Human fall detection methodologies : from machine learning using acted data to fall modelling using myoskeletal simulation

Human Fall Detection is a research area with interest from many disciplines and aims to perform for many assisted-living monitoring applications to promptly identify life-threatening situations. A fall occurs when a person is unable to maintain balance due to a variety of issues; physical; mental or environmental. The accurate detection of the fall is crucial as a missed detection can be fatal. Variability of human physiological characteristics is currently unstudied as to the impact on a fall detector's performance as young adults and elderly are expected to fall differently. Another important issue is the scene occlusions. In the use of visual sensors, an occluded fall is treated as a missed detection as the whereabouts of the person is unknown when occluded. Finally, current studies are based on acted fall datasets on which algorithms are trained. These dataset are unrepresentative of real fall events and illustrate the events without occlusions or other scene in uences. Several fall detection algorithms were developed during the study aiming to achieve accuracy in detection falls while fall-like actions such as lying down remain undetected. Human fall datasets were used for training and testing purposes of A machine learning algorithm using data from depth cameras which captured the fall events from different views. A new pathway was introduced tackling the issues of availability issues of data-driven machine learning approaches which was achieved with the use of simulation data. The use of myoskeletal simulation was then selected as a closer representation of the human body in terms of structure and behaviour. With the use of a simulation model, a personalised estimation of the fall event can be achieved as it is parametrised on a physical characteristic such as the height of the falling person. Alternative technologies such as accelerometers have been used for fall detection to prove the validity of this approach on other modalities. A study regarding the impact of occlusions for fall detection which is one of the issues not properly investigated in current work is proposed and examined. Synthetic occlusions were added to existing depth data from publicly available datasets. The research methodologies were evaluated using the most representative depth video and accelerometer data from existing datasets, as well as YouTube videos of real-fall events. The machine learning methodologies achieved good results on similar body variability datasets. A discussion regarding the proof of concept of the simulation-based approach for fall modelling is mentioned given the comparative results against existing methodologies which achieves better than any existing work evaluated against known datasets. The simulation approach is also evaluated against occluded fall and non-fall event data, proving the further robustness of the approach. This platform can be expanded to analyse any type of fall, or body posture (e.g. elderly), without the use of humans to performs fall events

A Rare Pancreatic Tail Metastasis from Squamous Cell Lung Carcinoma Diagnosed by EUS-FNB and a Small Review of the Literature

Differential diagnosis of pancreatic lesions is really challenging, especially when the patient is diagnosed with primary cancer at another site. In this case report, we managed to histologically confirm pancreatic metastasis from squamous cell lung carcinoma, which is a very rare entity, using endoscopic ultrasound fine needle biopsy

Methodology for designing an on-board vehicle tracking system

EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Προγνωστικοί δείκτες οξείας νεφρικής βλάβης σε βαρέως πάσχοντες ασθενείς στην Μονάδα Εντατικής Θεραπείας. Συστηματική ανασκόπηση

Εισαγωγή: Η Οξεία Νεφρική Βλάβη (ΟΝΒ) στη Μονάδα Εντατικής Θεραπείας (ΜΕΘ) αποτελεί ένα μείζων πρόβλημα που αναπτύσσεται και εξελίσσεται στους νοσηλευόμενους βαρέως πάσχοντες ασθενείς. Η κρεατινίνη αποτελεί δείκτη πρόγνωσης, αλλά τα τελευταία χρόνια έχει αποδειχθεί ότι δεν είναι επαρκής, ώστε να λυθεί άμεσα και εξ΄ ολοκλήρου το πρόβλημα της ΟΝΒ. Σκοπός: Η καταγραφή των σύγχρονων επιστημονικών δεδομένων της διεθνούς βιβλιογραφίας σχετικά με την επίδραση των νεότερων βιοδεικτών στη διάγνωση και πρόγνωση της ΟΝΒ σε βαρέως πάσχοντες ασθενείς στη ΜΕΘ Μεθοδολογία: Η αναζήτηση της βιβλιογραφίας πραγματοποιήθηκε στις διεθνείς βάσεις δεδομένων Pubmed, Cinahl, και Medline με λέξεις κλειδιά όπως οξεία νεφρική βλάβη (ΟΝΒ), λιποκαλίνη, N-GAL, συστατίνη C, μόριο νεφρικής βλάβης, KIM-1, IL-18, ιντερλευκίνη 18, NAG, Μονάδα Εντατικής Θεραπείας (ΜΕΘ) και βιοδείκτες. Συλλέχθηκαν 158 άρθρα και από αυτά επιλέχθηκαν και κρίθηκαν ως αποδεκτά για να συμπεριληφθούν στη συστηματική ανασκόπηση τα 52, καθώς πληρούσαν τα κριτήρια της αναζήτησης. Αποτελέσματα: Η πρόωρη ταυτοποίηση των βιοδεικτών για τους ασθενείς που διατρέχουν κίνδυνο ανάπτυξης ΟΝΒ ήταν ένα θεμελιώδες βήμα προς την πρόληψη, διάγνωση και άμεση αντιμετώπιση της. Η χρήση της κρεατινίνης ως διαγνωστικό κριτήριο αναφοράς χαρακτηρίζεται από σημαντικές ελλείψεις, επειδή δεν ανιχνεύει τις υποκλινικές καταστάσεις ΟΝΒ. Από την άλλη πλευρά οι βιοδείκτες μπορεί να επηρεαστούν από συγκεκριμένες κλινικές παραμέτρους. Συμπεράσματα: Είναι γεγονός ότι έχει έρθει η ώρα να αφήσουμε πίσω την κρεατινίνη του ορού ως δείκτη νεφρικής λειτουργίας σε ασθενείς στη ΜΕΘ και ΟΝΒ, ώστε να είμαστε σε θέση στο μέλλον να προσφέρουμε έγκαιρη κατευθυνόμενη και ολιστική θεραπεία με την αρωγή των βιοδεικτών NGAL, κυστατίνης C , IL-18, L-FABP και KIM-1.Aim: Acute kidney injury (AKI) is an increasingly common disorder that is strongly linked to short- and long-term morbidity and mortality .Delayed intervention has been attributed to the deficiency of serum creatinine as an AKI biomarker and is the primary impetus for the discovery of novel AKI biomarkers. The goal of most AKI biomarker research has been the discovery of a “kidney troponin”, a sensitive and specific early marker of renal injury. The purpose of this systematic review was to record the recent scientific data of the international literature on the effect of newer biomarkers on the diagnosis and prognosis of AKI, previously called acute renal failure (ARF), in critically ill patients in the ICU as valuable tools for the immediate recognition of deterioration and early intervention. Methodology: The literature was searched in the international databases Pubmed, Cinahl, and Medline with keywords such as acute kidney failure (AKI),lipocaline, N-GAL, cystatin C , renal failure molecule, KIM-1, IL-18, interleukin 18, NAG, Intensive Care Unit (ICU) and biomarkers. There were collected 158 articles from the international literature. Only 52 were selected and judged as acceptable to include in the systematic review as they met the search criteria. Results: Early identification of biomarkers for patients at risk for developing AKI was a fundamental step toward preventing, diagnosing and treating it immediately. The use of creatinine as a diagnostic reference criterion is characterized by significant deficiencies because it does not detect subclinical AKI conditions. On the other hand, biomarkers may be affected by specific clinical parameters Conclusions: In conclusion, it is time to leave serum creatinine behind as an indicator of renal function in patients in ICU and AKI, so that in the future we will be able to offer timely directed and holistic treatment with the help of NGAL, cystatin C, IL-18, L-FABP and KIM-1