Parkinson’s disease (PD) is one of the most common neurodegenerative diseases, affecting millions of people worldwide, especially among the elderly population. It has been demonstrated that handwriting impairment can be an important early marker for the detection of this disease. The aim of this study was to propose a simple and quick way to discriminate PD patients from controls through handwriting tasks using machine-learning techniques. We developed a telemonitoring system based on a user-friendly application for drawing tablets that enabled us to collect real-time information about position, pressure, and inclination of the digital pen during the experiment and, simultaneously, to supply visual feedback on the screen to the subject. We developed a protocol that includes drawing and writing tasks, including tasks in the Italian language, and we collected data from 22 healthy subjects and 9 PD patients. Using the collected signals and data from a preexisting database, we developed a machine-learning model to automatically discriminate PD patients from healthy control subjects with an accuracy of 77.5%.

Parkinson’s disease (PD) is a progressive neurodegenerative disorder that impairs motor function, including the fine motor control required for handwriting. Traditional diagnostic methods often lack sensitivity and objectivity in the early stages, limiting opportunities for timely intervention. There is a growing need for non-invasive, accessible tools capable of capturing subtle motor changes that precede overt clinical symptoms. Among early PD manifestations, handwriting impairments such as micrographia have shown potential as digital biomarkers. However, conventional handwriting analysis remains subjective and limited in scope. Recent advances in artificial intelligence (AI) and machine learning (ML) enable automated analysis of handwriting dynamics, such as pressure, velocity, and fluency, collected via digital tablets and smartpens. These tools support the detection of early-stage PD, monitoring of disease progression, and assessment of therapeutic response. This paper highlights how AI-enhanced handwriting analysis provides a scalable, non-invasive method to support diagnosis, enable remote symptom tracking, and personalize treatment strategies in PD. This approach integrates clinical neurology with computer science and rehabilitation, offering practical applications in telemedicine, digital health, and personalized medicine. By capturing dynamic features often missed by traditional assessments, AI-based handwriting analysis contributes to a paradigm shift in the early detection and long-term management of PD, with broad relevance across neurology, digital diagnostics, and public health innovation.

In this work we present and evaluate a multi-source power management system, based on human body energy harvesting, to extend the battery lasting of an electronic sensory glove, used to measure flexion/extension, abduction/adduction movements of fingers of the hand. The system exploits heat of the human forearm and pressure impressed by the foot heel during walking, so to gather additional energy. The aim is to allow hours of energy-autonomy for the user working with the sensory glove. Such a glove is equipped with a number of flex sensors which furnish data from finger movements, acquired and pre-processed by a microcontroller, and wireless sent to a Personal Computer for analysis, visualization and storage purposes. The multi-source harvester is based on vibrational and thermic sources. Prototype discrete element boards were designed and tested for the microelectronics integration. Measurement results demonstrate how the overall system extends the battery lasting time up to 20%.

Flex sensors are frequently used as wearable tools for unobtrusively tracking human joint movements. Among all the flex sensor types, the resistive ones are the mostly adopted thanks to their electrical and mechanical properties capable to furnish electrical resistance values related to the amount of mechanical flexion. In particular, resistive flex sensors have been finding many applications when embedded into gloves, in order to evaluate fine flexion/extension movements of the finger joints. Within this frame, here we investigate the possible utilization of a different type of flex sensors embedded into gloves, i.e., the stretch ones, since the stretch sensors change in resistance proportionally to their stretch that can be just obtained when laid on-top of a finger joint. In such a view, here we compare the characteristics of commercial flex and stretch sensors obtained by means of an ad-hoc setup and protocol. Results demonstrate the different peculiarities of the two different types of sensors, so to determine when it is convenient to adopt one type instead of the other.

Sensor-based systems for detecting, recognizing and measuring the position and movement of the human body in a three-dimensional space with great accuracy and precision have been widespread in last decades. Starting from consolidated optical systems, which still represent the reference for other cost effective systems, this work presents smart solutions for motion capture and posture recognition systems, either based on the most popular inertial sensors, but also flex sensors, such as the sensory gloves for measurement of bending angles of finger joints, whose development increases at the same rate of application developed to exploit the potential of these systems. Recently surface electromyography (sEMG) is gaining more and more importance in gesture recognition systems for disabled skills assessment and prosthesis control of transradial amputees.