17 research outputs found
A load-cell based in-bed body motion detection and classification system
No full textThe basic necessity of sleep in our life is critically important to ensure our wellbeing. Sufficient sleep of good quality is highly desired in order to have enough energy to live. One of the main factors to measure sleep quality is the amount of body motion during sleep. In-bed motion detection is an important technique that can enable an array of applications, among which are sleep monitoring and abnormal movement detection. When detection is combined with classification, it can be used to detect, notify, and recognize specific events, enabling us to focus on critical tasks. In this study, we present a low-cost, low-overhead, and highly robust system for in-bed movement detection and classification that uses low-end load cells. By observing the forces sensed by the load cells, placed under each bed leg, we can detect many different types of movements, and further classify them as big or small depending on magnitude of the force changes on the load cells. We have designed three different features, which we refer to as Log-Peak, Energy-Peak, and ZeroX-Valley, that can effectively extract body movement signals from load cell data that is collected through wireless links in an energy-efficient manner. After establishing feature values, we employ a simple threshold-based algorithm to detect and classify movements. We have conducted a thorough evaluation, that involves collecting data from 30 subjects who perform 27 pre-defined movements in an experiment. By comparing our detection and classification results against the ground truth captured by a video camera, we show the Log-Peak strategy can detect these 27 types of movements at an error rate of 6.3% while classifying them as big or small movements at an error rate of 4.2%. In the second part of this dissertation, we set out to achieve much finer body motion classification. Towards this goal, we define 9 classes of movements, and design a machine learning algorithm using Support Vector Machine (SVM) and Random Forest techniques to classify a movement into one of these 9 classes. In this way, we can find out which body parts are involved in every movement. For every movement, we have extracted 24 features and used them in our model. This movement classification system was evaluated on data collected from 40 subjects who performed 35 predefined movements in each experiment. The accuracy of our model is not the same for all classes of movements. On average, it correctly classifies 90% of movements. This model can be used conveniently for long-term home monitoring. To improve the classification accuracy, we investigate more machine learning techniques. We use Random Forest and XGBoost as additional classification tools. We apply multiple tree topologies for each technique to reach their best results. After examining various combinations, we achieve the final classification accuracy of 91.5%. Lastly, another in-bed motion detection system is built. We use a geophone sensor to detect body motions in bed, which we call MotionPhone. MotionPhone is more accurate in detecting motion but not efficient for classification purposes. We thus believe combining these two systems can give us better results. Both systems are unobtrusive, low-cost, and private, which can thus enable a large array of important applications.Ph.D.Includes bibliographical referencesby Musaab Adil Alazi
Corrigendum to Coital Incontinence: What Can We Learn From Urodynamic Assessment? [Urology 85 (2015) 1034-1038].
No full textThe authors regret the surname of the last author was misspelled. The byline to this Corrigendum is correct. The XML and online PDF of the article has been reposted and replaced. Unfortunately, the discovery of the error was too late to correct the printed issue. The authors would like to apologise for any inconvenience caused. DOI of original article: 10.1016/j.urology.2015.02.007 From the Department of Urogynecology, Derriford Hospital, Plymouth, Devon, UK; the Department of Urogynecology, Bristol Urological Institute, Southmead Hospital, Bristol, UK; Department of Urogynecology, University of Bristol, Bristol, UK; and the Department of Urogynecology, Plymouth University Peninsula Schools of Medicine and Dentistry, Plymouth, Devon, UK Address correspondence to: Musaab Yassin, [email protected]
Acute limb ischemia as a first presentation of a thyrotoxic patient: A Case Report
No full textIntroduction Arterial embolism in thyrotoxic atrial fibrillation is more common than is realized and can be the first presentation of undiagnosed hyperthyroidism. Report Acute limb ischemia as a first presentation of hyperthyroidism Discussion A medical condition can modify a surgical approach to achieve successful results. </jats:sec
Ramadan Fasting and Complications of Metabolic Dysfunction-Associated Steatotic Liver Disease: Impacts on Liver Cirrhosis and Heart Failure
No full textBackground: Metabolic-dysfunction-associated steatotic liver disease (MASLD) and heart failure are two intersecting growing pandemics. Studies have demonstrated a strong association between MASLD and heart failure. Liver cirrhosis is a well-recognized complication of MASLD. This study aimed to summarize the potential effects of Ramadan fasting on MASLD, liver cirrhosis, and heart failure. The author searched the SCOPUS and PubMed databases using specific terms. The literature review focused on research articles published in English from 2000 to 2024. Twenty-two articles were selected for this narrative review. Ramadan fasting reduced serum cholesterol serum levels, improved symptoms of heart failure and reduced anthropometric measurements. However, it increased ascitic fluid production and plasma bilirubin levels and might increase the risk of hepatic encephalopathy and upper gastrointestinal haemorrhage in liver cirrhosis. Ramadan fasting might improve symptoms of heart failure and might decrease the risk of heart failure in patients with MASLD. Further research studies are needed to confirm the efficacy and evaluate the safety of Ramadan fasting in patients with heart failure and liver cirrhosis
Coital Incontinence: what can we learn from urodynamic assessment?
No full text45. Madhu C, Hashim H, Enki D, Yaasin M, Drake M
The Functional Effects of Cigarette Smoking in Women on the Lower Urinary Tract
No full textMethods: Methods: Methods: AAim: The aim of the study was to evaluate the urodynamic findings in women who smoke cigarettes, with bothersome lower urinary tract symptoms, to help develop an understanding of potential impact of smoking on the lower urinary tract function. Methods: A database of 11,678 women who underwent urodynamic testing in a tertiary referral centre in the United Kingdom, from January 1991 to December 2009 was retrospectively analysed. All women reporting cigarette smoking were included in the study group. Urodynamic testing and interpretation of results were done in accordance with the recommendations of the International Continence Society. Results: Overall, 2,476 (21.2%) women reported smoking cigarettes. Overactive bladder symptoms (OAB) were more common in smokers (OR 1.14, p = 0.006). Female smokers significantly complained of secondary nocturnal enuresis (OR 2.26, p < 0.001) and coital incontinence (CI; OR 1.14, p < 0.001). Detrusor overactivity (DO; OR 1.42, p < 0.001) and detrusor overactivity incontinence (DOI; OR 1.42, p < 0.001) were the most significant urodynamic findings. Smoking was not shown to be significantly associated with SUI (OR 1.08, p = 0.213) or urodynamic stress incontinence (OR 0.86, p = 0.001). Conclusion: Cigarette smoking is associated with OAB, secondary nocturnal enuresis and CI. DO and DOI are the most significant urodynamic findings
