Insole-Based Systems for Health Monitoring: Current Solutions and Research Challenges

Wearable health monitoring devices allow for measuring physiological parameters without restricting individuals’ daily activities, providing information that is reflective of an individual’s health and well-being. However, these systems need to be accurate, power-efficient, unobtrusive and simple to use to enable a reliable, convenient, automatic and ubiquitous means of long-term health monitoring. One such system can be embedded in an insole to obtain physiological data from the plantar aspect of the foot that can be analyzed to gain insight into an individual’s health. This manuscript provides a comprehensive review of insole-based sensor systems that measure a variety of parameters useful for overall health monitoring, with a focus on insole-based PPD measurement systems developed in recent years. Existing solutions are reviewed, and several open issues are presented and discussed. The concept of a fully integrated insole-based health monitoring system and considerations for future work are described. By developing a system that is capable of measuring parameters such as PPD, gait characteristics, foot temperature and heart rate, a holistic understanding of an individual’s health and well-being can be obtained without interrupting day-to-day activities. The proposed device can have a multitude of applications, such as for pathology detection, tracking medical conditions and analyzing gait characteristics.

Design Innovation and Entrepreneurship Organization Based on Psychological Cognitiveness of the Space Narrative

The high-quality workspace can be used as a physical carrier for design innovation and entrepreneurial organizational culture to continuously change the psychological cognition and behavior of employees in community of practice. The spatial narrative of the culture of design innovation and entrepreneurial organizations means to integrate entrepreneurship and organizational culture into the space through visual presentation. Whether the spatial narrative is successful or not needs to be judged by whether the change of people’s psychological cognition achieves the expected effect. The traditional qualitative research methods such as interviews and questionnaires cannot fully and accurately present the psychological cognitive mechanism of design Innovation and entrepreneurship organization members. We use virtual reality technology combined with electrophysiological technology to conduct experiments. We use these technologies to conduct quantitative experiments on psychological cognition in community of practice. This study will select a design innovation and entrepreneurial organization, randomly select 20 participants, and divide them into 2 groups for experimentation. The VR scene is based on their real office space as a prototype. Put the visual elements of corporate culture in one of the VR scenes. The other VR scene as a reference does not incorporate visual elements of organizational culture. Participants participated in the experiment in these two VR scenarios. There are many advanced devices that can accurately test individual psychological changes, but the ErgoLab man-machine environment test platform, can collect and compare these data [physiological data, electroencephalogram (EEG) data, and behavior data] in real-time and comprehensively, which is its advantage. According to the experimental results, judge the changes in the psychological cognitive data of the participants before and after the placement of the spatial narrative in design innovation and entrepreneurial organizations. The experiment combined interviews and questionnaires to ensure the authenticity of the quantitative data. The conclusion of the experiment will produce an accurate quantitative study on the psychological cognition of the spatial narrative of design innovation and entrepreneurial organizational culture. A sense of organizational belonging, collective sense, pride, mission, and work fun can be generated in the workspace.

Supervised Classification of Operator Functional State Based on Physiological Data: Application to Drones Swarm Piloting

To improve the safety and the performance of operators involved in risky and demanding missions (like drone operators), human-machine cooperation should be dynamically adapted, in terms of dialogue or function allocation. To support this reconfigurable cooperation, a crucial point is to assess online the operator’s ability to keep performing the mission. The article explores the concept of Operator Functional State (OFS), then it proposes to operationalize this concept (combining context and physiological indicators) on the specific activity of drone swarm monitoring, carried out by 22 participants on simulator SUSIE. With the aid of supervised learning methods (Support Vector Machine, k-Nearest Neighbors, and Random Forest), physiological and contextual are classified into three classes, corresponding to different levels of OFS. This classification would help for adapting the countermeasures to the situation faced by operators.

Two Possible Strategies for Drug Modification of Gemcitabine and Future Contributions to Personalized Medicine

Drug repurposing is an emerging strategy, which uses already approved drugs for new medical indications. One such drug is gemcitabine, an anticancer drug that only works at high doses since a portion is deactivated in the serum, which causes toxicity. In this review, two methods were discussed that could improve the anticancer effect of gemcitabine. The first is a chemical modification by conjugation with cell-penetrating peptides, namely penetratin, pVEC, and different kinds of CPP6, which mostly all showed an increased anticancer effect. The other method is combining gemcitabine with repurposed drugs, namely itraconazole, which also showed great cancer cell inhibition growth. Besides these two strategies, physiologically based pharmacokinetic models (PBPK models) are also the key for predicting drug distribution based on physiological data, which is very important for personalized medicine, so that the correct drug and dosage regimen can be administered according to each patient’s physiology. Taking all of this into consideration, it is believed that gemcitabine can be repurposed to have better anticancer effects.

Virtual Reality Exposure Therapy and Physiological Data Analysis for Treatment of Stress Disorders

Anxiety disorders are diagnosed when people become overreactive, disassociated, and feel emotionally unable to control feelings to the extent that their daily lifes are affected. Driving phobia is one of the widespread anxiety disorders in modern society, which cause problematic disruptions of a patient's daily activities. Exposure therapy is an approach gaining popularity for treating patients with stress disorders. Virtual reality (VR) technology allows people to interact with objects and stimuli in an immersive way. The VR for phobic therapy using indirect exposure, which can be safely discontinued or lowed in terms of intensity, is the area of research with literature published and patents granted. This research focuses on reviewing virtual reality exposure therapy (VRET) literature and patents. The chapter also presents the research and development of a novel driving phobia VRET system with the detailed experiments to demonstrate the design, development, implementation, enhancement, and verification of VRET.

Anxiety Detection Using Physiological Data and Wearable IoT Devices

Anxiety has been the primary cause of multiple illnesses in society. Gadgets, smartwatches, and wristbands have become an integral part of our daily lives and are widely used. This shows whether wearable sensors and technologies can be used to prevent anxiety and stress. The authors look at recent research on recognizing anxiety in everyday life in this chapter. There are few studies that examine the detection of stress in daily life, as there are few studies that examine a variety of tasks involving the recognition of anxiety in regulated laboratory settings. In this analysis, the authors isolate and examine tasks based on the physiological modality used and their intended areas, such as the workplace, education, automobiles, and the uncontrolled conditions of everyday life. In addition, they explore promising technologies, prevention, and research issues.

DATA SECURITY — THE NEXT IMPERATIVE IN HEALTHCARE SYSTEMS

Wireless Sensor Networks (WSNs) have significantly impacted healthcare applications by giving the possibility of monitoring the patient’s physiological parameters using different sensors. The use of WSN and the wireless Body Area Networks (WBANs) offers possible solutions for monitoring the health parameters in remote areas. On the other hand, the use of wireless communication medium and information security is the primary concern in WBANs. Because WBANs use the different small sensors placed on the human body to collect the physiological data. They need resource and computational restrictions, thus, building the use of complex and advanced encryption algorithms infeasible. It is essential in the WBAN to monitor and transmit the data to provide reliable and secure communication. Wrong and incomplete information can create difficulties in patient health which can be sometimes more dangerous. This gives the motivation to make such security protocols or algorithms to achieve high security in WBANs. So, the research has been currently focused on reliable communication between the doctor and patient, routing algorithms, and the data’s security by using various new technologies. This paper discusses the different security threats and solutions for designing healthcare applications and routing and layer attacks. Furthermore, the paper has been focused on the Data Distribution Service Models for data security. The paper also includes artificial intelligence and machine learning algorithms in healthcare implemented by various companies.

PMHE:A Wearable Medical Sensor Assisted Framework For Health Care Based On Blockchain And Privacy Computing

Nowadays, smart medical cloud platforms have become a new direction in the industry. However, because the medical system involves personal physiological data, user privacy in data transmission and processing is also easy to leak in the smart medical cloud platform. This paper proposed a medical data privacy protection framework named PMHE based on blockchain and fully homomorphic encryption technology. The framework receives personal physiological data from wearable devices on the client side, and uses blockchain as data storage to ensure that the data cannot be tampered with or forged; Besides, it use fully homomorphic encryption method to design a disease prediction model, which was implemented using smart contracts. In PMHE, data is encoded and encrypted on the client side, and encrypted data is uploaded to the cloud platform via the public Internet, preventing privacy leakage caused by channel eavesdropping; Smart contracts run on the blockchain platform for disease prediction, and the operators participating in computing are encrypted user data too, so it avoids privacy and security issues caused by platform data leakage. The client-to-cloud interaction protocol is also designed to overcome the defect that fully homomorphic encryption only supports addition and multiplication by submitting tuples on the client side, to ensure that the prediction model can perform complex computing. In addition, the design of the smart contract is introduced in detail, and the performance of the system is analyzed. Finally, experiments are conducted to verify the operating effect of the system, ensuring that user privacy is not leaked without affecting the accuracy of the model, and realizing a smart medical cloud platform in which data can be used but cannot be borrowed.

A Multimodal Approach to Investigate the Neural Mechanisms of Real World Social Vision

Eye tracking and other behavioral measurements collected from patient-participants in their hospital rooms afford a unique opportunity to study immersive natural behavior for basic and clinical-translational research, and also requires addressing important logistical, technical, and ethical challenges. Hospital rooms provide the opportunity to richly capture both clinically relevant and ordinary natural behavior. As clinical settings, they add the potential to study the relationship between behavior and physiology by collecting physiological data synchronized to behavioral measures from participants. Combining eye-tracking, other behavioral measures, and physiological measurements enables clinical-translational research into understanding the participants' disorders and clinician-patient interactions, as well as basic research into natural, real world behavior as participants eat, read, converse with friends and family, etc. Here we describe a paradigm in individuals undergoing surgical treatment for epilepsy who spend 1-2 weeks in the hospital with electrodes implanted in their brain to determine the source of their seizures. This provides the unique opportunity to record behavior using eye tracking glasses customized to address clinically-related ergonomic concerns, synchronized direct neural recordings, use computer vision to assist with video annotation, and apply multivariate machine learning analyses to multimodal data encompassing hours of natural behavior. We discuss the acquisition, quality control, annotation, and analysis pipelines to study the neural basis of real world social and affective perception during natural conversations with friends and family in participants with epilepsy. We also discuss clinical, logistical, and ethical and privacy considerations that must be addressed to acquire high quality multimodal data in this setting.

Development of reference equations for the six-minute walk distance of school-aged Nigerian children

BACKGROUND: The six-minute walk test (6MWT), a simple, reliable and valid test that uses the distance walked in six minutes (six-minute walk distance, 6MWD) to quantify functional exercise capacity, is widely used in chronic cardiopulmonary and non-cardiopulmonary disorders. However, th absence of reference standards for Nigerian school-aged children limits its utility in this age group OBJECTIVES: To develop normative values and equations for the 6MWT of school-aged Nigerian children METHODS: In a cross-sectional study, healthy Nigerian children aged 6-11 years in Lagos completed 6MWT on 20-meter straight outdoor courses in their schools following standardized guidelines. Potential predictors: demographic (age, sex), anthropometric (height, weight, chest circumference, leg length) and physiological data [pre-walk, immediate post-walk and 5th-minute post-walk heart rate (HR), oxygen saturation (SpO2), systolic blood pressure (SBP), diastolic blood pressure (DBP) and rating of perceived exertion (RPE)] and the difference between pre-walk and post-walk HR (HR change), SpO2 (SpO2 change), SBP (SBP change), DBP (DBP change) and RPE (RPE change). Primary outcomes:six-minute walk distance (6MWD) in meters. RESULTS: Overall, 627 pupils (52.1% girls) walked 504.6 (66.6) m (95% CI: 499.4, 509.8), ranging from 326.6 to 673.0 m; boys walking 16 m longer (p=0.002). Stepwise linear regression yielded 6MWD=347.9+14(Age in years)+1.6(HR change)+17.6(sex; male=1, female=0)+1.2(SBP change);R square=0.25.Previously published reference equations mostly over-estimated Nigerian children's 6MWD. CONCLUSION: The reference values and equations, after validation in other Nigerian geographic populations, may be useful for the functional evaluation of Nigerian children aged 6-11 years with chronic childhood disorders.