Development and application of the Rapha® device for the treatment of diabetic foot ulcers.

INTRODUCTION: Transforming medical research into real-world healthcare solutions is a complex endeavor that may benefit from the synergy between academic research, governmental support, and industry innovation. OBJECTIVES: In this article we delve into the framework of Translational Medical Research (TMR) in Brazil, elucidating the possible interplay between public universities and other pivotal stakeholders in the translational journey. METHODS: Our focal point is the Rapha® device, an innovative medical technology, as we explore its ethical and regulatory journey. We seek to understand the environment that shapes healthcare technology development through a mixed-methods research design, combining policy analysis with stakeholder interviews. RESULTS: The research begins by examining public policies, aiming to carve out a socially inclusive and advantageous ecosystem. We then highlight the pivotal components-steps, milestones, stakeholders, and policies that underpin the TMR process. Our findings reveal that while TMR frequently culminates in patents and technology transfer agreements, specific regulatory and production challenges exist, particularly during transitioning from the T3 (clinical trials) to T4 (public health practice) phase. We provide insights into its translational progression by tracing the developmental stages from foundational research (T0) to clinical trials (T3) for the Rapha® device. CONCLUSION: Ultimately, this study underscores TMR's vital role in advancing healthcare access and posits that academic institutions can significantly influence the creation of ethically robust, regulated, and impactful medical innovations, contributing meaningfully to global healthcare.

Estimation of Joint Angle From sEMG and Inertial Measurements Based on Deep Learning Approach.

Continuous kinematics estimation from surface electromyography (sEMG) allows more natural and intuitive human-machine collaboration. Recent research has suggested the use of multimodal inputs (sEMG signals and inertial measurements) to improve estimation performance. This work focused on assessing the use of angular velocity in combination with myoelectric signals to simultaneously and continuously predict 12 joint angles in the hand. Estimation performance was evaluated for five functional and grasping movements in 20 subjects. The proposed method is based on convolutional and recurrent neural networks using transfer learning (TL). A novel aspect was the use of a pretrained deep network model from basic joint hand movements to learn new patterns present in functional motions. A comparison was carried out with the traditional method based solely on sEMG. Although the performance of the algorithm slightly improved with the use of the multimodal combination, both strategies had similar behavior. The results indicated a significant improvement for a single task: opening a bottle with a tripod grasp.

Entropy and Clustering Information Applied to sEMG Classification.

The EMG signal is very difficult to classify due to the stochastic complexity of its characteristics. A way to reduce the complexity of a signal is to use clusters to resize them to a smaller space and then perform the classification. A classification improvement was verified by clustering the electromyographic signal and comparing it with the possible movements that can be performed. In this study, the Agglomerative Hierarchical Clustering was used. The basic idea is to give prior information to the final classifier so the posterior classification has fewer classes, diminishing his complexity. Through the methodology applied in this article, an accuracy of more than 90% was achieved by using a time window of only 10 ms in a signal sampled at 2000 Hz. Experimentation confirms that the methods presented in this paper are competitive with other methods presented in the literature.

Shoulder Flexion Pre-Movement Recognition Through Subject-Specific Brain Regions to Command an Upper Limb Exoskeleton.

This work presents two brain-computer interfaces (BCIs) for shoulder pre-movement recognition using: 1) manual strategy for Electroencephalography (EEG) channels selection, and 2) subject-specific channels selection by applying non-negative factorization matrix (NMF). Besides, the proposed BCIs compute spatial features extracted from filtered EEG signals through Riemannian covariance matrices and a linear discriminant analysis (LDA) to discriminate both shoulder pre-movement and rest states. We studied on twenty-one healthy subjects different frequency ranges looking the best frequency band for shoulder pre-movement recognition. As a result, our BCI located automatically EEG channels on the contralateral moved limb, and enhancing the pre-movement recognition (ACC = 71.39 ± 12.68%, κ = 0.43 ± 0.25%). The ability of the proposed BCIs to select specific EEG locations more cortically related to the moved limb could benefit the neuro-rehabilitation process.

Effects of Biofeedback in Preventing Urinary Incontinence and Erectile Dysfunction after Radical Prostatectomy.

In this study, we present a biofeedback method for the strengthening of perineal muscles during the preoperative procedures for radical prostatectomy, and we evaluate this technique as a prevention measure against complications such as urinary incontinence (UI) and erectile dysfunction (ED), which affect prostatectomy patients after surgery. In the experimental protocol, the patients performed specific exercises with the help of a device that provided the patient with visual biofeedback, based on a plot of the anal pressure. For the experimental protocol, we selected 20 male patients, with an average age of 64.0 years, and submitted them to ten therapeutic sessions each. A control group consisting of 32 men with an average age of 66.3 years, who were treated with the same surgical procedure but not with the preoperative procedures, also took part in the experiment. To evaluate UI and ED after the surgery in both control and experimental groups, we used two validated questionnaires-to assess UI, we used the King's Health Questionnaire (KHQ) and, for ED, we used the International Index of Erectile Function (IIEF-5) Questionnaire. We compared the variables associated with UI and ED after the surgery for the control and experimental groups. The occurrence of UI after radical prostatectomy in the control group (100% of the patients) was higher than that for the experimental group (5% of the patients), with p < 0.0001. Likewise, the occurrence of erectile dysfunction after prostatectomy in the control group (48.6% of the patients) was higher than that for the experimental group (5% of the patients), with p < 0.0001. The number of nocturia events also decreased as a consequence of the intervention (p < 0.0001), as did the number of disposable underwear units for urinary incontinence (p < 0.0001). Furthermore, we compared, only for the experimental group, the anal pressure before the biofeedback intervention and after the surgery, and we verified that the anal pressure after surgery was significantly higher (p < 0.0001). The results strongly suggest that the preoperative biofeedback procedure was effective in decreasing urinary incontinence and erectile dysfunction after radical prostatectomy. As future work, we intend to extend this analysis for larger samples and considering a broader age range.

Effects of very short pauses on electromyographic variables measured during fatiguing isometric contractions.

15 healthy men (26.6 ± 4.6 years old, weight of 70.7 ± 8.6 kg, and height of 1.750 ± 0.072 m) performed three 30-seconds isometric contractions at 60% MVC, with two 10-seconds resting intervals between them. The goal was to study the effect of the resting intervals on the variables that are most commonly used to analyze surface electromyographic (S-EMG) signals (conduction velocity [CV], root mean square [RMS], average rectified value [ARV], mean power frequency [MNF], and median power frequency [MDF]). For the first 30-second contraction, the S-EMG variables behaved exactly like described in the literature. However, after the first and second pauses, the CV variable ceased to behave like in the literature. In the first contraction, the conduction velocity had a statistically significant decreasing trend, in the second contraction, it had a statistically non-significant positive trend, and, in the third contraction, a statistically significant positive trend. These results suggest that short pauses between isometric constant-force contractions lead to changes in the recruiting strategies of the muscles involved in the contraction. The causes of these changes are not yet clear, and further work is needed in order to understand this effect.

Heart rate, oxygen saturation, and skin conductance: a comparison study of acute pain in Brazilian newborns.

Heart rate variability (HRV), oxygen saturation variability (OSV) and skin conductance activity (SCA) are recognized physiological markers of acute pain. In order to verify which of them has the best correlation with psychophysical parameters of pain (intensity, reactivity, direction, regulation and slope), an observational prospective study was performed, including 41 healthy full term newborns. The measurements studied were the HRV, the OSV, and the following SCA variables: number of waves per second (NWps) and relative area under the curve of waves (AUC). The measurements were performed in periods labeled before, during, and after a heel prick. The variation measured for intensity between periods was significant for the NWps (p=0.001), AUC (p=0.03), HRV (p=0.001) and OSV (p=0.004). Also, the reactivity and direction were significant for all variables, except AUC. The regulation parameter was significant for the variables NWps (p<0.01), AUC (p<0.05), HRV (p<0.01) and OSV (p<0.01). The slope was statistically significant only for the OSV variable (p=0.000). We concluded that the responses of the SCA, HRV and OSV to painful events fit the psychophysical parameters of a physiological marker and serve as valuable measures for pain diagnostic working the use in accordance with the needs of the context.

Building smart sensor nodes according to IEEE 1451.3 standard.

A Body Sensor Network (BSN) application requires many software and hardware adaptations to support correctly data exchanges between different sensor node architectures. However, these customizations demand extra time, cost and components. This paper introduces a simple development process in order to customize off-the-shelf BSN sensor nodes according to Transducer Bus Interface Modules (TBIM) standard. IEEE 1451.3 offers technical solutions for interfacing multiple and physically separated transducer allowing self-identification, self-configuration, plug and play and hot swapping capabilities. These are important requirements relating to most BSN applications.

Influence of neuromuscular electrical stimulation on the conduction velocity measured using EMG signals provided by linear arrays of electrodes.

Neuromuscular electrical stimulation (NMES) can alter the functioning of muscles and even assist muscle rehabilitation. In this paper, we evaluate the effect of NMES on the conduction velocity (CV) of the brachial biceps' motor units. We used a linear array of electrodes to acquire electromyographic signals, as different subjects perform isometric voluntary contractions (IVCs), with and without prior NMES. Our results show that, after NMES, the CVs at the beginning of the IVCs tend to increase, with respect to the case without NMES. Also, we observed that, while in the absence of NMES, the CVs tend to decrease over time with continued IVCs, this does not happen after 20 minutes of NMES, and the CVs can, in some cases, increase with the contractions.

Analysis of biomaterial latex-derived flow mechanical controller.

This paper describes the basic guidelines for developing an innovative biomedical device. It covers the issues of researching about a suitable material, developing a new device, and testing its proprieties to check its effectiveness. The goal of the device is to control food flow into the esophagus, reducing its volume and the speed of food intake to help in the treatment of obesity. This module, called Esophageal Flow Controller (EFC®), is made of latex. Three different models of prototypes were developed, and 10 units of each model had their constructive and mechanical characteristics evaluated. All of them have followed the same manufacturing cycle. The results showed that the Esophageal Flow Control module has all the essential characteristics of an effective device for flow control in the esophagus.

Insole with pressure control and tissue neoformation induction systems for diabetic foot.

This article presents the development of a prototype insole derived from natural rubber from Hevea brasiliensis, equipped with pressure control and capable of neoformation of tissue for people who have diabetic foot. The active element of this insole is the electronic circuit that monitors the plantar pressure. In addition, on the present stage of the research, a signal irradiating cell is used based on the principle of tissue regeneration using laser. This project proposes a "smart" insole prototype with a pressure monitoring system and an electronic system for tissue regeneration, which will open a new approach in an attempt to solve the problem of diabetic foot.

Semi-automatic algorithm for construction of the left ventricular area variation curve over a complete cardiac cycle.

BACKGROUND: Two-dimensional echocardiography (2D-echo) allows the evaluation of cardiac structures and their movements. A wide range of clinical diagnoses are based on the performance of the left ventricle. The evaluation of myocardial function is typically performed by manual segmentation of the ventricular cavity in a series of dynamic images. This process is laborious and operator dependent. The automatic segmentation of the left ventricle in 4-chamber long-axis images during diastole is troublesome, because of the opening of the mitral valve. METHODS: This work presents a method for segmentation of the left ventricle in dynamic 2D-echo 4-chamber long-axis images over the complete cardiac cycle. The proposed algorithm is based on classic image processing techniques, including time-averaging and wavelet-based denoising, edge enhancement filtering, morphological operations, homotopy modification, and watershed segmentation. The proposed method is semi-automatic, requiring a single user intervention for identification of the position of the mitral valve in the first temporal frame of the video sequence. Image segmentation is performed on a set of dynamic 2D-echo images collected from an examination covering two consecutive cardiac cycles. RESULTS: The proposed method is demonstrated and evaluated on twelve healthy volunteers. The results are quantitatively evaluated using four different metrics, in a comparison with contours manually segmented by a specialist, and with four alternative methods from the literature. The method's intra- and inter-operator variabilities are also evaluated. CONCLUSIONS: The proposed method allows the automatic construction of the area variation curve of the left ventricle corresponding to a complete cardiac cycle. This may potentially be used for the identification of several clinical parameters, including the area variation fraction. This parameter could potentially be used for evaluating the global systolic function of the left ventricle.

On the use of motion-based frame rejection in temporal averaging denoising for segmentation of echocardiographic image sequences.

We have recently introduced an algorithm for semi-automatic segmentation of the left ventricular wall in short-axis echocardiographic images (EMBC 30:218-221). In its preprocessing stage, the algorithm uses temporal averaging for image denoising. Motion estimation is used to detect and reject frames that do not correlate well with the set of images being averaged. However, the process of estimating motion vectors is computationally intense, which increases the algorithm's computation time. In this work, we evaluate the viability of replacing the motion estimation stage with less computationally intense approaches. Two alternative techniques are evaluated. The ventricular contours obtained from each of the three algorithm variants were quantitatively and qualitatively compared with contours manually-segmented by a specialist. We show that it is possible to reduce the algorithm's computational load without significantly reducing the segmentation quality. The proposed algorithms are also compared with three other techniques from the literature.

Two-dimensional compression of surface electromyographic signals using column-correlation sorting and image encoders.

We present a new preprocessing technique for two-dimensional compression of surface electromyographic (S-EMG) signals, based on correlation sorting. We show that the JPEG2000 coding system (originally designed for compression of still images) and the H.264/AVC encoder (video compression algorithm operating in intraframe mode) can be used for compression of S-EMG signals. We compare the performance of these two off-the-shelf image compression algorithms for S-EMG compression, with and without the proposed preprocessing step. Compression of both isotonic and isometric contraction S-EMG signals is evaluated. The proposed methods were compared with other S-EMG compression algorithms from the literature.

Estimation of the knee joint angle from surface electromyographic signals for active control of leg prostheses.

The surface electromyographic (SEMG) signal is very convenient for prosthesis control because it is non-invasively acquired and intrinsically related to the user's intention. This work presents a feature extraction and pattern classification algorithm for estimation of the intended knee joint angle from SEMG signals acquired using two sets of electrodes placed on the upper leg. The proposed algorithm uses a combination of time-domain and frequency-domain approaches for feature extraction (signal amplitude histogram and auto-regressive coefficients, respectively), a self-organizing map for feature projection and a Levenberg-Marquardt multi-layer perceptron neural network for pattern classification. The new algorithm was quantitatively compared with the method proposed by Wang et al (2006 Med. Biol. Eng. Comput. 44 865-72), which uses wavelet packet feature extraction, principal component analysis and a multi-layer perceptron neural classifier. The proposed method provided lower error-to-signal percentage and peak error amplitudes, higher correlation and fewer error events. The algorithm presented in this work may be useful as part of a myoelectric controller for active leg prostheses designed for transfemoral amputees.

Surface EMG and spatial resolution analysis with estimation of electromyographic descriptors.

Spatial filtering has become a common way to improve the resolution of surface electromyographic signals (SEMG) when used in connection with electrode arrays. The goal of this study is to observe the behavior of S-EMG amplitude and spectral descriptors when signals are submitted to a longitudinal quadruple differentiating spatial filter. Signals were acquired at 20% and 60% of the maximum voluntary contraction using a linear array of eight surface electrodes in order to understand the impact of the filtering technique in the S-EMG variables during fatiguing and non-fatiguing contractions. The final results show that the filtering procedure yields better selectivity, suggesting that single motor units can be better observed if spatial filters and measurement configurations with smaller pick-up areas are used. During fatiguing contractions, however, further analysis is needed.

Semi-automatic detection of the left ventricular border.

Two semi-automatic methods for the detection of the left ventricular border in two-dimensional short axis echocardiographic images are presented and compared. In these methods, the left ventricular area variation curve is calculated during a complete cardiac cycle after the segmentation of several frames. This allows the evaluation of the cardiovascular dynamics and the identification of important clinical parameters. The algorithms are proposed as several independent modules. The results are validated through the comparison between the semi-automatic continuous boundaries and manuals boundaries sketched by a medical specialist.

Left ventricle segmentation in echocardiography using a radial-search-based image processing algorithm.

A new left ventricle segmentation method in two-dimensional echocardiography images is proposed. Image processing techniques combined with radial search and temporal information are used to extract the left ventricle boundary. Borders from sequential images are extracted using the proposed method, and a curve illustrating the area variation within a cardiac cycle is presented. Performance evaluation is performed by comparing the borders obtained from the presented method to those manually prescribed by a medical specialist. The new sequential radial search algorithm improved the border extraction from long-axis ultrasound images, specially the ones where the mitral valve was open. Segmentation errors due to low contrast were corrected.

Energy efficient simulator for patient monitoring in Body Sensor Networks.

The medical tracking of patients carried out from a distance by means of wireless technology, incorporating various sensors simultaneously connected to the body of an individual, can promote health in suitable time. This article proposes a simulator for the monitoring of human health. This prototype simulates the workings of a BSN (Body Sensor Network) functioning as a sensor (electrophysiological signal) that is to be monitored, which utilizes a strategy based on the concept of data fusion. This tool incorporates a policy of economizing energy which has as its main objective increasing the operating time of the system.

A new model for programming software in body sensor networks.

A Body Sensor Network (BSN) must be designed to work autonomously. On the other hand, BSNs need mechanisms that allow changes in their behavior in order to become a clinically useful tool. The purpose of this paper is to present a new programming model that will be useful for programming BSN sensor nodes. This model is based on an intelligent intermediate-level compiler. The main purpose of the proposed compiler is to increase the efficiency in system use, and to increase the lifetime of the application, considering its requirements, hardware possibilities and specialist knowledge. With this model, it is possible to maintain the autonomous operation capability of the BSN and still offer tools that allow users with little grasp on programming techniques to program these systems.