This chapter addresses the systematic governance of clinical engineering equipment through its complete life cycle perspective, providing an analytical framework for the responsible and efficient management of medical technology infrastructure. Governance is treated as a structured process in which the stages of planning, acquisition, operation, maintenance, and decommissioning are interconnected in a coherent institutional ecosystem. The chapter emphasizes that equipment is not just a technical resource but a critical element of clinical safety, and its management requires clear coordination, traceability, and evaluation tools. The first part introduces the fundamental principles of life cycle governance, including international standards, organizational responsibilities, and evidence-based decision-making mechanisms. Modern models of clinical needs assessment are analyzed, along with criteria for selecting technologies in accordance with institutional strategy and anticipated operational performance. The central section examines critical maintenance, calibration, verification, and monitoring processes, highlighting the role of performance data in optimizing resources and reducing risks. Risk management elements are also integrated, including vulnerability assessment, security audits, and compliance requirements. The final part discusses emerging issues such as the digitalization of maintenance, predictive analytics, cybersecurity, and sustainability directions in decommissioning. The chapter concludes that coherent life cycle governance is not just an administrative tool but an essential vector for service quality, financial efficiency, and patient protection in modern healthcare environments.

Postural balance is maintained through the coordinated activity of visual, vestibular, and proprioceptive systems under the control of the central nervous system. Deficits in this system, whether due to neurological or musculoskeletal disorders, often lead to instability, functional decline, and reduced quality of life. To address these challenges, this study introduces a sensor-based rehabilitation platform designed to assess balance and encourage recovery through interactive feedback. The system combines a pressure-sensitive platform equipped with four load cells and an Arduino microcontroller with a Unity-based software application. The hardware captures changes in plantar pressure, while the software translates these variations into adaptive, game-like exercises that promote both motor control and cognitive engagement. Three rehabilitation scenarios were implemented, each allowing progressive adjustment of difficulty according to the user’s functional ability. Testing confirmed that the platform effectively detected asymmetrical weight distributions and provided consistent real-time responses during training. The approach encourages user participation through visual and auditory feedback while maintaining precise monitoring of balance parameters. Given its open and modular design, the prototype can be easily adapted or expanded to accommodate supplementary sensors or wireless communication for remote monitoring. Overall, the proposed system represents a technically feasible and engaging framework for postural rehabilitation, contributing to the development of intelligent and personalized rehabilitation technologies that integrate motor learning with interactive feedback.

Artificial intelligence (AI) is reshaping medical rehabilitation by advancing from isolated assistive technologies toward data-driven program management. Beyond established applications in robotics and virtual reality, AI enables multimodal data integration, predictive analytics, adaptive therapy optimization, and real-time monitoring across rehabilitation domains. This review synthesizes 61 peer-reviewed studies to examine how AI supports the management, planning, and evaluation of rehabilitation programs. The evidence indicates strong technical maturity at the device and session levels, particularly in robotic control and wearable monitoring, whereas longitudinal program orchestration and system-level coordination remain at an emerging stage. Machine learning, reinforcement learning, computer vision, and time-series models facilitate patient phenotyping, therapy personalization, and prognostic modeling. However, their scalability is constrained by limited interoperability, heterogeneous outcome measures, and insufficient multicenter validation. A structured six-layer management architecture is proposed to conceptualize AI as an integrated orchestration framework. Advancing toward scalable and trustworthy rehabilitation ecosystems will require interoperable infrastructures, longitudinal validation, and embedded ethical and explainability mechanisms.

Teledermatology has the potential to improve access to dermatological care in resource-limited settings, particularly for infectious skin diseases and Neglected Tropical Diseases (NTDs), where diagnostic capacity is often limited. This study aims to evaluate the feasibility of an artificial intelligence (AI) - based Teledermatology system, to support the classification of infectious versus non-infectious skin lesions. A convolutional neural network based on the ResNet50 architecture was trained on a dataset of dermatological images and optimized for deployment on a low-cost embedded device (Raspberry Pi 3). The system was designed to operate locally and includes a user interface for image acquisition, local storage, and cloud synchronization via Google Drive to enable remote consultation. The proposed model achieved an overall classification accuracy of 96% and a sensitivity of 89% for infectious lesions on the evaluated dataset, indicating its potential usefulness as a triage and decision-support tool for frontline healthcare workers. These findings suggest that AI-enabled Teledermatology systems deployed on affordable hardware may offer a scalable and cost-effective approach to supporting earlier identification of infectious cutaneous conditions, including NTDs, in underserved regions.

Background/Objectives: The aim was to identify patterns of autonomic and neuroendocrine reactivity to an immersive virtual reality (VR) social-emotional stressor and explore their associations with perceived stress and eating behavior. Methods: This one-group pretest–posttest study included 30 children and adolescents with obesity (15 boys and 15 girls), aged 8 to 17 years. The VR protocol consisted of two consecutive phases: a 5 min relaxation phase using the Forest application and a 5 min stimulation phase using a cognitively engaging VR game designed to elicit social-emotional stress. Physiological responses were measured using heart rate variability (HRV) indices and salivary stress biomarkers, including cortisol and alpha amylase. Subjective stress and eating responses were assessed via visual analogue scales (VAS) administered immediately post-exposure. The Three-Factor Eating Questionnaire (TFEQ-R21C) was used to evaluate cognitive restraint (CR), uncontrolled eating (UE), and emotional eating (EE). Results: The cortisol reactivity was blunted and may reflect both the attenuated HPA axis responsiveness characteristic of pediatric obesity and the moderate psychological challenge of the VR stressor used in this study. Two distinct autonomic response patterns were identified via exploratory factor analysis: (1) parasympathetic reactivity, associated with increased RMSSD and SDNN and decreased LF/HF, and (2) sympathetic activation, associated with increased heart rate and alpha-amylase levels and reduced RR intervals. Parasympathetic reactivity was correlated with lower perceived stress and anxiety, but also paradoxically with higher uncontrolled eating (UE). In contrast, sympathetic activation was associated with greater cognitive restraint (CR) and higher anxiety ratings. Conclusions: This study demonstrates that immersive VR game exposure elicits measurable autonomic and subjective stress responses in children and adolescents with obesity, and that individual differences in physiological reactivity are relevantly associated with eating behavior traits. The findings suggest that parasympathetic and sympathetic profiles may represent distinct behavioral patterns with implications for targeted intervention.

According to “The World Health Organization”, obesity during childhood is directly associated with multiple complications and with an increased risk of the installation of various pathologies. Considering the increase in this pathology among children and teenagers, new instruments of prevention are needed. Virtual reality is an innovative tool that offers several advantages over classical therapies, becoming important in various medical fields, starting from phobia treatment, pain relief, and body image perception to education. This technology has been successfully used to study the influence of virtual cues on behavioral responses and can be useful in nutritional education as well as understanding eating behavior. The objective of this scoping review study is to understand the impact of virtual supermarket exposure on individuals’ food choices and to explore the potential of technology on nutrition education in the general population. It seeks to explore purchasing based on product appearance and placement, food prices, nudging conditions and under-pressure decision making. A manual literature search was conducted using the databases Web of Science, SCOPUS and Google Scholar. Included articles were published between 2012 and 2024 using immersive virtual and augmented supermarket environments as a tool to understand food choices and education. The results showed that using higher immersion can be efficient in understanding food choices, rather than a lower immersive tool. The advantage of immersive virtual reality is highlighted by the sense of presence it offers, compared to other devices, providing a safe, controlled environment for users.

Assistive devices in rehabilitation engineering are essential for enhancing the quality of life for individuals with disabilities by supporting their mobility, communication, and daily activities. Recent advancements in robotics, artificial intelligence, and biomedical engineering have led to significant innovations in this field. This chapter explores key technologies and developments in assistive and rehabilitation devices. Robotic exoskeletons, wearable devices designed to assist or augment limb and body movement, benefit individuals with spinal cord injuries, stroke, or conditions like cerebral palsy. Brain-computer interfaces (BCIs) enable those with severe motor impairments, such as amyotrophic lateral sclerosis (ALS) or quadriplegia, to control devices using brain signals alone. Virtual reality (VR) and augmented reality (AR) are increasingly used in therapy, providing interactive, controlled environments where patients can practice movements and cognitive tasks. Additionally, Artificial Intelligence (AI) and Machine Learning (ML) contribute to rehabilitation by personalizing therapy, analyzing movement patterns, and predicting recovery progress. These innovative technologies make assistive devices more intuitive, accessible, and cost-effective, improving rehabilitation outcomes.

A complex interaction between the sensory, motor, and cognitive systems allows the human body to maintain postural balance, which is an essential function. This ability can be compromised by conditions that impact proprioceptive perception, muscle control, and mobility, such as stroke, Parkinson's disease, or musculoskeletal injuries. The development of a functional rehabilitation system that combines elements of body weight monitoring with an interactive setting is proposed in this paper. Four load cells are incorporated into the platform and are connected to an Arduino Uno microcontroller using HX711 load cell amplifier modules. An application created in Unity receives the signals and uses them to analyze pressure changes in four different directions. The device converts body weight distribution data into game commands with a high level of precision through individual calibration. This enhances the effectiveness and engagement of the rehabilitation process, encouraging active user participation and enhancing neuroplasticity mechanisms.

Background/Objectives: Acute stress is known to influence food-related motivation and decision-making, often promoting a preference for energy-dense, palatable foods. However, traditional laboratory paradigms have limited ecological validity. This study examined the relationship between stress-induced physiological changes, eating behavior traits, and food cravings using a virtual reality (VR) adaptation of the Trier Social Stress Test (VR-TSST) followed by a VR supermarket task in adolescents. Methods: Thirty-eight adolescents (mean age 15.8 ± 0.6 years) participated in the study. Physiological parameters (HR, QT, PQ intervals) were recorded pre- and post-stress using a portable ECG device (WIWE). Perceived stress and eating behavior traits were evaluated with the Perceived Stress Scale (PSS) and the Three-Factor Eating Questionnaire (TFEQ-R21C), respectively. Immediately after the VR-TSST, participants performed a VR supermarket task in which they rated cravings for sweet, fatty, and healthy foods using visual analog scales (VAS). Paired-samples t-tests examined pre–post changes in physiological parameters, partial correlations explored associations between ECG responses and eating traits, and a 2 × 3 mixed-model Repeated Measures ANOVA assessed the effects of food type (sweet, fatty, healthy) and uncontrolled eating (UE) group (low vs. high) on post-stress cravings. Results: Acute stress induced significant increases in HR and QTc intervals (p < 0.01), confirming a robust physiological stress response. The ANOVA revealed a strong main effect of food type (F(1.93, 435.41) = 168.98, p < 0.001, η2p = 0.43), indicating that stress-induced cravings differed across food categories, with sweet foods rated highest. A significant food type × UE group interaction (F(1.93, 435.41) = 16.49, p < 0.001, η2p = 0.07) showed that adolescents with high UE exhibited greater cravings for sweet and fatty foods than those with low UE. Overall, craving levels did not differ significantly between groups. Conclusions: The findings demonstrate that acute stress selectively enhances cravings for high-reward foods, and that this effect is modulated by baseline uncontrolled eating tendencies. The combined use of VR-based stress induction and VR supermarket simulation offers an innovative, ecologically valid framework for studying stress-related eating behavior in adolescents, with potential implications for personalized nutrition and the prevention of stress-induced overeating.

heart rate variability (HRV) is a simple way to explore autonomic nervous activity. Current studies emphasize the need to identify a dependent relationship between smoking habit and HRV. The findings of these studies could demonstrate that smoking can have both an acute and a chronic effect on HRV. The aim of this study is to investigate the relationship between smok-ing and anthropometric data such as body weight, body fat distribution and heart rate variability. Our study involves measuring HRV parameters at rest and during two tests that are part of the Ewing test battery. Through the obtained data we can demonstrate that active smoking is associ-ated with a reduction in HRV. At the same time, we can state that HRV is affected by different behavior patterns of the smoker.complex individualized rehabilitation treatment.

Heart rate variability (HRV) is a simple way to explore autonomic nervous activity. Current studies emphasize the need to identify a dependent relationship between smoking habit and HRV. The findings of these studies could demonstrate that smoking can have both an acute and a chronic effect on HRV. The aim of this study is to investigate the relationship between smoking and anthropometric data such as body weight, body fat distribution and heart rate variability. Our study involves measuring HRV parameters at rest and during two tests that are part of the Ewing test battery. Through the obtained data we can demonstrate that active smoking is associated with a reduction in HRV. At the same time, we can state that HRV is affected by different behavior patterns of the smoker.

The Internet of Things (IoT) is currently rapidly being incorporated into many fields, but there are still some fields, such as healthcare, where IoT adoption is much slower. Medical IoT refers to a range of medical devices and people that rely on wireless communication to enable healthcare data exchange, remote monitoring, and patient rehabilitation for a better quality of life for the patient. Medical IoT can provide better medical care and rehabilitation services under the careful supervision of the physician, resulting in more cost-effective systems for hospitals as well as for the patient. Due to the regulatory, ethical, and technological challenges of biomedical hardware, the growth of medical IoT is still inhibited. The chapter provides an overview of the various technologies and protocols used for the Internet of Medical Things (IoT), with an overview of the current technologies, applications, and challenges.

The revolution of physiotherapy is achieved by the application of artificial intelligence (AI) in medical rehabilitation programs. While the integration of virtual reality (VR) and robotics has already marked significant strides in the industry, AI is taking patient rehabilitation to new heights. The integration of AI, VR and robotics in physiotherapy linked to a significant leap forward in patient rehabilitation. These technologies not only increase the effectiveness of the treatment, but also make it more accessible and attractive. In this article, we propose to study the use of AI in the management of medical rehabilitation programs, and we present a comprehensive survey of the latest empirical studies, highlighting AI-based technologies that facilitate the rehabilitation process, trying to demonstrate that the integration of AI in medical rehabilitation programs offers a transformative approach to patient care.

Augmented reality (AR) is a technology that combines the real world with virtual elements, providing users with an enhanced interactive experience. AR has been used in a variety of fields, including medicine and bioengineering. In terms of training medical bioengineers, augmented reality can play a significant role in improving the learning process and understanding of human anatomy, medical procedures, and medical devices. Using AR technology, medical bioengineers can benefit from the following advantages in their training: three-dimensional visualization and interaction, medical procedure simulation, real-time guidance, collaboration and communication, medical device innovation and development. Using these technologies in the training of medical bioengineers, they can practice and become familiar with performing these procedures in a safe and controlled virtual environment. This can help increase confidence and practical skills before working in real life.

In this paper, the signal processing of non-electrical biosignals is studied and a number of results are presented. One of the most famous examples is that of the respiratory cycle. So that recordings of respiratory signals can often have a series of artifacts/noises at the time of signal acquisition. To eliminate these noises, a variety of high-performance digital filters are needed. Carrying out a filtering as thorough as possible depending on the type of signal given for analysis and the type of filter chosen for processing. After filtering, the biosignals can be exported, allowing a doctor to analyze this information and establish a diagnosis or a certain medical behavior. The working and display interface of the processed signals was developed with the help of the MATLAB 2021B software for the digital filtering of the signals and also for their segmentation according to the type of recognized noise. Having introduced a series of functions necessary for filtering and segmenting the signals given in the analysis. The digital filter specific to respiratory cycles being Butterworth that responds to the frequency range of interest. In addition to this filter, the High pass, Low pass filters and last but not least the Notch 50 Hz filter were also implemented. Certain functions were also introduced to segment signals according to function. Recommendations are presented for selecting the most appropriate topology for the applied filter, whether it is finite impulse response or infinite impulse response. In order to check the filter results, we accessed the public database “ICBHI 2017 Challenge”. This reference database supports those who want to evolve in the field of respiratory parameter analysis and research. The paper compares (before and after filtering) a series of respiratory biosignals, which can be exported for analysis and comparison.

Evaluating the maintenance of medical equipment is essential to ensure the proper and safe operation of these systems, which are essential for the diagnosis, treatment, and monitoring of patients. Throughout the life cycle of medical equipment, regular maintenance and calibration are required to maintain its performance and accuracy. In addition, it is important that medical equipment undergoes regular reviews to verify that it still meets applicable standards and regulations. If a malfunction or problem is found in the operation of the medical equipment, it is essential to report it immediately to the supplier or distributor of the equipment so that timely corrective measures can be taken. Some key reasons for the importance of evaluating the maintenance of medical systems are patient safety, system reliability and availability, performance optimization, regulatory and standards compliance. This paper presents aspects related to maintenance, management, maintenance, and quality management of medical equipment by firstly analyzing the technological and medical information obtained through various questionnaires. Summarize the issues encountered in medical equipment maintenance and design a medical quality control system to manage the maintenance and quality control of medical equipment. In the medical equipment maintenance system, scientific management theories and methods are used to predict, adjust, inspect, and account for the quality of the entire medical process and to establish a complete quality monitoring and management system. Compliance with standards and regulations, as well as adequate training of medical personnel, can ensure the correct and efficient use of this equipment, thus contributing to the provision of high-quality medical care.

Managing cardiopulmonary rehabilitation in patients with COVID-19 remains a global challenge due to the facets of this virus. The technologies used in the rehabilitation of post-COVID-19 patients fail to keep pace with the global epidemiological developments. The purpose of this article is to review the medical technologies used in post-COVID-19 cardiopulmonary rehabilitation and the innovations that have allowed us to adapt and care for our patients. The pandemic highlighted the need for seismic changes in diagnostic and rehabilitation paradigms. We discuss advances in telemedicine and telemedicine-based rehabilitation, remote patient monitoring and emerging technologies used in cardiopulmonary rehabilitation. The rapid adoption of modern technologies in the practice of post-COVID-19 cardiopulmonary rehabilitation is promising and can improve patients’ access to complex rehabilitation programs with outstanding results.

Modern rehabilitation procedures use devices that provide physical therapists with various types of information to improve assessment of patient progress during rehabilitation plans. The new trend of these technologies is the development of safe, portable and comfortable wearable devices with extensive applications in various environments (medical clinics or at the patient's home). The present work presents a portable and safe device for hand rehabilitation, consisting of five finger force sensors and a palmar sensor arranged in the ball, capable of capturing pressure signals during the execution of movements guided by the physiotherapist or by a video game/virtual reality. A 3-axis accelerometer was used to spatially monitor the patient's movements. A series of games with different levels of difficulty were created, through which the degree of mobility of the patient can be monitored depending on the game he chooses and at the same time reflected by the score obtained at the end of the game. Also, to be more interactive, the interface was chosen to play with 2 players simultaneously. So that they can choose to play in the team or as competitors. The system allows users to show different routines to guide them in their use and also evaluates pressure signals and response time.