Dynamic of Glucose Homeostasis in Virtual Patients: A Comparison between Different Behaviors.

This work presents a mathematical model of homeostasis dynamics in healthy individuals, focusing on the generation of conductive data on glucose homeostasis throughout the day under dietary and physical activity factors. Two case studies on glucose dynamics for populations under conditions of physical activity and sedentary lifestyle were developed. For this purpose, two types of virtual populations were generated, the first population was developed according to the data of a total of 89 physical persons between 20 and 75 years old and the second was developed using the Monte Carlo approach, obtaining a total of 200 virtual patients. In both populations, each participant was classified as an active or sedentary person depending on the physical activity performed. The results obtained demonstrate the capacity of virtual populations in the generation of in-silico approximations similar to those obtained from in-vivo studies. Obtaining information that is only achievable through specific in-vivo experiments. Being a tool that generates information for the approach of alternatives in the prevention of the development of type 2 Diabetes.

A Computational Evaluation of the temperature distribution generated by thermal splints designed to treat knee pain.

A 3D modeling study was performed to evaluate the temperature distribution produced due to knee thermal splints to facilitate future investigations into the possible relationship between the temperature distribution and the pain relief related performance of the splints. Water and phase change material (PCM) were used as splint fillers at two different temperatures (42 °C and 44 °C). Free cooling and a temperature-controlled thermal splint were also considered. The study showed that a PCM filled splint or a temperature-controlled splint produced relatively larger and deeper temperature changes compared to the one produced with a water filled splint. With a PCM filled splint it was possible to reach a temperature increase of 1 °C in muscle tissue (42.61 cm3) and not only in fatty tissue.

Intelligent Spectroscopy System Used for Physicochemical Variables Estimation in Sugar Cane Soils.

The current condition of soils is a major area of interest due to the lack of certainty in their physicochemical properties, which can guarantee the quality and the production of a specific crop. Additionally, methodologies to improve land management must be implemented in order to address the consequences of many environmental issues. To date, many techniques have been implemented to improve the accuracy-and more recently the speed-of analysis, in order to obtain results while in the field. Among those, Near Infrared (NIR) spectroscopy has been widely used to achieve the objectives mentioned above. Nevertheless, it requires particular knowledge, and the cost might be high for farmers who own the fields and crops. Thus, the present work uses a system that implements capacitance spectroscopy plus artificial intelligence algorithms to estimate the physicochemical variables of soil used to grow sugar cane. The device uses the frequency response of the soil to determine its magnitude and phase values, which are used by artificial intelligence algorithms that are capable of estimating the soil properties. The obtained results show errors below 8% in the estimation of the variables compared to the analysis results of the soil in laboratories. Additionally, it is a portable system, with low cost, that is easy to use and could be implemented to test other types of soils after evaluating the necessary algorithms or proposing alternatives to restore soil properties.

Use of the Stockwell Transform in the Detection of P300 Evoked Potentials with Low-Cost Brain Sensors.

The evoked potential is a neuronal activity that originates when a stimulus is presented. To achieve its detection, various techniques of brain signal processing can be used. One of the most studied evoked potentials is the P300 brain wave, which usually appears between 300 and 500 ms after the stimulus. Currently, the detection of P300 evoked potentials is of great importance due to its unique properties that allow the development of applications such as spellers, lie detectors, and diagnosis of psychiatric disorders. The present study was developed to demonstrate the usefulness of the Stockwell transform in the process of identifying P300 evoked potentials using a low-cost electroencephalography (EEG) device with only two brain sensors. The acquisition of signals was carried out using the Emotiv EPOC® device—a wireless EEG headset. In the feature extraction, the Stockwell transform was used to obtain time-frequency information. The algorithms of linear discriminant analysis and a support vector machine were used in the classification process. The experiments were carried out with 10 participants; men with an average age of 25.3 years in good health. In general, a good performance (75⁻92%) was obtained in identifying P300 evoked potentials.

An Interactive System for Fine Motor Rehabilitation.

PURPOSE: One of the most important aspects in neuromotor rehabilitation is the need of feedback for patients. The rehabilitation system's efficiency relies on the therapist's judgment; the therapist tells the patient whether he/she is performing the exercises correctly. This process may be quite subjective, because it depends on the therapist's personal opinion. On the other hand, recent studies have shown that vibrotactile biofeedback can improve the effectiveness of interaction as it is a very helpful tool in the physiological process of neuromotor rehabilitation. DESIGN: We designed an interactive system focused on rehabilitation of the upper limbs using active markers and image processing, which consists of drawing activities in both augment and virtual reality. METHODS: The system gives the user a correction through multimodal stimuli feedback (vibrotactile, visual, and sound stimulus) and force measurement to let the patients know if they are not achieving the tasks' goals. FINDINGS: The developed system could be used by nursing assistants to better help patients. The purpose of this system was assisting patients with injuries in shoulders, elbows, or wrists, providing an audio-vibrotactile feedback as a factor of correction in the movements of the patient. To examine our system, 11 participants were asked to participate in an experiment where they performed activities focused to strengthen their fine motor movements. CONCLUSIONS AND CLINICAL RELEVANCE: Results showed show that patients' fine motor skills improved 10% on average by comparing their error rates throughout the sessions.

Using health games for rehabilitation of patients with infantile cerebral palsy.

[Purpose] The purposes of this study were to evaluate whether the therapeutic games developed by the study team are significantly effective for upper limb rehabilitation of patients with cerebral palsy and to assess the development of the games and the evolution of patients throughout the therapy sessions. [Subjects and Methods] This study demonstrates the results of using therapeutic games in patients with infantile cerebral palsy. The therapies were performed in 30-minute sessions for about 1 to 4 months. This study shows the progress of five children with cerebral palsy during the sessions. The time it took the children on each road and the times required to complete a task were measured. In addition, the level of difficulty of the games was gradually increased at each session. [Results] Results have shown good progress on the accuracy of the movements and an increase in concentration level during the execution of the games, showing an improvement in the patients' performance by 40-55% faster. [Conclusions] Health games encourage children to comply with therapy. The advantage of the game is that the patient can perform the therapy at home, which could help achieve further progress in patients.

Development of an expert system as a diagnostic support of cervical cancer in atypical glandular cells, based on fuzzy logics and image interpretation.

Cervical cancer is the second largest cause of death among women worldwide. Nowadays, this disease is preventable and curable at low cost and low risk when an accurate diagnosis is done in due time, since it is the neoplasm with the highest prevention potential. This work describes the development of an expert system able to provide a diagnosis to cervical neoplasia (CN) precursor injuries through the integration of fuzzy logics and image interpretation techniques. The key contribution of this research focuses on atypical cases, specifically on atypical glandular cells (AGC). The expert system consists of 3 phases: (1) risk diagnosis which consists of the interpretation of a patient's clinical background and the risks for contracting CN according to specialists; (2) cytology images detection which consists of image interpretation (IM) and the Bethesda system for cytology interpretation, and (3) determination of cancer precursor injuries which consists of in retrieving the information from the prior phases and integrating the expert system by means of a fuzzy logics (FL) model. During the validation stage of the system, 21 already diagnosed cases were tested with a positive correlation in which 100% effectiveness was obtained. The main contribution of this work relies on the reduction of false positives and false negatives by providing a more accurate diagnosis for CN.

iPixel: a visual content-based and semantic search engine for retrieving digitized mammograms by using collective intelligence.

Nowadays, traditional search engines such as Google, Yahoo and Bing facilitate the retrieval of information in the format of images, but the results are not always useful for the users. This is mainly due to two problems: (1) the semantic keywords are not taken into consideration and (2) it is not always possible to establish a query using the image features. This issue has been covered in different domains in order to develop content-based image retrieval (CBIR) systems. The expert community has focussed their attention on the healthcare domain, where a lot of visual information for medical analysis is available. This paper provides a solution called iPixel Visual Search Engine, which involves semantics and content issues in order to search for digitized mammograms. iPixel offers the possibility of retrieving mammogram features using collective intelligence and implementing a CBIR algorithm. Our proposal compares not only features with similar semantic meaning, but also visual features. In this sense, the comparisons are made in different ways: by the number of regions per image, by maximum and minimum size of regions per image and by average intensity level of each region. iPixel Visual Search Engine supports the medical community in differential diagnoses related to the diseases of the breast. The iPixel Visual Search Engine has been validated by experts in the healthcare domain, such as radiologists, in addition to experts in digital image analysis.

ITOHealth: a multimodal middleware-oriented integrated architecture for discovering medical entities.

Emergency healthcare is one of the emerging application domains for information services, which requires highly multimodal information services. The time of consuming pre-hospital emergency process is critical. Therefore, the minimization of required time for providing primary care and consultation to patients is one of the crucial factors when trying to improve the healthcare delivery in emergency situations. In this sense, dynamic location of medical entities is a complex process that needs time and it can be critical when a person requires medical attention. This work presents a multimodal location-based system for locating and assigning medical entities called ITOHealth. ITOHealth provides a multimodal middleware-oriented integrated architecture using a service-oriented architecture in order to provide information of medical entities in mobile devices and web browsers with enriched interfaces providing multimodality support. ITOHealth's multimodality is based on the use of Microsoft Agent Characters, the integration of natural language voice to the characters, and multi-language and multi-characters support providing an advantage for users with visual impairments.

Simplified interval observer scheme: a new approach for fault diagnosis in instruments.

There are different schemes based on observers to detect and isolate faults in dynamic processes. In the case of fault diagnosis in instruments (FDI) there are different diagnosis schemes based on the number of observers: the Simplified Observer Scheme (SOS) only requires one observer, uses all the inputs and only one output, detecting faults in one detector; the Dedicated Observer Scheme (DOS), which again uses all the inputs and just one output, but this time there is a bank of observers capable of locating multiple faults in sensors, and the Generalized Observer Scheme (GOS) which involves a reduced bank of observers, where each observer uses all the inputs and m-1 outputs, and allows the localization of unique faults. This work proposes a new scheme named Simplified Interval Observer SIOS-FDI, which does not requires the measurement of any input and just with just one output allows the detection of unique faults in sensors and because it does not require any input, it simplifies in an important way the diagnosis of faults in processes in which it is difficult to measure all the inputs, as in the case of biologic reactors.