The smart physical rehabilitation becomes a new reality and challenge regarding the technology adoption by the users and high costs. In this context, the paper presents a smart physical rehabilitation system that combines augmented reality serious games and wearable sensor network to improve the patient engagement during physical rehabilitation. The implemented system provides also health status assessment acquiring vital data to be analyzed by clinical professionals. The wearable sensing is based on the Simblee platform that captures kinematic and dynamic quantities associated with lower limb motion using an IMU as so as the muscular activation and cardiac activity. The Healthcare IoT compatibility is assured by an augmented reality platform based on iOS smart phone that communicates with wearable wireless sensor network and after primary processing of the data it uploads the analyzed in an AWS cloud based web platform.

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 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.

Throughout the years, physiotherapy has become increasingly crucial in recovering members of the human body. However, for a faster recovery, physiotherapy requires a commitment from the patients to embrace therapy outside of its sessions. This commitment is not always accomplished due to a lack of motivation. To help with this problem, Gamification was introduced in treatment. Patients strive to achieve every level, which allows a better and more efficient recovery. To obtain an even better and faster recovery, augmented reality and virtual reality started to be used in this field of medicine. This primary project goal is to create a challenge game to improve the inferior members' functional recovery. This game will be built with the Unity3D platform, using the RFduino microcontroller, Structure Sensor, and Apple Watch. To help code this project, platforms such as Swift, PHP, MySQL, and Arduino will also be used. The obtained results were satisfying. Rehabilitation games help patients improve in therapy and give information that otherwise would not be available. In the future, having an integrated sensor to monitor a real-time heart rate would improve this project.

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.