A combined imaging, deformation and registration methodology for predicting respirator fitting

N95/FFP3 respirators have been critical to protect healthcare workers and their patients from the transmission of COVID-19. However, these respirators are characterised by a limited range of size and geometry, which are often associated with fitting issues in particular sub-groups of gender and ethnicities. This study describes a novel methodology which combines magnetic resonance imaging (MRI) of a cohort of individuals (n = 8), with and without a respirator in-situ, and 3D registration algorithm which predicted the goodness of fit of the respirator. Sensitivity analysis was used to optimise a deformation value for the respirator-face interactions and corroborate with the soft tissue displacements estimated from the MRI images. An association between predicted respirator fitting and facial anthropometrics was then assessed for the cohort

Studio di fattibilità per fissatore esterno circolare con possibilità di abbinamento a dispositivo articolare di ginocchio

La fissazione esterna si propone come una metodica alternativa molto valida nel trattamento delle fratture di bacino e delle ossa lunghe, nel massimo rispetto delle parti molli e dell’osso, e nel recupero precoce del movimento articolare. I maggiori vantaggi nell’utilizzo dei fissatori esterni sono rappresentati: dalla possibilità di essere utilizzati in una gamma di situazioni complesse, nelle quali le tecniche tradizionali non danno buoni risultati (ad esempio fratture esposte), dalla semplicità del procedimento chirurgico, dal fatto che evitano un secondo tempo chirurgico per la rimozione dei mezzi di sintesi. Contrariamente a tutti i mezzi di sintesi inoltre, sono indicati nel caso di infezioni. A livello articolare invece, la loro presenza, contrariamente a un gesso, consente la mobilizzazione precoce, sia passiva che attiva, dell’arto interessato, fondamentale per una completa ripresa funzionale. L'esperienza di questa tesi ha mostrato che i criteri di valutazione di un sistema di fissazione esterna sono rappresentati da: stabilità dell’impianto, cioè la capacità del sistema di mantenere la riduzione ottenuta resistendo alle forze di sollecitazione nel tempo, deformabilità elastica dell’impianto, cioè la possibilità di consentire e promuovere micromovimenti a livello del focolaio di frattura, versatilità dell’impianto, cioè la possibilità di realizzare montaggi diversi per rispondere a specifiche richieste terapeutiche, massimo rispetto biologico da parte degli elementi di presa, con un’accettabile ingombro per evitare intolleranze soggettive e semplicità d’applicazione. Pertanto i vantaggi clinici sono: minima invasività, sintesi stabile, compressione inter-frammentaria, versatilità, mobilizzazione precoce e carico precoce. Il presente lavoro si è rivelato uno strumento utile per arrivare alla progettazione finale di un sistema circolare e di un sistema ibrido che possano rispondere entrambi a tutti i requisiti sopra elencati. Ha focalizzato innanzitutto la propria attenzione su un sistema circolare, con il fine ultimo di descrivere in quale modo esso risponde all’applicazione di differenti cicli di carico. La particolarità del sistema studiato è rappresentata dalla possibilità di combinare il sistema circolare con un dispositivo articolato di ginocchio, costituendo così un sistema ibrido di fissazione esterna. Il sistema ibrido ha unito la rigidezza garantita dal sistema monolaterale all’elasticità dei sistemi circolari, in modo tale che le forze, che altrimenti agirebbero sul focolaio di frattura, si possano scaricare sia sul dispositivo monolaterale sia sui cerchi. L’elaborato ha focalizzato quindi la sua attenzione sul sistema ibrido, formato dalla combinazione del sistema circolare e del dispositivo monolaterale articolato, con il fine ultimo di descrivere come anch’esso risponde all’applicazione di differenti cicli di carico

Bioengineering technologies to monitor movements in supported postures: a potential strategy to prevent pressure ulcers

There are many clinical situations in which skin and soft tissues are subjected to sustained mechanical loading, particularly in individuals with restricted mobility. This can result in the breakdown of soft tissues in vulnerable areas, leading to the development of pressure ulcers (PUs). For several decades, interface pressure measuring systems have been employed to assess the magnitudes of pressures at the support surface interface of individuals at risk of developing PUs, typically to evaluate the short-term performance of pressure relieving systems (e.g. mattresses) and promote optimal postures. These technologies have recently been adapted to monitor over extended periods, providing the opportunity to estimate clinically-relevant temporal trends in posture and mobility. However, their ability to detect individual postural movements has not been established. Therefore, the present research was designed to assess the combination of pressure monitoring and intelligent data processing for the detection of postural changes during prolonged lying.A series of experimental studies utilised biomechanical parameters derived from pressure distribution and signals representative of body segmental movements using actimetry systems. Continuous measures were taken in cohorts of healthy individuals during evoked lying postures involving a raised head of the bed (HOB) and automated lateral tilt. The sensitivity and specificity of parameters for detecting changes in defined lying postures were examined. Data optimisation with Receiver Operating Characteristics (ROC) and Principal Component Analyses were performed to establish the most robust parameters, thus reducing the large volume of data associated with long-term monitoring. In particular, contact area and centre of pressure signals at specific body regions i.e. whole body and buttock, proved the most accurate of the interface pressure parameters, with ROC curve values (AUC) exceeding 0.5 for the majority of evoked postures. Signals derived from actimetry at the sternum also proved accurate in detecting postural movements, with the majority of postures revealing high AUC values. These parameters were combined with an automated detection method and machine learning algorithms to develop a robust methodology capable of predicting the frequency and magnitude of postural changes.The methodology was refined to accommodate a random sequence of postures on different support surfaces. The final automated methodology was then tested on pressure monitoring data from a small cohort of spinal cord injured subjects, who are vulnerable to PU development.Prediction of lying postural changes was achieved with a derivative threshold – based method, which yielded an accuracy of 100% when pressure signals were combined with body angles, and >85% for pressure signals in isolation. Prediction of lying postures was achieved by applying machine learning classifiers to either a combination of actimetry and pressure data or the pressure parameters in isolation. The most accurate combination of clinically relevant parameters involved pressure signals and body angles, achieving an average accuracy of ≥88%.The series of experiments and analytical approaches undertaken in this project contributed to the development of a semi-automated methodology based on robust biomechanical parameters for prediction of posture and mobility during prolonged lying. This was translated to a clinical data set, where long-term pressure monitoring was employed to evaluate previously unknown postures and provide the objective means to evaluate whether repositioning for pressure ulcer prevention adhered to international guidelines. Although further improvements are required for the analysis and visualisation of pressure data in clinical settings, this novel methodology has the potential to provide objective indication of posture and mobility which will inform effective personalised PU prevention

A sensitivity analysis to evaluate the performance of temporal pressure - related parameters in detecting changes in supine postures

Pressure mapping systems have been traditionally used to assess load distributions in individuals at risk of pressure ulcers. Recently, the technology has been adapted to monitor pressures over prolonged periods. The present study aims to investigate the predictive ability of selected biomechanical parameters estimated from pressure distributions for detecting postural changes in lying. Healthy participants (n = 11) were recruited and positioned in different lying postures, by utilizing the head of bed (HOB) angle and an automated tilting system to achieve evoked movements in the sagittal and transverse planes, respectively. Measurements included continuous monitoring of interface pressures and accelerations from the trunk and waist. Selected interface pressure parameters included; centre of pressure, contact area and pressure gradient. A threshold range for all parameters was established and Receiver Operating Characteristic (ROC) curves presented to determine the sensitivity and specificity for detecting postural changes. Temporal trends in the data revealed significant variance in the signal perturbations during each evoked postural change. Indeed, sensitivity and specificity were influenced by the specific threshold values and the relative position of the individual. As an example, sensitivity of some parameters exhibited a compromised trend at higher HOB angles, with low corresponding area under the ROC curve. By contrast, contact area provided the highest values, with 7/12 signals achieve AUC >0.5. This corresponded with actimetry signals, which provided high discrimination between postures. Parameters estimated from a commercial pressure monitoring can have the potential to detect postural changes. Further research is required to convert the data into meaningful clinical information, to inform patient repositioning strategies

Optimization of spatial and temporal configuration of a pressure sensing array to predict posture and mobility in lying

Commercial pressure monitoring systems have been developed to assess conditions at the interface between mattress/cushions of individuals at risk of developing pressure ulcers. Recently, they have been used as a surrogate for prolonged posture and mobility monitoring. However, these systems typically consist of high-resolution sensing arrays, sampling data at more than 1 Hz. This inevitably results in large volumes of data, much of which may be redundant. Our study aimed at evaluating the optimal number of sensors and acquisition frequency that accurately predict posture and mobility during lying. A continuous pressure monitor (ForeSitePT, Xsensor, Calgary, Canada), with 5664 sensors sampling at 1 Hz, was used to assess the interface pressures of healthy volunteers who performed lying postures on two different mattresses (foam and air designs). These data were down sampled in the spatial and temporal domains. For each configuration, pressure parameters were estimated and the area under the Receiver Operating Characteristic curve (AUC) was used to determine their ability in discriminating postural change events. Convolutional Neural Network (CNN) was employed to predict static postures. There was a non-linear decline in AUC values for both spatial and temporal down sampling. Results showed a reduction of the AUC for acquisition frequencies lower than 0.3 Hz. For some parameters, e.g., pressure gradient, the lower the sensors number the higher the AUC. Posture prediction showed a similar accuracy of 63−71% and 84−87% when compared to the commercial configuration, on the foam and air mattress, respectively. This study revealed that accurate detection of posture and mobility events can be achieved with a relatively low number of sensors and sampling frequency

A novel approach to identify individual positioning in a range of supine postures

International audienceINTRODUCTION: Pressure mapping provides visual feedback of the interface pressures between vulnerable tissues and supporting surfaces. However, the short-term nature of these measures provides limited insight into the temporal changes in pressure during evoked or self-induced movements. We examined the performance of selected parameters derived from continuous pressure monitoring and actimetry to detect postural changes. This yielded large data sets, which would benefit from intelligent data processing. This motivates the present study, which examines the accuracy of machine learning for the prediction of supine postures.METHODS: Nineteen healthy participants adopted supine postures on a standard mattress, movements were evoked using the head of bed (HOB) angle and a tilting system to achieve sagittal (HOB between 0 and 60o) and lateral (left and right) postures, respectively. A series of time-related biomechanical parameters were estimated using a pressure monitor and actimetry placed on the sternum. Two supervised machine learning algorithms were assessed, namely K-nearest neighbors (KNN) and Naïve-Bayes (NB), established with training data (n=9) and cross-validated with test data (n=10). KNN estimates the distance between a test data point and the nearest data point in the training phase, and NB the probability that a test data point belongs to specific cluster of postures.RESULTS: Ranking of the biomechanical parameters revealed whole body contact area (>20mmHg) and trunk tilt angles provided the highest discrimination for postural changes. Separate clusters were identified for postures incorporating 20oHOB increments. The accuracy in predicting the range of sagittal and lateral postures was >80% for all subjects, for NB approach. By contrast, KNN accuracy resulted >70% for 8/10 subjects. An exemplar of both results are presented for one participant (Figure2). The NB algorithm was probably able to accommodate part of the non-linearity in the data, which could explain the differences in accuracy.CONCLUSIONS: Accurate prediction of supine postures was achieved by combining machine-learning approaches with robust parameters estimated from two monitoring systems. This approach represents an advanced method of monitoring postures and mobility. Future work will combined evaluation of the local physiological response to these postures in order to create intelligent monitoring solutions. These technologies have the potential to identify pressure ulcer risk and efficient strategies for prevention in practice

Prediction of individual positioning in a range of lying postures

International audienc

Biomechanical monitoring and machine learning for the detection of lying postures

Background: pressure mapping technology has been adapted to monitor over prolonged periods to evaluate pressure ulcer risk in individuals during extended lying postures. However, temporal pressure distribution signals are not currently used to identify posture or mobility. The present study was designed to examine the potential of an automated approach for the detection of a range of staticlying postures and corresponding transitions between postures.Methods: healthy subjects (n=19) adopted a range of sagittal and lateral lying postures. Parameters reflecting both the interactions at the support surface and body movements were continuously monitored. Subsequently, the derivative of each signal was examined to identify transitions between postures. Three machine learning algorithms, namely Naïve-Bayes, k-Nearest Neighbors and Support Vector Machine classifiers, were assessed to predict a range of static postures, established with a training model (n=9) and validated with new input from test data (n=10). Findings: results showed that the derivative signals provided a means to detect transitions between postures, with actimetry providing the most distinct signal perturbations. The accuracy in predicting the range of postures from new test data ranged between 82%-100%, 70%-98% and 69%-100% for Naïve-Bayes, k-Nearest Neighbors and Support Vector Machine classifiers, respectively.Interpretation: the present study demonstrated that detection of both static postures and their corresponding transitions was achieved by combining machine learning algorithms with robust parameters from two monitoring systems. This approach has the potential to provide reliable indicators of posture and mobility, to support personalized pressure ulcer prevention strategies.<br/

Continuous pressure monitoring of inpatient spinal cord injured patients: implications for pressure ulcer development

Study design: cohort observational study.Objectives: to examine the movement profiles of individuals with spinal cord injury (SCI) during their inpatient rehabilitative phase using continuous pressure monitoring (CPM), evaluating the trends in those with skin damage.Setting: SCI specialist rehabilitation centre in the United Kingdom.Methods: individuals with SCI (n = 12) were assessed using CPM in the bed and chair over a 24–72 h. Pressure data was used as a surrogate for movement using both nursing interpretation and an intelligent algorithm. Clinical features were obtained including participants age, injury level, ASIA score, co-morbidities and prescribed support surfaces. Trends between movement profiles (frequency and intervals), SCI demographics and observed skin damage were assessed using cross-tabulation and histograms.Results: the data revealed significant correlations (p &lt; 0.05) between the nursing observation and algorithm for predicting movement, although the algorithm was more sensitive. Individuals with high level injuries (C1-T6) were observed to have a lower frequency of movement and larger intervals between movements when compared to low level injuries (T7-L5) during both lying and sitting periods. The individuals observed to have skin damage were predominantly those who had both a low frequency of movement and extended gaps between movements.Conclusions: movements for pressure relief in both the bed and chair environments were dependent on the level of injury in individuals with SCI during their inpatient rehabilitation. Distinct movement patterns corresponded with those who acquired skin damage, revealing the potential clinical applications for technologies to monitor PU risk and inform personalised care