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IntroductionDue to high rate of operative mortality and morbidity non-operative management of blunt liver and spleen trauma was widely accepted in stable pediatric patients, but the general surgeons were skeptical to adopt it for adults. The current study is analysis of so far largest sample (1071) of hemodynamically stable blunt liver, spleen, kidney and pancreatic trauma patients managed non operatively irrespective of severity of a single /multiple solid organ injury or other associated injuries with high rate of success.MethodsExperience of 1071 blunt abdominal trauma patients treated by NOM at a tertiary care National Trauma Centre in Oman (from Jan 2001 to Dec 2011) was reviewed, analyzed to determine the indications, methods and results of NOM. Hemodynamic stability along with ultra sound, CT scan and repeated clinical examination were the sheet anchors of NOM. The patients were grouped as (1) managed by NOM successfully, (2) failure of NOM and (3) directly subjected to surgery.ResultsDuring the 10 year period, 5400 polytrauma patients were evaluated for abdominal trauma of which 1285 had abdominal injuries, the largest sample study till date. Based on initial findings 1071 patients were admitted for NOM. Out of 1071 patients initially selected 963 (89.91%) were managed non operatively, the remaining 108 (10.08%) were subjected to laparotomy due to failure of NOM. Laparotomy was performed on 214(19.98%) patients as they were unstable on admission or had evidence of hollow viscous injury.ConclusionNOM for blunt abdominal injuries was found to be highly successful in 89.98% of the patients in our study. Management depended on clinical and hemodynamic stability of the patient. A patient under NOM should be admitted to intensive care / high dependency for at least 48-72 hours for close monitoring of vital signs, repeated clinical examinations and follow up investigations as indicated.

and Nguyen, Huan X. ORCID logoORCID: https://orcid.org/0000-0002-4105-2558 (2022) Digital twins: a survey on enabling technologies, challenges, trends and future prospects. IEEE Communications Surveys and Tutorials .

Physical activity is essential for physical and mental health, and its absence is highly associated with severe health conditions and disorders. Therefore, tracking activities of daily living can help promote quality of life. Wearable sensors in this regard can provide a reliable and economical means of tracking such activities, and such sensors are readily available in smartphones and watches. This study is the first of its kind to develop a wearable sensor-based physical activity classification system using a special class of supervised machine learning approaches called boosting algorithms. The study presents the performance analysis of several boosting algorithms (extreme gradient boosting—XGB, light gradient boosting machine—LGBM, gradient boosting—GB, cat boosting—CB and AdaBoost) in a fair and unbiased performance way using uniform dataset, feature set, feature selection method, performance metric and cross-validation techniques. The study utilizes the Smartphone-based dataset of thirty individuals. The results showed that the proposed method could accurately classify the activities of daily living with very high performance (above 90%). These findings suggest the strength of the proposed system in classifying activity of daily living using only the smartphone sensor’s data and can assist in reducing the physical inactivity patterns to promote a healthier lifestyle and wellbeing.

Human emotions are strongly coupled with physical and mental health of any individual. While emotions exbibit complex physiological and biological phenomenon, yet studies reveal that physiological signals can be used as an indirect measure of emotions. In unprecedented circumstances alike coronavirus (Covid-19) outbreak, a remote IoT enabled solution, coupled with AI can interpret and communicate emotions to serve substantially in healthcare and related fields. This work proposes an integrated IoT framework which enables wireless communication of physiological signals to data processing hub where Long Short-Term Memory (LSTM) based emotion recognition is performed. The proposed framework offers real-time communication and recognition of emotions which enables health monitoring and distance learning support amidst pandemics. In this study, the achieved results are very promising. In proposed IoT protocols (TS-MAC and R-MAC) ultra-low latency of 1 millisecond is achieved. R-MAC also offers improved reliability in comparison to state-of-the-art. In addition, the proposed deep learning scheme offers high performance (f-score) of 95%. The achieved results in communications and AI match the interdependency requirements of deep learning and IoT frameworks, thus ensuring the suitability of proposed work in distance learning, student engagement, healthcare, emotion support and general wellbeing.