Search

Amputees with lower limb loss need special care during daily life activities to make the movement natural as before amputation. No such work exists covering the main aspects from causes of amputation to the psycho-social impact of the amputees after using the prosthetic device. This review presents for lower limb prosthesis; the study of lower limb amputation, design & development, control strategies & machine learning algorithms, the psycho-social impact of prosthetic users, and design trends in patents. Research articles, review papers, magazines, letters, study reports, surveys, and patents, etc. have been used as sources for this review. Traumatic injuries and different diseases have been found as common causes of amputation. Design & development section illustrates design mechanisms, the categories of passive, active, & semi-active prostheses, an overview of a subset of commercially available prosthetic devices, and 3D printing of the accessories. The control section provides information about control techniques, sensors used, machine learning algorithms, and their key outcomes. Quality of life, phantom limb pain, and psycho-social impact of prosthetic users have been summarized for different countries that are believed to attract the interest of the readers. We have also developed an open-source database “FAKH-50” for patents to emphasize the design trends and advancements in lower limb prostheses from 1970 to 2020. Overall trend analysis determined is in the descending order as the knee (48%) > ankle (28%) > foot (22%) > hip (2%) patents in the current version of our database. The forthcoming section highlights the challenges and prospects of the domain. A mutual observation demands the design of a bio-compatible, lightweight, and economic prosthesis to track the normal human gait by eliminating phantom limb pain. This will empower the amputees to live a quality life in society. This work may be beneficial for researchers, technicians, clinicians, and amputees.

Zinc oxide (ZnO) nanoparticles (NPs) have been investigated for various skin therapies in recent years. These NPs can improve the healing and modulate inflammation in the wounds, but the mechanisms involved in such changes are yet to be known. In this study, we have designed a facile ZnO nano-coated dressing with improved antimicrobial efficiency against typical wound pathogens involved in biofilm and chronic infections. ZnO NPs were obtained by hydrothermal method and characterized by X-ray diffraction, scanning electron microscopy, transmission electron microscopy, and Fourier-transform infrared spectroscopy. Antibacterial and antibiofilm effects were evaluated against laboratory and clinical isolates of significant Gram-negative (Pseudomonas aeruginosa and Escherichia coli) and Gram-positive (Staphylococcus aureus and Enterococcus faecalis) opportunistic pathogens, by quantitative methods. Our results have shown that the developed dressings have a high antibacterial efficiency after 6–24 h of contact when containing 0.6 and 0.9% ZnO NPs and this effect is similar against reference and clinical isolates. Moreover, biofilm development is significantly impaired for up to three days of contact, depending on the NPs load and microbial species. These results show that ZnO-coated dressings prevent biofilm development of main wound pathogens and represent efficient candidates for developing bioactive dressings to fight chronic wounds.

Efficient antibiotics to cure Pseudomonas aeruginosa persistent infections are currently insufficient and alternative options are needed. A promising lead is to design therapeutics able to modulate key phenotypes in microbial virulence and thus control the progression of the infectious process without selecting resistant mutants. In this study, we developed a nanostructured system based on Fe3O4 nanoparticles (NPs) and eugenol, a natural plant-compound which has been previously shown to interfere with microbial virulence when utilized in subinhibitory concentrations. The obtained functional NPs are crystalline, with a spherical shape and 10–15 nm in size. The subinhibitory concentrations (MIC 1/2) of the eugenol embedded magnetite NPs (Fe3O4@EUG) modulate key virulence phenotypes, such as attachment, biofilm formation, persister selection by ciprofloxacin, and the production of soluble enzymes. To our knowledge, this is the first report on the ability of functional magnetite NPs to modulate P. aeruginosa virulence and phenotypic resistance, our data highlights the potential of these bioactive nanostructures to be used as anti-pathogenic agents.

Experimentation and analysis in brain-computer interface (BCI) has increasingly been receiving quite some consideration as a substitute communication possibility for patients who are severely paralyzed in the last few years. To measure brain activities using optical signals a fairly new and non-invasive brain imaging tool can be put to test know as Functional near-infrared spectroscopy (fNIRS). Comparability low cost, safety, portability and wear ability are some of the main advantages of imaging of brain using this non-invasive modality. We propose in this paper to apply this relatively new non-invasive fNIRS technique to make an image of brain activities during four different mental tasks. These tasks include Mental Arithmetic (MA), Motor Imagery (i.e. Left-Hand and Right-Hand Motor Imagery) and Rest. fNIRS data used is an open access dataset which was collected by Continuous-wave imaging system (NIR Scout NIRx GmbH, Berlin, Germany) with the sampling frequency of 10 Hz. The research we have done in this paper Data synchronization is performed before the data is preprocessed. After the preprocessing and signal analysis of data our results shows hemodynamic behavior of multiple patterns during the tasks performed. These unique patterns of hemodynamic behavior can be used to differentiate and distinguish different task. We were able to compare, differentiate and distinguish the brain signal activities captured while performing 4 different tasks using 3 different classifiers i.e. Linear Discriminant Analysis (LDA), Support Vector Machine (SVM) and K Nearest Neighbor (KNN). The average classification accuracy of above 90% is achieved by using K Nearest Neighbors (KNN).

Foot movement during walking, jogging, running, jumping, skidding, and climbing etc. is an important emphasis in lower limb prosthesis. This work presents the estimation of gait phases of walking persons on flat terrain. Different phases of swing and stance of foot gait cycle have been considered with sensing unit consisting of gyroscope and potentiometer. Arduino microcontroller has been incorporated to process the values received from the sensing unit using rule based algorithm. Sensor values are processed using ‘Extra Putty’ software and the results are observed at serial monitor. This project has been tested on a person and real time gait phases have been drawn. Gait phases of forward and reverse walk have been successfully identified. The work is useful to identify the balanced/unbalanced state of persons during normal walk.

Mimicking ankle angles continuously and accurately during gait cycle for lower limb prosthesis is of prime importance in prosthesis control. A robust control system is thus desired to mimic natural gait cycle of a healthy human for an amputee. This research focuses on simulating a control system which mimics natural ankle angles of human ankle biomechanics. The controller mimics a reference ankle angle input as output with maximum accuracy for lower limb prosthesis developed earlier. This control system can be further advanced to mimic human ankle torque and power profiles as well.

Diabetic Retinopathy (DR) is an eye disorder that progressively leads to vision loss due to high glucose causing impairment of retinal blood vessels (BVs). ‘Retinal Bright Lesions’ such as ‘Hard Exudates’ (HEs) are plasma leakages from rapture retinal capillaries. HEs appear as hard, waxy, yellowish deposits from tiny spots to fat patches and signify moderate-severe Non-Proliferative Diabetic Retinopathy (NPDR). This paper proposes a simple, compact and computationally inexpensive technique for detection and classification of HEs using Digital Image Processing Techniques on digital fundus images complements and Artificial Neural Networks (ANN). The proposed technique unfolds through five stages i.e. Pre-processing, coarse detection, optimization, features detection & extraction followed by classification. ‘Speed Up Robust Features’ (SURF) algorithm has been used for features detection & extraction while ‘Feed-Forward Back-propagation’ (FFBP) ANN has been used for classification. The proposed technique has yielded 98.7% ‘Sensitivity’ (SE), 97.5% ‘Specificity’ (SP) and 97.7% ‘Accuracy’ (AC) on ‘DIARETDB1’ fundus images.

Artificial immune systems (AIS) are intelligent algorithms derived from the principles inspired by the human immune system. In this study, electroencephalography (EEG) signals for four distinct motor movements of human limbs are detected and classified using a negative selection classification algorithm (NSCA). For this study, a widely studied open source EEG signal database (BCI IV-Graz dataset 2a, comprising nine subjects) has been used. Mel frequency cepstral coefficients (MFCCs) are extracted as selected features from recorded EEG signals. Dimensionality reduction of data is carried out by applying two hidden layered stacked auto-encoder. Genetic algorithm (GA) optimized detectors (artificial lymphocytes) are trained using negative selection algorithm (NSA) for detection and classification of four motor movements. The trained detectors consist of four sets of detectors, each set is trained for detection and classification of one of the four movements from the other three movements. The optimized radius of detector is small enough not to mis-detect the sample. Euclidean distance of each detector with every training dataset sample is taken and compared with the optimized radius of the detector as a nonself detector. Our proposed approach achieved a mean classification accuracy of 86.39% for limb movements over nine subjects with a maximum individual subject classification accuracy of 97.5% for subject number eight.

Doctors have a central role in managing patients across a multitude of clinical environments, which places them in the ideal position to identify systemic issues. Traditional medical training focuses on the knowledge and technical skills required; rarely are doctors trained in leadership, management or how to analyse and understand systems so as to design safer, better care. Quality improvement methodology is an approach that is known to enable improvement of the systems in which healthcare professionals work in order to provide safe, timely, evidence-based, equitable, efficient and patient-centred care. To address the current disparity, the Royal College of Physicians (RCP) has launched a Quality Improvement Hub, which will aim to support physicians to face the challenges of improving medical care, enabling them to navigate the tools with more confidence and share and implement the learning more swiftly.

The doctor’s role involves helping patients to understand their condition, including the anticipated benefits and risks of proposed treatments or omissions to treat. In order to treat, doctors require consent from patients but the duty to advise is equally strong if conservative management is appropriate. The recent judgement in the case of Montgomery has set a precedent for patient autonomy. However, doctors are still required to judge what risks they should disclose in their reasonable assessment of that patient and their specific situation. The General Medical Council reflects a consensus that the empowered autonomous patient is more likely to be satisfied with their clinical outcome than the passive victim of medical paternalism. Doctors, regardless of specialty, must counsel their patients adequately, paying particular attention to identifying material risks that are likely to be significant to their case.

For many who pertain to particular theological paradigms, their faith cannot be compartmentalised, but is mobilised to inform all aspects of their being, most notably their ethical and moral persuasions. As clinicians, the concept that there are good and bad deaths is already known; understanding the origin and depth of non-physical suffering, and aiming to alleviate it is not possible without learning the individual experiences and beliefs that go with it. Spiritual care forms a fundamental consideration in the endeavor to address the holistic experience of those patients receiving palliative care. Good palliative care seeks to promote the wellbeing and priorities of those with faltering health in a way that continues to support individualised notions of self-determination. The last few decades have resulted in a multicultural and multi-ethnic patient population. Addressing the spiritual and physical needs of patients allows healthcare professionals to deliver truly holistic care. Exploring and understanding the specific nuances of the five major religions of the UK provides healthcare professionals the opportunity to comfort the religiously observant patient at the end of life.

Brain Computer Interface (BCI) determines the intent of the user from a variety of electrophysiological signals. These signals, Slow Cortical Potentials, are recorded from scalp, and cortical neuronal activity is recorded by implanted electrodes. This paper is focused on design of an embedded system that is used to control the finger movements of an upper limb prosthesis using Electroencephalogram (EEG) signals. This is a follow-up of our previous research which explored the best method to classify three movements of fingers (thumb movement, index finger movement, and first movement). Two-stage logistic regression classifier exhibited the highest classification accuracy while Power Spectral Density (PSD) was used as a feature of the filtered signal. The EEG signal data set was recorded using a 14-channel electrode headset (a noninvasive BCI system) from right-handed, neurologically intact volunteers. Mu (commonly known as alpha waves) and Beta Rhythms (8–30 Hz) containing most of the movement data were retained through filtering using “Arduino Uno” microcontroller followed by 2-stage logistic regression to obtain a mean classification accuracy of 70%.

Background. Brain computer interface (BCI) is a combination of software and hardware communication protocols that allow brain to control external devices. Main purpose of BCI controlled external devices is to provide communication medium for disabled persons. Now these devices are considered as a new way to rehabilitate patients with impunities. There are certain potentials present in electroencephalogram (EEG) that correspond to specific event. Main issue is to detect such event related potentials online in such a low signal to noise ratio (SNR). In this paper we propose a method that will facilitate the concept of online processing by providing an efficient filtering implementation in a hardware friendly environment by switching to finite impulse response (FIR). Main focus of this research is to minimize latency and computational delay of preprocessing related to any BCI application. Four different finite impulse response (FIR) implementations along with large Laplacian filter are implemented in Xilinx System Generator. Efficiency of 25% is achieved in terms of reduced number of coefficients and multiplications which in turn reduce computational delays accordingly.

Proteases are widely used in leather processing, silk industry, diary meat processing, and preparation of organic fertilizer as well as for the liberation of silver from recycled X-ray films. Ultraviolet radiation mutagenesis of Bacillus subtilis IBL-04 was carried out for hyper producing strain development. Mutants of Bacillus subtilis were isolated and screened for selection of hyper producing mutant. Production of protease by the selected mutant BS-90 (treated for 90 min) was optimized by varying pH, temperature, and inoculum size and fermentation time simultaneously in Response Surface Method (RSM) under Central Composite Design (CCD). The mathematical response model is considered to be reliable with an R2 value of 0.9842. The adjusted R2 value was 0.9695 suggesting a significant model by determining the close relationship to the actual R2 value. Predicted R2 value shown in this model was 0.9133. The "Pred R-Squared" of 0.9133 is close as to the "Adj R-Squared" of 0.9695 as expected. The ratio of 22.60 attained in this model represents an adequate signal. The calculated C.V was 3.25 which indicate the good level of model precision and reliability. The maximum enzyme activity was 95.89 (IU/mL) at optimum conditions pH 8, Temperature 50°C, Inoculum size 2.5 mL and fermentation time 72 h. These characteristics render its potential use in detergent industries for detergent formulation.

Understanding and learning from hospital deaths is an important component of good clinical practice but current approaches and measures are complex, controversial and difficult to understand. Patients who die are not a homogeneous group but fall into three distinct categories; most learning will be achieved by recognising this and investigating categories of deaths in different ways, relying heavily on qualitative approaches. Numerical measures of overall hospital mortality, such as hospital standardised mortality ratio (HSMR) or measures of 'preventable' deaths, are most unlikely to be helpful at a hospital level and may even give false reassurance, as accuracy of measurement is strongly influenced by factors apart from quality of care.

Recent advancements in brain computer-interfacing (BCI) and neuro-robotics have played an indispensable role for people suffering from neural injuries to expect better quality of life by restoring sensory functions and replacement of neuro-muscular pathways as BCI systems work on imagination of movements to control prosthetic limbs. In this research, multiple combinations of features and classifiers have been used to classify electroencephalographic (EEG) signals in order to acquire maximum classification accuracy for a four class EEG based BCI system. Knee and ankle joint movements are executed in a predefined manner during which EEG signals are acquired from sensorimotor cortex of four healthy test subjects, using Emotiv headset. After removing artifacts, five features; average band power, peak, kurtosis, mean and skewness are calculated. Multiple combinations of calculated features are used to classify ankle dorsi-planter flexion and knee extension-flexion movements using five different classifiers; Naive Bayes, A-nearest neighbor (ANN), linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), and support vector machine (SVM). It was found that features set comprising; average band power, peak and mean value yielded significantly higher classification accuracy of 81.31% among all other combinations whereas in classifiers, maximum accuracy of 81.48% is obtained through LDA as compared to other classifiers. These novel findings clearly exhibit the feasibility of achieving good classification accuracies using average band power, mean and peak values of EEG signals as feature vectors and LDA as classifier.

In lower limb prosthetics, pistoning and increased volume change of residual limb inside the socket is the main problem. To overcome this problem, a vacuum suspension system has been developed to hold the limb firmly into the socket. This paper aims at the design of the suction geometry made of neoprene rubber to create a vacuum forming system for lower limb prosthetics. The designed geometry was integrated with bionic foot for below knee amputation. The proposed design was modeled using SOLIDWORKS 14 and analyzed by using finite element method (FEM) in ANSYS 14.5. The deformed geometry was further used to calculate the change in internal volume which gives the volume of air evacuated during each step. The value of required pressure to create a vacuum in the socket is −69 KPa (508 mmHg). Further calculations were made to find out the steps to achieve such pressure.

The design and fabrication of a Polyvinylidene fluoride (PVDF) based, mouse (or rodent) whisker mimicking, tactile sensor is presented. Unlike previous designs reported in the literature, this sensor mimics the mouse whisker not only mechanically, but it also makes macro movements just like a real mouse whisker in a natural environment. We have developed a mathematical model and performed finite element analysis using COMSOL, in order to optimise the whisker to have the same natural frequency as that of a biological whisker. Similarly, we have developed a control system that enables the whisker mimicking sensor to vibrate at variable frequencies and conducted practical experiments to validate the response of the sensor. The natural frequency of the whisker can be designed anywhere between 35 and 110 Hz, the same as a biological whisker, by choosing different materials and physical dimensions. The control system of this sensor enables the whisker to vibrate between 5 and 236 Hz.

Transtibial amputation may be caused by a disease or an accident. The rehabilitation for this type of amputation is to replace the amputated part by a prosthetic limb. Most of the commercially available DER (Dynamic Energy return) prosthetic feet contain soft components, that store and release energy. In contrast, the proposed model of prosthetic foot stores and release energy by means of material properties and geometry. Traditionally, the performance analysis of prosthetic feet required the existence of prototype. However, latest numerical techniques allow analysis, during the design stage. ISO-22675 has proposed experimental setup and standard for the analysis of any prosthetic foot. In this work, instead of using experimental setup and physical prototype, the proposed foot has been modeled, analyzed, and optimized using FEM (finite element method) as per the boundary conditions specified by ISO-22675, during the design process. The aim of his paper is to develop a low-cost, fixed prosthetic lower limb, DER foot, that is within the buying power of lower income class and its performance and features are as per ISO standards. The foot has been designed using displacement and stiffness parameters of Niagara FootTM and analyzed as per ISO Protocol 22675 using FEM analysis.

Recent developments in Brain Computer Interfacing (BCI) and neuroprosthetics have played a vital role for disable people to expect better life quality. In this contribution Electroencephalographic (EEG) signals acquired from six healthy test subjects, are used for the offline analysis of BCI through classification of four lower limb movements including talocrural (ankle) joint dorsi-planter flexion and knee joint extension-flexion. Fourteen channel Emotive EPOC head set is used to acquire EEG signals from sensorimotor cortex area of brain, using a particular data acquisition timeline protocol. Features are extracted in time domain from raw EEG data. Power spectral density, variance, mean value and kurtosis features are applied on raw EEG signals. Multiple classification algorithms are implemented for discrimination of four lower limb movements within data set. The paper uses Quadratic discriminant analysis, Naive Bayes and Support vector machine classifiers to stratify the movement intent of lower limb. Maximum classification accuracies achieved through various classifiers are; 86.35% with average band power & QDA, 84.38% with mean value & QDA and 78.13% with power spectral density & Quadratic-SVM. The presented findings are optimistic in making the path easier towards the development of BCIs with rich EEG based control signals using noninvasive technology.

Cure sometimes, treat often and comfort always – Hippocrates As clinicians, we are in a uniquely privileged position; we have passive admittance to the most private aspects of human life and often find ourselves silent observers of death. Sometimes we may be left feeling powerless and that maybe we have failed, given the expectations of modern medicine. In this issue of the Future Healthcare Journal we explore the complexities of creating sustainably resourced end-of-life and palliative care services. These require a foundation of specialist education and skills to empower clinicians, not …

Once a highly competitive specialty, competition rates (CR) for core medical training (CMT) have been falling since 2013. Based on CR (number of applicants received per advertised post), CMT ranked 14th out of 16 specialties in 2016. We sought to further understand and provide evidence for the endemic roots of low morale among all grades of junior doctors.