A novel approach to sensor implementation for healthcare systems using internet of things

The Internet of Things is touching all spheres of life, be it in connecting cities together, making agricultural farms and health care smarter, predictable and more secure, and in industries it is set out to bring about changes that are similar to those of the industrial revolution that took place in the 19th and 20th century. It is estimated by pundits that in next 5 to 10 years, the Internet of Things will become a 50 billion dollar industry by itself, encompassing everything that it touches and goes upon. In order to get healthcare enabled into the IoT ecosystem, the sensors and the actuators related to it must be able to support the protocols that is required for the acquisition, processing and storing of data from the sensors to the IoT based infrastructure. Here, for a proposed model for a health care monitor using Internet of Things, the sensors characteristics, working principal, the protocol associated with it, its internal mechanism, and the results obtained when interfaced using a Raspberry Pi arediscussed, laying the framework for the future of the sensors that need to be adapted to stay relevant in the future, when IoT transitions from concept to reality

Trust Enhanced Role Based Access Control Using Genetic Algorithm

Improvements in technological innovations have become a boon for business organizations, firms, institutions, etc. System applications are being developed for organizations whether small-scale or large-scale. Taking into consideration the hierarchical nature of large organizations, security is an important factor which needs to be taken into account. For any healthcare organization, maintaining the confidentiality and integrity of the patients’ records is of utmost importance while ensuring that they are only available to the authorized personnel. The paper discusses the technique of Role-Based Access Control (RBAC) and its different aspects. The paper also suggests a trust enhanced model of RBAC implemented with selection and mutation only ‘Genetic Algorithm’. A practical scenario involving healthcare organization has also been considered. A model has been developed to consider the policies of different health departments and how it affects the permissions of a particular role. The purpose of the algorithm is to allocate tasks for every employee in an automated manner and ensures that they are not over-burdened with the work assigned. In addition, the trust records of the employees ensure that malicious users do not gain access to confidential patient data

Security framework for cloud based electronic health record (EHR) system

Health records are an integral aspect of any Hospital Management System. With newer innovations in technology, there has been a shift in the way of recording health information. Medical records which used to be managed using various paper charts have now become easier to organize and maintain, thereby increasing the efficiency of medical staff. The Electronic Health Records (EHR) System is becoming a high-tech medical management technology developed for the economic or emerging economic countries like India. In a national health system, the EHR integrates the Electronic Medical Records (EMR) in all collaborating hospitals through different networks. EHR gives healthcare professionals a way to share and manage patient data quickly and effectively. Due to the mass storage of confidential patient data, healthcare organizations are considered as one of the most targeted sectors by intruders. This paper proposes a security framework for EHR system, which takes into consideration the integrity, availability, and confidentiality of health records. The threats posed to the EHR system are modeled by STRIDE modeling tool, and the amount of risk is calculated using DREAD. The paper also suggests the security mechanism and countermeasures based on security standards, which can be utilized in an EHR environment. The paper shows that the utilization of the proposed methods effectively addresses security concerns such as breach of sensitive medical information

Modeling EEG Signals for Mental Confusion Using DNN and LSTM With Custom Attention Layer

This study explored the impact of confusion on concentration and cognition, emphasizing the importance of detecting and preventing confusion from enhancing learning outcomes. By leveraging electroencephalogram (EEG) data, we proposed a novel deep learning model that uses long short-term memory (LSTM) networks to predict confusion levels in online massive open courses (MOOCs). LSTM’s ability to model sequential data such as EEG signals has been harnessed to capture long-term dependencies and temporal dynamics effectively. To enhance pattern detection, we incorporated probabilistic features from machine learning (ML) models. By training them on the same dataset, we utilized their predictions as additional features for the deep learning model. Thereby, the neural network could make more informed decisions and improve its ability to detect and analyze EEG data patterns. Using LSTM and probabilistic features, our model effectively captured temporal dependencies, enabling an accurate online assessment of student perplexity to identify moments of confusion. The integration of attention mechanisms further enhanced the focus on critical EEG features, providing valuable insights into students’ cognitive states during online learning. We evaluated our approach by comparing the deep-learning model trained on the original dataset with that trained on feature-engineered data using K-fold cross-validation. Preliminary testing showed that the proposed DNN + LSTM model, which incorporates probabilistic features and a custom attention layer, achieves high accuracy in identifying moments of confusion among MOOC students. This study advances the EEG data analysis, leading to a better understanding of confusion patterns and supports personalized interventions for online education platforms