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Type 1 diabetes is a chronic and a life threatening disease: an adjusted treatment and a proper management of the disease are crucial to prevent or delay the complications of diabetes. Although during the last decade the development of the artificial pancreas has presented great advances in diabetes care, the multiple daily injections therapy still represents the most widely used treatment option for type 1 diabetes. This work presents the proposal and first development stages of an application focused on guiding patients using the continuous glucose monitors and smart pens together with insulin and carbohydrates recommendations. Our proposal aims to develop a platform to integrate a series of innovative machine learning models and tools rigorously tested together with the use of the latest IoT devices to manage type 1 diabetes. The resulting system actually closes the loop, like the artificial pancreas, but in an intermittent way.
There are several intra-operative use cases which require the surgeon to interact with medical devices. I used the Leap Motion Controller as input device for three use-cases: 2D-interaction (e.g. advancing EPR data), selection of a value (e.g. room illumination brightness) and an application point and click scenario. I evaluated the Palm Mouse as the most suitable gesture solution to coordinate the mouse and advise to use the implementation using all fingers to perform a click. This small case study introduces the implementations and methods that result those recommendations.
Today many scientific works are using deep learning algorithms and time series, which can detect physiological events of interest. In sleep medicine, this is particularly relevant in detecting sleep apnea, specifically in detecting obstructive sleep apnea events. Deep learning algorithms with different architectures are used to achieve decent results in accuracy, sensitivity, etc. Although there are models that can reliably determine apnea and hypopnea events, another essential aspect to consider is the explainability of these models, i.e., why a model makes a particular decision. Another critical factor is how these deep learning models determine how severe obstructive sleep apnea is in patients based on the apnea-hypopnea index (AHI). Deep learning models trained by two approaches for AHI determination are exposed in this work. Approaches vary depending on the data format the models are fed: full-time series and window-based time series.
The potentials and opportunities created by digitized healthcare can be further customized through smart data processing and analysis using accurate patient information. This development and the associated new treatment concepts basing on digital smart sensors can lead to an increase in motivation by applying gamification approaches. This effect can also be used in the field of medical treatment, e.g. with the help of a digital spirometer combined with an app. In one of our exemplary applications, we show how to control an airplane within an app by breathing respectively inhaling and exhaling. Using this biofeedback within a game allows us to increase the motivation and fun for children that need to perform necessary exercises.
Detecting semantic similarities between sentences is still a challenge today due to the ambiguity of natural languages. In this work, we propose a simple approach to identifying semantically similar questions by combining the strengths of word embeddings and Convolutional Neural Networks (CNNs). In addition, we demonstrate how the cosine similarity metric can be used to effectively compare feature vectors. Our network is trained on the Quora dataset, which contains over 400k question pairs. We experiment with different embedding approaches such as Word2Vec, Fasttext, and Doc2Vec and investigate the effects these approaches have on model performance. Our model achieves competitive results on the Quora dataset and complements the well-established evidence that CNNs can be utilized for paraphrase recognition tasks.
This paper investigates the possibility to effectively monitor and control the respiratory action using a very simple and non invasive technique based on a single lightweight reduced-size wireless surface electromyography (sEMG) sensor placed below the sternum. The captured sEMG signal, due to the critical sensor position, is characterized by a low energy level and it is affected by motion artifacts and cardiac noise. In this work we present a preliminary study performed on adults for assessing the correlation of the spirometry signal and the sEMG signal after the removal of the superimposed heart signal. This study and the related findings could be useful in respiratory monitoring of preterm infants.
Sleep is essential to existence, much like air, water, and food, as we spend nearly one-third of our time sleeping. Poor sleep quality or disturbed sleep causes daytime solemnity, which worsens daytime activities' mental and physical qualities and raises the risk of accidents. With advancements in sensor and communication technology, sleep monitoring is moving out of specialized clinics and into our everyday homes. It is possible to extract data from traditional overnight polysomnographic recordings using more basic tools and straightforward techniques. Ballistocardiogram is an unobtrusive, non-invasive, simple, and low-cost technique for measuring cardiorespiratory parameters. In this work, we present a sensor board interface to facilitate the communication between force sensitive resistor sensor and an embedded system to provide a high-performing prototype with an efficient signal-to-noise ratio. We have utilized a multi-physical-layer approach to locate each layer on top of another, yet supporting a low-cost, compact design with easy deployment under the bed frame.
Determination of accelerometer sensor position for respiration rate detection: initial research
(2022)
Continuous monitoring of a patient's vital signs is essential in many chronic illnesses. The respiratory rate (RR) is one of the vital signs indicating breathing diseases. This article proposes the initial investigation for determining the accelerometric sensor position of a non-invasive and unobtrusive respiratory rate monitoring system. This research aims to determine the sensor position in relation to the patient, which can provide the most accurate values of the mentioned physiological parameter. In order to achieve the result, the particular system setup, including a mechanical sensor holder construction was used. The breathing signals from 5 participants were analyzed corresponding to the relaxed state. The main criterion for selecting a suitable sensor position was each patient's average acceleration amplitude excursion, which corresponds to the respiratory signal. As a result, we provided one more defined important parameter for the considered system, which was not determined before.
The field of breath analysis has developed to be of growing interest in medical diagnosis and patient monitoring. The main advantages are that it’s noninvasive, painless and repeatable in flexible cycles. Even though breath analysis is being researched for a couple of decades there are still many unanswered questions. Human breath contains volatile organic compounds which are emitted from inside the body. Some of these compounds can be assigned to specific sources, such as inflammation or cancer, but also to non health related origins. This paper gives an overview of breath analysis for the purpose of disease diagnosis and health monitoring. Therefore, literature regarding breath analysis in the medical field has been analyzed, from its early stages to the present. As a result, this paper gives an outline of the topic of breath analysis.
Information and communication technologies support telemedicine to lower health access barriers and to provide better health care. While the potential in Active Assisted Living (AAL) is increasing, it is difficult to evaluate its benefits for the user, and it requires coordinated actions to launch it. The European Commission’s action plan 2012–2020 provides a roadmap to patient empowerment and healthcare, to link up devices and technologies, and to invest in research towards the personalized medicine of the future. As a quickly developing area in medicine, telemonitoring is a demanding field in research and development. Telemonitoring is an essential component of personalized medicine, where health providers can obtain precise information on outcare or chronic patients to improve diagnosis and therapy and also help healthy persons with prevention support. Telemonitoring combines mobile and wearable devices with the personal AAL home environment, a private or (partly) supervised home, most often called ’smart home’. The focus of this workshop is on new hardware and software solutions specifically designed to be applicable in AAL environments to empower patients. This workshop presents system-oriented solutions covering wearable and AAL-embedded devices, computer science infrastructure both at the users’ and the medical premises, to handle the data and decision support systems to support diagnose and treatment.