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Changes in gait are associated with an increased risk of falling and may indicate the presence of movement disorders related to neurological diseases or age-related weakness. Continuous monitoring based on inertial measurement unit (IMU) sensor data can effectively estimate gait parameters that reflect changes in gait dynamics. Monitoring using a waist-level IMU sensor is particularly useful for assessing such data, as it can be conveniently worn as a sensor-integrated belt or observed through a smartphone application. Our work investigates the efficacy of estimating gait events and gait parameters based on data collected from a waist-worn IMU sensor. The results are compared to measurements obtained using a GAITRite® system as reference. We evaluate two machine learning (ML)-based methods. Both ML methods are structured as sequence to sequence (Seq2Seq). The efficacy of both approaches in accurately determining gait events and parameters is assessed using a dataset comprising 17,643 recorded steps from 69 subjects, who performed a total of 3588 walks, each covering approximately 4 m. Results indicate that the Convolutional Neural Network (CNN)-based algorithm outperforms the long short-term memory (LSTM) method, achieving a detection accuracy of 98.94% for heel strikes (HS) and 98.65% for toe-offs (TO), with a mean error (ME) of 0.09 ± 4.69 cm in estimating step lengths.

In this work, the optimum homogeneous phantom size for an equivalent whole-body electromagnetic (EM) modeling is calculated. This will enable the simple characterization of plane wave EM attenuation and far-field link budgets in Active Medical Implant (AMI) applications in the core region of the body for Industrial, Scientific, Medical and MedRadio frequency bands. A computational analysis is done to determine the optimum size in which a minimum phantom size reliably represents a whole-body situation for the corresponding frequency of operation, saving computer and laboratory resources. After the definition of a converge criterion, the computed minimum phantom size for subcutaneous applications, 0–10 mm insertion depth, is 355 × 160 × 255 mm3 for 402 MHz and 868 MHz and a cube with a side of 100 mm and 50 mm for 2.45 GHz and 5.8 GHz, respectively. For deep AMI applications, 10–50 mm insertion depth, the dimensions are 355 × 260 × 255 mm3 for 402 MHz and 868 MHz, and a cube with a side of 200 mm and 150 mm for 2.45 GHz and 5.8 GHz, respectively. A significant reduction in both computational and manufacturing resources for phantom development is thereby achieved. The verification of the model is performed by field measurements in phantoms made by aqueous solutions with sugar.

Long-term electrocardiogram (ECG) monitoring, as a representative application of cyber-physical systems, facilitates the early detection of arrhythmia. A considerable number of previous studies has explored monitoring techniques and the automated analysis of sensing data. However, ensuring patient privacy or confidentiality has not been a primary concern in ECG monitoring. First, we propose an intelligent heart monitoring system, which involves a patient-worn ECG sensor (e.g., a smartphone) and a remote monitoring station, as well as a decision support server that interconnects these components. The decision support server analyzes the heart activity, using the Pan–Tompkins algorithm to detect heartbeats and a decision tree to classify them. Our system protects sensing data and user privacy, which is an essential attribute of dependability, by adopting signal scrambling and anonymous identity schemes. We also employ a public key cryptosystem to enable secure communication between the entities. Simulations using data from the MIT-BIH arrhythmia database demonstrate that our system achieves a 95.74% success rate in heartbeat detection and almost a 96.63% accuracy in heartbeat classification, while successfully preserving privacy and securing communications among the involved entities.

Background: Diabetes, a metabolic disorder, has reached epidemic proportions in developed countries. The disease has two main forms: type 1 and type 2. Disease management entails administration of insulin in combination with careful blood glucose monitoring (type 1) or involves the adjustment of diet and exercise level, the use of oral anti-diabetic drugs, and insulin administration to control blood sugar (type 2). Objective: State-of-the-art technologies have the potential to assist healthcare professionals, patients, and informal carers to better manage diabetes insulin therapy, help patients understand their disease, support self-management, and provide a safe environment by monitoring adverse and potentially life-threatening situations with appropriate crisis management. Methods: New care models incorporating advanced information and communication technologies have the potential to provide service platforms able to improve health care, personalization, inclusion, and empowerment of the patient, and to support diverse user preferences and needs in different countries. The REACTION project proposes to create a service-oriented architectural platform based on numerous individual services and implementing novel care models that can be deployed in different settings to perform patient monitoring, distributed decision support, health care workflow management, and clinical feedback provision. Results: This paper presents the work performed in the context of the REACTION project focusing on the development of a health care service platform able to support diabetes management in different healthcare regimes, through clinical applications, such as monitoring of vital signs, feedback provision to the point of care, integrative risk assessment, and event and alarm handling. While moving towards the full implementation of the platform, three major areas of research and development have been identified and consequently approached: the first one is related to the glucose sensor technology and wearability, the second is related to the platform architecture, and the third to the implementation of the end-user services. The Glucose Management System, already developed within the REACTION project, is able to monitor a range of parameters from various sources including glucose levels, nutritional intakes, administered drugs, and patient's insulin sensitivity, offering decision support for insulin dosing to professional caregivers on a mobile tablet platform that fulfills the need of the users and supports medical workflow procedures in compliance with the Medical Device Directive requirements. Conclusions: Good control of diabetes, as well as increased emphasis on control of lifestyle factors, may reduce the risk profile of most complications and contribute to health improvement. The REACTION project aims to respond to these challenges by providing integrated, professional, management, and therapy services to diabetic patients in different health care regimes across Europe in an interoperable communication platform. Adapted from the source document.

The study was designed to assess the validity of a finite element analysis for predicting the behavior of cemented knee implant used in total knee arthroplasty (TKA), for different mechanical loads, and correlation with clinical outcomes of this procedure. We conducted computational simulations using finite element analysis of two situations: i) The ideal prosthetic component positioning; and ii) variable varus tibial malposition, but with a balanced knee. A total of 80 cemented TKAs performed on 70 patients were divided into two groups. Patients from one group required secondary asymmetric tibial recut for balancing the prosthetic knee and patients from the other group, did not. In regards to the results, we observed no differences upon analysis of the postoperative results of the Knee Society Score (KSS), the angle between the femur and tibia, the range of motion and frontal laxity between groups. The finite element analysis showed that in a 3˚ varus inclination of the joint interline, but with a balanced knee, the maximum contact stress, measured on the tibial plateau surface, increased by 11% compared to the value of mechanical alignment. In conclusion, analysis of the computational model using finite elements showed predictable results of cemented TKA for the different situations of mechanical loads.

Purpose Training in Interventional Radiology currently uses the apprenticeship model, where clinical and technical skills of invasive procedures are learnt during practice in patients. This apprenticeship training method is increasingly limited by regulatory restrictions on working hours, concerns over patient risk through trainees’ inexperience and the variable exposure to case mix and emergencies during training. To address this, we have developed a computer-based simulation of visceral needle puncture procedures.Methods A real-time framework has been built that includes: segmentation, physically based modelling, haptics rendering, pseudo-ultrasound generation and the concept of a physical mannequin. It is the result of a close collaboration between different universities, involving computer scientists, clinicians, clinical engineers and occupational psychologists.Results The technical implementation of the framework is a robust and real-time simulation environment combining a physical platform and an immersive computerized virtual environment. The face, content and construct validation have been previously assessed, showing the reliability and effectiveness of this framework, as well as its potential for teaching visceral needle puncture.Conclusion A simulator for ultrasound-guided liver biopsy has been developed. It includes functionalities and metrics extracted from cognitive task analysis. This framework can be useful during training, particularly given the known difficulties in gaining significant practice of core skills in patients.

Lesion prediction, a very important aspect of cancer disease prediction, is an important marker for patients before they become cancerous. Currently, traditional machine learning methods are gradually applied in disease prediction based on patient vital signs data. Accurate prediction requires a large amount and high quality of data, however, the difficulty in obtaining and incompleteness of electronic medical record (EMR) data leads to certain difficulties in disease prediction by traditional machine learning methods. Secondly, there are many factors that contribute to the development of cervical lesions, some risk factors are directly related to it while others are indirectly related to them. In addition, risk factors have an interactive effect on the development of cervical lesions; it does not occur in isolation, a large-scale knowledge graph is constructed base on the close relationships among risk factors in the literature, and new potential key risk factors are mined based on common risk factors through a subgraph mining method. Then lesion prediction algorithm is proposed to predict the likelihood of lesions in patients base on the set of key risk factors. Experimental results show that the circumvents the problems of large number of missing values in EMR data and discovered key risk factors that are easily ignored but have better prediction effect. Therefore, The method had better accuracy in predicting cervical lesions.

Measurement of the information flow along the ascending auditory pathway from the periphery to the auditory cortex (AC) has been given much attention in neurocomputation. While the neurophysiological mechanisms of the auditory pathway have been well studied, the temporal resolution and relationships among the auditory centers are still under investigation, especially when suffering from acoustic trauma that results in peripheral deficits and neural signal changes in the auditory system. In this study, we measured rat AC neurons and auditory nerve (AN) signals in digital format by using two new algorithms to calculate the neural response time of the AC neurons to electrical stimulation of the AN and quantify the neural information flow in the temporal domain. One algorithm compared time difference of neural spikes directly, which was based on the conventional idea of spike train in neurocomputation. The other employed a modified cross-correlation algorithm. Both algorithms shared the same pre-signal processing of spike selection. The statistical results by the two methods were compared and various parameters in the algorithms and their impact on the accuracy of the results were discussed. To test the effectiveness of the proposed method, the time difference between the AC to AN activities was calculated by both algorithms with raw neural signals collected. The neural signals from the animals were measured before and after noise trauma, and one of the animals received intra-modiolus electrical stimulation (IMES) to stimulate the AN. The results from using the two algorithms were generally consistent, and the biological mechanisms behind the time delay results between AC and AN activities were discussed.