Gait is an important part of our life, as it affects many daily activities. Special footwear is fundamental to obtain an ergonomic gait and to extract data for analysis. The plantar foot pressure can be employed to detect many kinds of disorders, suggest improvements in treatments, rehabilitation tasks, patient monitoring, development of orthopedic devices, and other applications. In recent years, attention to this topic has grown and is reflected in many works issued in both commercial and academic groups, and has focused on the development of devices for foot plantar pressure measurement with applications in medicine, sports, and research. First works on this subject appeared around 1963 and have continuously evolved with emerging technologies. This paper reviews the reported developments in the field of footwear-embedded sensors for gait measurement, monitoring, diagnosis, and analysis in rehabilitation. Future work is proposed to improve the field of measurement of the footprint with electronic devices.

Digital transformation is reshaping the healthcare field by streamlining diagnostic workflows and improving disease management. Within this transformation, Digital Twins (DTs), which are virtual representations of physical systems continuously updated by real-world data, stand out for their ability to capture the complexity of human physiology and behavior. When coupled with Artificial Intelligence (AI), DTs enable data-driven experimentation, precise diagnostic support, and predictive modeling without posing direct risks to patients. However, their integration into healthcare requires careful consideration of ethical, regulatory, and safety constraints in light of the sensitivity and nonlinear nature of human data. In this review, we examine recent progress in DTs over the past seven years and explore broader trends in AI-augmented DTs, focusing particularly on movement rehabilitation. Our goal is to provide a comprehensive understanding of how DTs bolstered by AI can transform healthcare delivery, medical research, and personalized care. We discuss implementation challenges such as data privacy, clinical validation, and scalability along with opportunities for more efficient, safe, and patient-centered healthcare systems. By addressing these issues, this review highlights key insights and directions for future research to guide the proactive and ethical adoption of DTs in healthcare.

Plants have mechanisms to perceive and transmit information between their organs and tissues to respond quickly to abiotic and biotic external stimuli from the environment, producing different electrical potential types. It is reported on the generation and conduction of electrochemical impulses within different tissues and organs of the plant, which have been acquired using different methods to use them in various applications in the improvement of the agro-industrial sector and the development of different types of phytosensors. In this review paper, the various studies that have been carried out since Pfeffer, Burdon-Sanderson, Darwin, Haberlandt, and Bose discovered electrical activity in plants until today are reported. Plants provide mechanisms to perform biosensors based on responding to environmental changes, opening a great path for the design of low-cost and highly sensitive sensors and sensor networks, the current trend is towards experimental analysis using various stimuli.

The function of the back, hip, knee, ankle and other orthopedic alterations of the human body can be analyzed through plantar pressure distribution. The development of Clinical Decision Support Systems (CDSS) can handle the uncertainties present in biological data using different Artificial Intelligence techniques to obtain accurate and easy-to-use systems. This paper presents the application of a Fuzzy Cognitive Map (FCM) formulation, for knowledge extraction in the classification of human plantar foot alterations, with a relatively small and transparent model. The FCM is trained using the Bacterial Search Optimization Algorithm (BFOA). One hundred and twenty-five volunteer subjects (aged 20-68 years) participated in the study. Classification of the foot into normal (n=31), flat (n=32), cavus type III (n=31) and cavus type IV (n=31) to train the system was performed by specialized physicians. The test was performed by walking on a FreeMed platform. The proposed method shows an accuracy rate of about 89% in the classification task and allows extracting information related to the important factors that the system considers to make a decision.