Health monitoring is crucial in hospitals and rehabilitation centers. Challenges can affect the reliability and accuracy of health data. Human error, patient compliance concerns, time, money, technology, and environmental factors might cause these issues. In order to improve patient care, healthcare providers must address these challenges. We propose a non-intrusive smart sensing system that uses a SensFloor smart carpet and an inertial measurement unit (IMU) wearable sensor on the user’s back to monitor position and gait characteristics. Furthermore, we implemented machine learning (ML) algorithms to analyze the data collected from the SensFloor and IMU sensors. The system generates real-time data that are stored in the cloud and are accessible to physical therapists and patients. Additionally, the system’s real-time dashboards provide a comprehensive analysis of the user’s gait and balance, enabling personalized training plans with tailored exercises and better rehabilitation outcomes. Using non-invasive smart sensing technology, our proposed solution enables healthcare facilities to monitor patients’ health and enhance their physical rehabilitation plans.

Stress is a natural feeling of not being able to cope with specific demands and events, and it may even worsen a person’s health, especially in chronic disease patients. Stress questionnaires are inefficient and time-consuming. Several models for stress estimation are based on facial analysis, voice recognition, thermography, electrocardiography (ECG), and photoplethysmography (PPG), but they are not practical for patients. More robust systems with multiple parameters use devices that are incompatible in the same ecosystem. Machine learning techniques can also be used, but most studies only detect stress, few classify it, and none quantify it. The latest developments in health state monitoring present PPG as the leading solution. Since it is noninvasive and can be integrated into wearable devices, it is more user-friendly and could be used in smart environments. Since it is noninvasive and can be integrated into wearable devices, it is more user-friendly and could be used in smart environments. The proposed work introduces novelty regarding PPG signal processing algorithms to extract multiple physiological parameters simultaneously. In terms of innovations, a multichannel detection system with a distributed computing platform is considered, which, besides containing the algorithms, also includes the introduction of new physiological parameters and the proposal of a model for estimating stress levels based on fuzzy logic, classifying stress into five levels. To validate the results, experimental protocols were created to induce thermal stress in volunteers, which yielded excellent system efficiency and accuracy indicators. The health status monitoring results and estimations are presented using a mobile application that was also developed.

Stress is the inherent sensation of being unable to handle demands and occurrences. If not properly managed, stress can develop into a chronic condition, leading to the onset of additional chronic health issues, such as cardiovascular illnesses and diabetes. Various stress meters have been suggested in the past, along with diverse approaches for its estimation. However, in the case of more serious health issues, such as hypertension and diabetes, the results can be significantly improved. This study presents the design and implementation of a distributed wearable-sensor computing platform with multiple channels. The platform aims to estimate the stress levels in diabetes patients by utilizing a fuzzy logic algorithm that is based on the assessment of several physiological indicators. Additionally, a mobile application was created to monitor the users’ stress levels and integrate data on their blood pressure and blood glucose levels. To obtain better performance metrics, validation experiments were carried out using a medical database containing data from 128 patients with chronic diabetes, and the initial results are presented in this study.

Stress is an inherent sensation of being unable to handle demands and occurrences. If not properly managed, stress can develop into a chronic condition, leading to the onset of additional chronic health issues like cardiovascular illnesses and diabetes. Various stress meters have been suggested in the past, along with diverse approaches for its estimate. However, in the case of more serious health issues such as hypertension and diabetes, the results can be significantly improved. This study presents the design and implementation of a distributed wearable sensor computing platform with multiple channels. The platform aims to estimate stress levels in diabetes patients by utilizing a fuzzy logic algorithm that is based on the assessment of several physiological indicators. Additionally, a mobile application was created to monitor users' stress levels and integrate data on their blood pressure and blood glucose levels. Validation experiments were performed on patients with chronic diabetes, and the initial findings are presented in the study.

This chapter explores strategies to increase well-being and reduce stress by analyzing the effects of external stimuli on physiological responses. It examines musical sound stimulation and its impact on stress and well-being by analyzing heart rate variability (HRV), a key indicator of autonomic nervous system (ANS) balance. It also presents a healthcare-centered Internet of Things (IoT) system for cardiac assessment using photoplethysmography (PPG) and ballistocardiography (BCG). Experimental results from indoor applications evaluate HRV and respiratory patterns under varying temperature and humidity conditions to assess their short-term impact on thermal comfort. In addition, this chapter also presents studies conducted through the implementation of new PPG signal processing algorithms to extract several physiological markers simultaneously, such as heart rate (HR), HRV, respiratory rate (RR) and blood oxygen saturation (SpO2), through the implementation of multichannel detection systems with distributed computing platforms. New models for the classification and quantification of stress levels are also discussed, using artificial intelligence techniques such as fuzzy logic and more complex algorithms. Furthermore, the chapter addresses the development of mobile applications, serving as a patient support tool and also as a way of acquiring important markers, from which it is possible to draw correlations between stress, blood pressure and blood glucose levels. As a way of trying to counteract the effects of stress on patients’ health, studies on alternative techniques for continuous relaxation are also discussed, such as the use of holistic medicine and aromatherapy.