor rehabilitation. For these application scenarios, the report illustrates the state of play in terms of scientific advances, available products and research project. The open challenges are also highlighted. The report ends with an overview of the challenges, the hindrances and the opportunities posed by the uptake in real world settings of AAL technologies. In this respect, the report illustrates the current procedural and technological approaches to cope with acceptability, usability and trust in the AAL technology, by surveying strategies and approaches to co-design, to privacy preservation in video and audio data, to transparency and explainability in data processing, and to data transmission and communication. User acceptance and ethical considerations are also debated. Finally, the potentials coming from the silver economy are overviewed.publishedVersio

Patient Safety and Quality: An Evidence-Based Handbook for Nurses

Compiles peer-reviewed research and literature reviews on issues regarding patient safety and quality of care, ranging from evidence-based practice, patient-centered care, and nurses' working conditions to critical opportunities and tools for improvement

An Educational Intervention for Anesthesia Handoff and Recovery in the ICU

Background: Despite advancements in pharmaceuticals and patient monitoring, postoperative complications remain common. As more patients are transferred from the operating room to the intensive care unit, staff should be aware of potential complications and treatments for patients following anesthesia. Purpose: The purpose of this project was to provide an educational intervention to members of the ICU team on the components of an anesthesia handoff, postoperative complications, and appropriate management of these complications. Methods: A pre intervention survey consisting of Likert style questions assessing confidence levels during handoff and management of patients following anesthesia was distributed to bedside nursing staff of a 15-bed ICU within an urban hospital in North Carolina. A standardized handoff tool and an educational video were provided to staff. One month after education, nurses were asked to complete a survey containing Likert style questions assessing confidence levels and practice change. Results: Sixty percent of nurses who responded agreed that they felt more confident receiving handoff from the anesthesia team with the use of the tool. Eighty percent agreed that the handoff tool will be helpful during future transfer of care events with the anesthesia team. Eighty percent agreed that the educational information provided would be beneficial during future management of the postoperative patient following anesthesia. Recommendations and Conclusions: The standardized handoff tool should be used to assist in improving the transfer of care process and to help reduce the omission of information. Information about the recognition and management of post anesthesia complications should be integrated into education for ICU nurses

2022 - The Third Annual Fall Symposium of Student Scholars

The full program book from the Fall 2022 Symposium of Student Scholars, held on November 17, 2022. Includes abstracts from the presentations and posters.https://digitalcommons.kennesaw.edu/sssprograms/1026/thumbnail.jp

Intravenous Acetaminophen Reduces Opioid Use for Postoperative Pain in Obese Patients Undergoing Laparoscopic Cholecystectomy

Opioid-induced respiratory depression is categorized as a sentinel event, which is an unanticipated occurrence that may result in severe injury or death. Although the incidence of opioid-induced respiratory depression is 0.2 to 2%, this issue persists as a substantial basis of morbidity and mortality. The obese population has multiple physiologic elements that increase their risk for opioid-induced respiratory depression. This retrospective study examined if intravenous (IV) acetaminophen reduces opioid consumption in the postoperative period in obese patients who underwent a laparoscopic cholecystectomy. Adequately controlled postoperative pain provides for greater patient satisfaction, decreased hospital length of stay (LOS), and reduced costs for the medical facility. The inclusion criterion comprised patients within the ages of 18 to 65, a body mass index (BMI) greater than 30, and who underwent a laparoscopic cholecystectomy at the designated medical facility. Exclusion criteria included patients with a known allergy to acetaminophen; severe hepatic impairment, chronic alcoholism or use of opioids, and malnutrition; severe hypovolemia, or renal impairment. The independent group t-test was used to compare the (a) amount of opioids administered in the post anesthesia care unit (PACU), (b) amount of opioids administered for the entire LOS, and (c) LOS between the two groups. There was a significant difference found in all of these factors. A considerable finding of this capstone project was the mean cost for patients who received IV acetaminophen was $1,143.60, compared to$1,406.00 for those who did not receive IV acetaminophen. This price difference was due to the variance in the total LOS