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This study aims to examine the essence of the principle of balance in the agreement and dissect the position of the principle of balance as the legal reason for the cancellation of an agreement in order to realize equitable law in Indonesia. The research method used in this research is doctrinal research by using a statutory approach, a conceptual approach, and a comparative approach. Based on the research conducted, it was found that the essence of the principle of balance is a principle that emphasizes the existence of balance or equality of the rights and obligations of the parties in an agreement. Furthermore, the principle of balance can be used as a reason to cancel an agreement in Indonesia that has fulfilled the conditions for a valid agreement in Article 1320 jo 1338 of the Indonesian Civil Code as long as it is interpreted in the form of abuse of circumstances.

Background Referral systems support the mobility of patients to ensure they receive adequate healthcare services from the appropriate provider. The major triggers for referrals are limited resources and skills at the lower level of care. An electronic referral system has the potential to considerably improve communication between primary care providers and specialists by submitting standardized and complete electronic referral requests which contain all relevant information needed for a smooth coordination, continuity of care, and facilitating urgent cases. The main objective of this study is to investigate the perception of healthcare providers regarding the existing paper-based referrals and their suggestions on the development of the electronic referral system in order to improve the health information exchange, the coordination and the continuity of care. Methods A mixed method approach was used to examine healthcare providers' perception of the referral system. For the quantitative analysis, a questionnaire was used to collect data from July to October 2021. A purposive sampling was used to select healthcare providers and a descriptive analysis was performed using SPSS 27. For the qualitative study design two focused group discussions were conducted, data were transcribed and analyzed using NVivo R 1.6. Results Overall, 345 referral constituents, including 202 mandatory and 143 optional ones were confirmed by the healthcare providers. The qualitative findings concerned two themes. The first one elucidated the challenges with the existing paper-based referral system pointing out delays in the referral process; incompleteness of paper-based referral sheet; referral triage; premature referral; absence or incomplete counter referral; patient privacy; and language problems. The second theme suggested features to be included in the electronic referral system, the procedures to follow during the transition phase, proposed mandatory and optional constituents, predicted challenges and expected benefits. Conclusion This study gathered perceptions of healthcare providers on the existing referral system, further elucidated their expectations on the electronic referral system and proposed features to be considered in its design. Finally, leads to a proposed design of an electronic external referral form.

Over the past few years, Deep learning (DL) has revolutionized the field of data analysis. Not only are the algorithmic paradigms changed, but also the performance in various classification and prediction tasks has been significantly improved with respect to the state-of-the-art, especially in the area of computer vision. The progress made in computer vision has produced a spillover in many other domains, such as biomedical engineering. Some recent works are directed towards surface electromyography (sEMG) based hand gesture recognition, often addressed as an image classification problem and solved using tools such as Convolutional Neural Networks (CNN). This paper extends our previous work on the application of the Hilbert space-filling curve for the generation of image representations from multi-electrode sEMG signals, by investigating how the Hilbert curve compares to the Peano- and Z-order space-filling curves. The proposed space-filling mapping methods are evaluated on a variety of network architectures and in some cases yield a classification improvement of at least 3%, when used to structure the inputs before feeding them into the original network architectures.

NeuroEvolution (NE) refers to a family of methods for optimizing Artificial Neural Networks (ANNs) using Evolutionary Computation (EC) algorithms. NeuroEvolution of Augmenting Topologies (NEAT) is considered one of the most influential algorithms in the field. Eighteen years after its invention, a plethora of methods have been proposed that extend NEAT in different aspects. In this article, we present a systematic literature review (SLR) to list and categorize the methods succeeding NEAT. Our review protocol identified 232 papers by merging the findings of two major electronic databases. Applying criteria that determine the paper's relevance and assess its quality, resulted in 61 methods that are presented in this article. Our review article proposes a new categorization scheme of NEAT's successors into three clusters. NEAT-based methods are categorized based on 1) whether they consider issues specific to the search space or the fitness landscape, 2) whether they combine principles from NE and another domain, or 3) the particular properties of the evolved ANNs. The clustering supports researchers 1) understanding the current state of the art that will enable them, 2) exploring new research directions or 3) benchmarking their proposed method to the state of the art, if they are interested in comparing, and 4) positioning themselves in the domain or 5) selecting a method that is most appropriate for their problem.

BACKGROUND When preparing for the resection of an intracranial lesion, its borders and optimal approach are often determined using neuronavigation with a tracked pointer. This can sometimes prove challenging, especially for deep-seated lesions. Augmented reality (AR) can simplify and improve this step by directly displaying the lesion on the patient's skin. METHODS A proprietary inside-out infrared tracking solution was developed, allowing for heads-up displaying AR scenes on the Microsoft HoloLens II without the need for an external tracking camera or computer. We included twenty patients with an intracerebral lesion planned for resection. After semi-automatic hologram-to-patient registration, different participants marked the lesion outlines on the patient’s skin, consecutively aided by the Brainlab neuronavigation system and the HoloLens. Each registration on both systems provided a registration transform that was compared for accuracy and consistency. Participant performance was quantified in terms of duration and accuracy for both patient registration and lesion delineation, and compared to expert performance. RESULTS When using AR both registration and delineation were significantly faster than with conventional neuronavigation (p = 0.02 and p < 0.001, respectively, and p < 0.001 for the total duration), taking 79.23 ± 17.48 and 39.58 ± 39.10 seconds while neuronavigation required 96.61 ± 24.54 and 90.80 ± 44.09 seconds. AR had a registration offset of 3.3mm and 3.4°, and was more consistent compared to neuronavigation. AR facilitated more accurate and detailed lesion delineation, while neuronavigation often overestimated lesion size. CONCLUSION Augmented reality provides a faster and more accurate alternative for resection planning. Lesion delineation was more intuitive while retaining high accuracy. Future research should focus on further intraoperative implementations.

Decision support clinical pathways are used to improve the performance of the health-care management system. An effective clinical pathway(CP) helps to know the optimal treatment route that patients will follow. The extent of the CP goes from the first contact in the health-center(or hospital) to the completion of the treatment until the patient is dismissed. Up to now, far too little attention has been paid to a systematic review, the research and the development of CPs in a low resource setting(LRS). The main focus has been primarily on data-intensive environments where there is no shortage of resources. A systematic search in PubMed and Web of Science was conducted for bundling and categorizing the relevant approaches for LRS. Of 45 full reviewed articles, 25/45(55.6%) and 20/45(44.4%) of the studies were conducted using knowledge-based and data-driven approaches respectively. Among the knowledge-based studies, 9/25(36%) were reporting a stand-alone applications,10/25(40%) attempting to deliver a paper-based CP, and the remaining focus was on web-based applications. In the data-driven approaches,15/20(75%) tried to integrate with the electronic health record. The paper identifies the approaches for executing CPs and highlights key considerations for building LRS-compatible CPs. Data-driven CPs do not only resolve the challenges of improving the quality of existing knowledge-based CPs, but also enable evidence-based practice, improve outcomes, and reduce cost and delay.

Though a clinical pathway is one of the tools used to guide evidence-based healthcare, promoting the practice of evidence-based decisions on healthcare services is incredibly challenging in low resource settings (LRS). This paper proposed a novel approach for designing an automated and dynamic generation of clinical pathways (CPs) in LRS through a hybrid (knowledge-based and data-driven based) algorithm that works with limited clinical input and can be updated whenever new information is available. Our proposed approach dynamically maps and validate the knowledge-based clinical pathways with the local context and historical evidence to deliver a multi-criteria decision analysis (concordance table) for adjusting or readjusting the order of knowledge-based CPs decision priority. Our finding shows that the developed approach successfully delivered probabilistic-based CPs and found a promising result with Jimma Health Center “pregnancy, childbearing, and family planning” dataset.

Deep learning has transformed the field of data analysis by dramatically improving the state of the art in various classification and prediction tasks, especially in the area of computer vision. In biomedical engineering, a lot of new work is directed toward surface electromyography (sEMG)-based gesture recognition, often addressed as an image classification problem using convolutional neural networks (CNNs). In this paper, we utilize the Hilbert space-filling curve for the generation of image representations of sEMG signals, which allows the application of typical image processing pipelines such as CNNs on sequence data. The proposed method is evaluated on different state-of-the-art network architectures and yields a significant classification improvement over the approach without the Hilbert curve. Additionally, we develop a new network architecture (MSHilbNet) that takes advantage of multiple scales of an initial Hilbert curve representation and achieves equal performance with fewer convolutional layers.

Frailty and hypertension are interrelated, but it remains unclear whether this relationship is modified by antihypertensive drugs.A systematic review of PubMed and Web of Science databases was performed to review the influence of hypertension management on preventing the occurrence or progression of frailty in older people aged 65 and over. Studies providing information on this association regardless of the study setting, or definition of hypertension and frailty were included. Among the initial 2298 articles identified, 7 were included in the review. Three observational studies assessed the association between frailty and hypertension. Two of them reported no relationship between Aldosterone Antagonists use and frailty prevention. No relationship between BP and incidence frailty after adjustment for hypertension treatment was observed in two other articles. An indirect relationship was reported in the RCTs included. Higher AT11RaAb levels (control group), can lead to a generalized weakness/frailty risk shown by a decrease in grip strength (r = -0.57, p 0.005) and walking speed (r = - 0.47, p 0.005). No significant differences between int-hypertensive intervention and control were observed in frailty status after a 12-weeks follow-up after applying three different frailty measurement tools in the other RCT.Based on the results of this systematic review we conclude that BP and frailty occur together but whether the treatment with anti-hypertensive drugs modifies this relationship remains unclear and needs to be further investigated.

The spray characteristics of an air-assisted electrostatic atomiser are experimentally investigated using a shadowgraphy particle/droplet image analysis (PDIA) technique. Tests are conducted for varied rates of liquid- and airflow, with and without a voltage applied to the nozzle electrode. The found droplet sizes neatly match the Rosin–Rammler distribution, and an expression for the mean diameter as a function of the flow rates is given. Notably, the electrode voltage (up to 40 kV) has no measurable impact on the droplet size and velocity, and the energy efficiency of the sprayer is found to decrease with increases in both liquid- and airflow.

Introduction: Low grip work and high feelings of self-perceived fatigue could be an early characteristic of decline in reserve capacity, which comes to full expression as physical frailty in a later stage. When grip work and self-perceived fatigue can be identified as characteristics differentiating between robustness and pre-frailty it might allow to identify pre-frailty earlier. Therefore, this study aimed to investigate whether the combination of grip work and self-perceived fatigue is related to pre-frailty in well-functioning older adults aged 80 and over. Methods: Four-hundred and five community-dwelling older adults aged 80 and over (214 robust and 191 pre-frail) were assessed for muscle endurance (grip Work corrected for body weight (GW_bw)), self-perceived fatigue (MFI-20) and frailty state (Fried Frailty Index, FFI). A Capacity to Perceived Vitality ratio (CPV) was calculated by dividing GW_bw by the MFI-20 scores. ANCOVA analysis (corrected for age and gender) was used to compare robust and pre-frail older adults, and binary logistic regressions were applied to analyze the relationship between CPV and pre-frailty status. Results: Pre-frail older adults who scored negative on the exhaustion item of the FFI still showed significantly lower GW (p < 0.001), CPV ratios (p < 0.001) and higher self-perceived fatigue (p < 0.05) compared to the robust ones. The likelihood for pre-frailty related significantly to higher age, being men and lower CPV ratios. In women, every unit increase in CPV ratio decreased the likelihood for pre-frailty by 78% (OR 0.22; 95% CI: 0.11–0.44), for men this effect was less strong (34%, OR 0.66; 95% CI: 0.47–0.93). Conclusions: Pre-frail community-dwelling persons aged 80 years and over without clinical signs of exhaustion on the FFI still experience significantly higher fatigue levels (lower Grip Work, higher self-perceived fatigue and lower CPV levels) compared to robust ones. CPV ratio could therefore be a good tool to identify subclinical fatigue in the context of physical (pre-)frailty.

OBJECTIVE The traditional freehand technique for external ventricular drain (EVD) placement is most frequently used, but remains the primary risk factor for inaccurate drain placement. As this procedure could benefit from image guidance, the authors set forth to demonstrate the impact of augmented-reality (AR) assistance on the accuracy and learning curve of EVD placement compared with the freehand technique. METHODS Sixteen medical students performed a total of 128 EVD placements on a custom-made phantom head, both before and after receiving a standardized training session. They were guided by either the freehand technique or by AR, which provided an anatomical overlay and tailored guidance for EVD placement through inside-out infrared tracking. The outcome was quantified by the metric accuracy of EVD placement as well as by its clinical quality. RESULTS The mean target error was significantly impacted by either AR (p = 0.003) or training (p = 0.02) in a direct comparison with the untrained freehand performance. Both untrained (11.9 ± 4.5 mm) and trained (12.2 ± 4.7 mm) AR performances were significantly better than the untrained freehand performance (19.9 ± 4.2 mm), which improved after training (13.5 ± 4.7 mm). The quality of EVD placement as assessed by the modified Kakarla scale (mKS) was significantly impacted by AR guidance (p = 0.005) but not by training (p = 0.07). Both untrained and trained AR performances (59.4% mKS grade 1 for both) were significantly better than the untrained freehand performance (25.0% mKS grade 1). Spatial aptitude testing revealed a correlation between perceptual ability and untrained AR-guided performance (r = 0.63). CONCLUSIONS Compared with the freehand technique, AR guidance for EVD placement yielded a higher outcome accuracy and quality for procedure novices. With AR, untrained individuals performed as well as trained individuals, which indicates that AR guidance not only improved performance but also positively impacted the learning curve. Future efforts will focus on the translation and evaluation of AR for EVD placement in the clinical setting.

Objective: In DSM-5, Section III, the Level of Personality Functioning (LPF) was proposed as a severity index of personality disorders (PDs), but as it reflects both trait-like (availability) and state-like (accessibility) features, of which, moreover, the relationship with the experience of patients is unclear, we critically examined LPF in patients with general psychopathology. Method: This study compared the validity of the direct Inventory of Personality Organization (IPO), and the indirect Differentiation-Relatedness Scale (DRS) LPF-measure, in relation to measures of intrapersonal and interpersonal functioning. The sample consisted of 70 inpatients with general psychopathology and no primary PDs. Associations of both measures with DSM-PDs were examined, with and without controlling for clinical distress. Results: The IPO was significantly related to age and clinical distress. When controlling for clinical distress, the IPO was still associated with cluster A (odd) and B (erratic) PD features, high levels of self-criticism, conflict in relationships and low levels of adaptive coping strategies. The DRS was only related to the schizotypical PD. Conclusions: In patients with general psychopathology, both the IPO and the DRS, appear to have limitations in measuring LPF. The IPO seems to be prone to state effects, although correlations with PDs remained significant when controlling for clinical distress. The DRS seemed to be more independent from clinical distress but was unexpectedly unrelated to features of personality pathology. DRS reflects availability, while IPO also reflects different degrees of accessibility of LPF in PDs. ispartof: PSYCHIATRY-INTERPERSONAL AND BIOLOGICAL PROCESSES vol:83 issue:1 pages:84-93 ispartof: location:United States status: published

Reasonableness and fairness are keystones of law. They are implemented broadly and important doctrine for civil law. However, the open nature of reasonableness and fairness allow various interpretations, influenced by the legal system, legal tradition, jurisprudence or measured on a case per case basis. Indonesia recognizes more than one kind of a legal system, making it a legal pluralist State. This article aims to describe the dynamics of the reasonableness and fairness principle within a pluralistic legal system. Indonesian inheritance laws use three different legal systems: Adat, Islam, and civil law, each provides distinctive perspectives of reasonableness and fairness. These differences may lead to a clash of interpretation or it may leave a wide room of discretion for the judges. Court judgments are analyzed to examine the implementation of such dynamics in practice. Finally, the outcome of the paper concludes whether the differences shall be embraced or whether there is a need to agree upon what is ‘reasonable’ and ‘fair’.

Major hurdles for Microsoft's HoloLens as a tool in medicine have been accessing tracking data, as well as a relatively high-localisation error of the displayed information; cumulatively resulting in its limited use and minimal quantification. The following work investigates the augmentation of HoloLens with the proprietary image processing SDK Vuforia, allowing integration of data from its front-facing RGB camera to provide more spatially stable holograms for neuronavigational use. Continuous camera tracking was able to maintain hologram registration with a mean perceived drift of 1.41 mm, as well as a mean sub 2-mm surface point localisation accuracy of 53%, all while allowing the researcher to walk about a test area. This represents a 68% improvement for the later and a 34% improvement for the former compared with a typical HoloLens deployment used as a control. Both represent a significant improvement on hologram stability given the current state-of-the-art, and to the best of the authors knowledge are the first example of quantified measurements when augmenting hologram stability using data from the RGB sensor.

In the Dutch capitalistic consumer society, things are not made to stand the test of time, but to be replaced by other things within the foreseeable future. They are made to be thrown away, and quite often as quickly as possible, because it is this characteristic that guarantees a new purchase. In this contribution the authors will focus on a white T-shirt. The main questions are: What is the current practice in the Netherlands regarding the disposal, separation, reusing, and recycling of this Dutch white trash and how does the legal framework regulate the matter of RMG waste?.

Deep learning (DL) has transformed the field of data analysis by dramatically improving the state of the art in various classification and prediction tasks. Especially in the area of computer vision and speech processing, DL has recently demonstrated better performance and generalisation properties, compared to classical machine learning approaches, which are based on the extraction of hand-crafted model-based features followed by classification. Hand gestures and speech constitute two of the most important modalities in human-to-human communication and man-machine interaction. In biomedical engineering, a lot of new work is directed towards electromyography-based gesture recognition. In this paper, we present a brief overview of DL methods for electromyography-based hand gesture recognition and then we select from literature a simple model based on Convolutional Neural Networks that we consider as the baseline model. The proposed modifications to the baseline model yield a 3% classification improvement. In the current paper, we concentrate on the explanatory analysis of this performance improvement. An ablation study identifies which modifications are the most important ones, and label smoothing is investigated to verify if the results can be improved by reducing a priori bias. The analysis helps in understanding the limitations of the model and exploring new ways to improve the performance.

Summary Background To date, the accuracy of bio-impedance (BIA) to assess body composition & sarcopenia in persons aged 80 and over remains unclear. Objective We aimed to evaluate the agreement between dual energy X-ray absorptiometry (DXA) and BIA equations to determine lean mass, as well as their suitability to identify sarcopenia. Design 174 community dwelling well-functioning persons (83 women, 91 men) aged 80 and over were included. Appendicular lean mass (ALM) was predicted using BIA-based equations available in literature, and compared to DXA outcomes. Through cross-validation and stepwise multiple linear regression, a new ALM-formula was generated suitable for this population. Results Literature-based BIA equations systematically overestimated ALM. The new prediction formula that we propose for the 80+ is: ALM = 0,827+(0,19*Impedance Index)+(2,101*Sex)+(0,079*Weight); R2 = 0,888; SEE = 1,450 kg. Sarcopenia classification based on our new BIA equation for ALM showed better agreement with DXA (k ≥ 0,454) compared to literature-based BIA equations (k Conclusions Despite the high correlation between both methods, literature-based BIA equations consistently overestimate ALM compared to DXA in persons aged 80 and over. We proposed a new equation for ALM, reaching higher agreement with DXA and thus improving the accuracy of BIA for this specific age group.

Purpose To identify the different fatigue items in existing frailty scales. Methods PubMed, Web of Knowledge and PsycINFO were systematically screened for frailty scales. 133 articles were included, describing 158 frailty scales. Fatigue items were extracted and categorized in 4 fatigue constructs: “mood state related tiredness”, “general feeling of tiredness”, “activity based feeling of tiredness” and “resistance to physical tiredness”. Results 120 fatigue items were identified, of which 100 belonged to the construct “general feeling of tiredness” and only 9 to the construct “resistance to physical tiredness”. 49,4% of the frailty scales included at least 1 fatigue item, representing 15 ± 9,3% of all items in these scales. Fatigue items have a significantly higher weight in single domain (dominantly physical frailty scales) versus multi domain frailty scales (21 ± 3.2 versus 10.6 ± 9.8%, p= Conclusion Fatigue is prominently represented in frailty scales, covering a great diversity in fatigue constructs and underlying pathophysiological mechanisms by which fatigue relates to frailty. Although fatigue items were more prevalent and had a higher weight in physical frailty scales, the operationalization of fatigue leaned more towards psychological constructs. This review can be used as a reference for choosing a suitable frailty scale depending on the type of fatigue of interest.

Objective Although dual-task paradigm is commonly used in rehabilitation there is, currently, a lack of information about quantitative balance assessment performed in dual-task conditions. The aim of this study is to validate the use of the Nintendo Wii Balance Board (WBB) to assess balance modifications induced by dual-task activity in healthy subjects. Material and Methods Seventy-five healthy participants (37 ± 14 years old, 173± 8 cm, 73± 16 kg, 35 women) participated in this study. Balance was recorded in control situation (baseline) and under two different cognitive tasks (verbal fluency and calculation). Each trial lasted for 60 seconds and were performed with eyes open and closed. Nine parameters were extracted based on center of pressure displacement based on a previously-validated method. ANOVA tests were used to compare the different conditions followed by Bonferroni’s corrections. Protocol was repeated one week after to assess the reproducibility. Results Statistically significant differences were found comparing eyes open and closed situation. Concerning the influence of dual-tasks statistically significant decrease of the balance was observed for both eyes open and closed conditions (increase of the total sway and the speed of displacement). Concerning the reproducibility all parameters shows good to excellent ICC values for the different conditions. Conclusion In this study, we demonstrated the validity of the WBB to detect the effect of the dual-task on balance in healthy subjects. Further studies are needed to determine if such a kind of evaluation can be used in clinics with subjects at risk of falling.

Objective The aim of this work was to determine if scoring mechanisms embedded in cognitive mobile games (CMG) designed for cognitive training are sensitive enough to detect changes in cognitive function induced by various acute respiratory loads in healthy subjects. Material and methods Thirty healthy subjects participated in this study (25 ± 4 years old, 13 women). A set of three brief CMG was used to assess cognitive function (Rush Back, Must Sort, True Color) in control situation and at four different inspiratory loads (through a mouthpiece without resistance and with 10, 50 and 70% of the maximal inspiratory pressure). Results Statistically significant decreases in CMG scores were observed in Rush Back (p = 0.032) and True Color (p = 0.002) when the subjects breathed through the mouthpiece without resistance compared to the control condition. A statistical difference was observed for Must Sort (p = 0.003) between baseline and 10% of Inspiratory Threshold Load (ITL). Significant differences for the three games were observed between the baseline and 50% of ITL and between baseline and 70% of ITL. Conclusion CMG designed for cognitive training are sensitive in detecting transitional changes in cognitive function induced by low, medium and high acute respiratory loads in healthy subjects. This offers interesting new possibilities for the assessment and long-term follow-up of patients suffering from chronic respiratory disease, since this type of assessment could easily be completed independently by patients in their own homes, and could be combined with rehabilitation exercises as an evaluative measure.

Research on human eye image processing and iris recognition has grown steadily over the last few decades. It is important for researchers interested in this discipline to know the relevant datasets in this area to (i) be able to compare their results and (ii) speed up their research using existing datasets rather than creating custom datasets. In this paper, we provide a comprehensive overview of the existing publicly available datasets and their popularity in the research community using a bibliometric approach. We reviewed 158 different iris datasets referenced from the 689 most relevant research articles indexed by the Web of Science online library. We categorized the datasets and described the properties important for performing relevant research. We provide an overview of the databases per category to help investigators conducting research in the domain of iris recognition to identify relevant datasets.

The range of applications of electromyography-based gesture recognition has increased over the last years. A common problem regularly encountered in literature is the inadequate data availability. Data augmentation, which aims at generating new synthetic data from the existing ones, is the most common approach to deal with this data shortage in other research domains. In the case of surface electromyography (sEMG) signals, there is limited research in augmentation methods and quite regularly the results differ between available studies. In this work, we provide a detailed evaluation of existing (i.e., additive noise, overlapping windows) and novel (i.e., magnitude warping, wavelet decomposition, synthetic sEMG models) strategies of data augmentation for electromyography signals. A set of metrics (i.e., classification accuracy, silhouette score, and Davies&ndash, Bouldin index) and visualizations help with the assessment and provides insights about their performance. Methods like signal magnitude warping and wavelet decomposition yield considerable increase (up to 16%) in classification accuracy across two benchmark datasets. Particularly, a significant improvement of 1% in the classification accuracy of the state-of-the-art model in hand gesture recognition is achieved.

In recent years, Deep Learning methods have been successfully applied to a wide range of image and speech recognition problems highly impacting other research fields. As a result, new works in biomedical engineering are directed towards the application of these methods to electromyography-based gesture recognition. In this paper, we present a brief overview of Deep Learning methods for electromyography-based hand gesture recognition along with an analysis of a modified simple model based on Convolutional Neural Networks. The proposed network yields a 3% improvement on the classification accuracy of the basic model, whereas the analysis helps in understanding the limitations of the model and exploring new ways to improve the performance.

There is a link between breathing and balance and posture. When the inspiratory loads are increased by pathologies, there is a decrease of postural control. The increase of the inspiratory load on respiratory muscles is a common feature in various chronic pulmonary pathologies. Consequently, the balance of those patients is likely affected., SCOPUS: ar.j, info:eu-repo/semantics/published

Optoelectronic devices are the gold standard for 3D evaluation in clinics, but due to the complexity of this kind of hardware and the lack of access for patients, affordable, transportable, and easy-to-use systems must be developed to be largely used in daily clinics. The Kinect™ sensor has various advantages compared to optoelectronic devices, such as its price and transportability. However, it also has some limitations: (in)accuracy of the skeleton detection and tracking as well as the limited amount of available points, which makes 3D evaluation impossible. To overcome these limitations, a novel method has been developed to perform 3D evaluation of the upper limbs. This system is coupled to rehabilitation exercises, allowing functional evaluation while performing physical rehabilitation. To validate this new approach, a two-step method was used. The first step was a laboratory validation where the results obtained with the Kinect™ were compared with the results obtained with an optoelectronic device; 40 healthy young adults participated in this first part. The second step was to determine the clinical relevance of this kind of measurement. Results of the healthy subjects were compared with a group of 22 elderly adults and a group of 10 chronic stroke patients to determine if different patterns could be observed. The new methodology and the different steps of the validations are presented in this paper., SCOPUS: ar.j, info:eu-repo/semantics/published

NeuroEvolution (NE) is a powerful method that uses Evolutionary Algorithms (EAs) for learning Artificial Neural Networks (ANNs). However, NE's performance is determined by the definition of dozens of parameters that guide the search of the EAs. In this study we apply automatic algorithm configuration for tuning the parameters of a NE method in an offline matter. The tuned NE method is then used to evolve the weights, topology and activation functions of ANNs while performing feature selection and its performance is compared to the case of using default parameters. We show that tuning the parameters results in NE methods able to solve the problems with 100% accuracy in significantly less generations.

The development of portable haematology analysers receives increased attention due to their deployability in resource-limited or emergency settings. Lens-free in-line holographic microscopy is one of the technologies that is being pushed forward in this regard as it eliminates complex and expensive optics, making miniaturisation and integration with microfluidics possible. On-chip flow cytometry enables high-speed capturing of individual cells in suspension, giving rise to high-throughput cell counting and classification. To perform a real-time analysis on this high-throughput content, we propose a fast and robust framework for the classification of leukocytes. The raw data consists of holographic acquisitions of leukocytes, captured with a high- speed camera as they are flowing through a microfluidic chip. Three different types of leukocytes are considered: granulocytes, monocytes and T-lymphocytes. The proposed method bypasses the reconstruction of the holographic data altogether by extracting Zernike moments directly from the frequency domain. By doing so, we introduce robustness to translations and rotations of cells, as well as to changes in distance of a cell with respect to the image sensor, achieving classification accuracies up to 96.8%. Furthermore, the reduced computational complexity of this approach, compared to traditional frameworks that involve the reconstruction of the holographic data, allows for very fast processing and classification, making it applicable in high-throughput flow cytometry setups.

Purpose: Cerebral palsy (CP) leads to various clinical signs mainly induced by muscle spasticity and muscle weakness. Among these ones impaired balance and posture are very common. Traditional physical therapy exercise programs are focusing on this aspect, but it is difficult to motivate patients to regularly perform these exercises, especially at home without therapist supervision. Specially developed serious games (SG) could therefore be an interesting option to motivate children to perform specific exercise for balance improvement. Method: Ten CP children participated in this study. Patients received four sessions of SG included into conventional therapy (1 session of 30 min a week during 4 weeks). Trunk control and balance were assessed using Trunk Control Motor Scale (TCMS) before and after interventions. Results: Children presented a significant improvement in TCMS global score after interventions [37.6 (8.7) and 39.6 (9.5) before and after interventions, respectively, p = 0.04]. Conclusion: SG could therefore be an interesting option to integrate in the conventional treatment of CP children.Implication for RehabilitationCerebral palsy (CP) leads to balance issues.Rehabilitation exercises are not performed (enough) at home.Serious games (SG) could increase patients’ motivation.SG increase balance control of CP children. Cerebral palsy (CP) leads to balance issues. Rehabilitation exercises are not performed (enough) at home. Serious games (SG) could increase patients’ motivation. SG increase balance control of CP children.