Iosif Birlescu

Neurological disorders such as Parkinson’s and Alzheimer’s diseases often involve overlapping motor and cognitive impairments that motivate integrated rehabilitation approaches. This study presents the technical validation of a dual-modality rehabilitation platform that combines haptic-based motor interaction with cognitive engagement through an adaptive Sudoku task in healthy adults under simulated tremor conditions. The system integrates a real-time tremor-filtering pipeline based on discrete wavelet denoising, Kalman smoothing, and wavelet packet decomposition, designed to attenuate high-frequency oscillations while preserving voluntary motion. The preclinical evaluation was carried out in two stages: (i) technical validation with healthy adults performing a standardized cognitive–haptic task under three conditions (no tremor, simulated tremor without filtering, simulated tremor with filtering) and (ii) extended usability testing with older participants without diagnosed neurological disorders. Quantitative evaluation focused on latency, performance degradation under simulated tremor, and partial restoration with filtering, while usability was assessed using the System Usability Scale (SUS). The platform achieved low end-to-end latency (41.4 ± 1.4 ms) and high usability (overall mean SUS = 81.4 ± 6.2), indicating stable performance and positive user feedback. Filtering significantly improved performance compared with unfiltered tremor but did not fully restore baseline performance, highlighting the current algorithm as a first-step compensation strategy rather than a complete solution. This work therefore demonstrates technical feasibility and interaction performance in healthy participants under simulated tremor; it does not assess clinical effectiveness and is intended to inform subsequent patient studies in populations with neurodegenerative diseases.

Robotic-assisted motor rehabilitation has seen significant development over the past decade, driven by the distinct advantages that robots offer in this medical task. A key aspect of robotic-assisted rehabilitation is ensuring that the performed rehabilitation exercises are safely planned (i.e., without the risk of patient injury or anatomic joint strain). This paper presents a new numerical approach to trajectory planning for a LegUp parallel robot designed for lower limb rehabilitation. The proposed approach generates S-shaped motion profiles, also called S-curves, with precise control over all kinematic parameters, resulting in smooth acceleration and deceleration. This approach ensures the safety and effectiveness of rehabilitation exercises by minimizing strain on the patient’s anatomical joints. The mathematical models employed (numerical integration and differentiation) are well-established and computationally efficient for real-time implementation in the robot’s control hardware. Experimental tests using LegUp validate the effectiveness of the proposed trajectory-smoothing approach.

Background: Cerebrovascular accident, commonly known as stroke, Parkinson’s disease, and multiple sclerosis represent significant neurological conditions affecting millions globally. Stroke remains the third leading cause of death worldwide and significantly impacts patients’ hand functionality, making hand rehabilitation crucial for improving quality of life. Methods: A comprehensive literature review was conducted analyzing over 300 papers, and categorizing them based on mechanical design, mobility, and actuation systems. To evaluate each device, a database with 45 distinct criteria was developed to systematically assess their characteristics. Results: The analysis revealed three main categories of devices: rigid exoskeletons, soft exoskeletons, and hybrid devices. Electric actuation represents the most common source of power. The dorsal placement of the mechanism is predominant, followed by glove-based, lateral, and palmar configurations. A correlation between mass and functionality was observed during the analysis; an increase in the number of actuated fingers or in functionality automatically increases the mass of the device. The research shows significant technological evolution with considerable variation in design complexity, with 29.4% of devices using five or more actuators while 24.8% employ one or two actuators. Conclusions: While substantial progress has been made in recent years, several challenges persist, including missing information or incomplete data from source papers and a limited number of clinical studies to evaluate device effectiveness. Significant opportunities remain to improve device functionality, usability, and therapeutic effectiveness, as well as to implement advanced power systems for portable devices.

Stroke, amyotrophic lateral sclerosis (ALS), and Parkinson’s disease are some of the conditions that can lead to neuromotor disabilities requiring rehabilitation. To address the socio-economic burden that is amplified by the rapidly increasing elderly population, traditional rehabilitation techniques have recently been complemented by technological advancements, particularly Robot-Assisted Therapy (RAT). RAT enhances motor learning by improving both accuracy and consistency. This study proposes an innovative rehabilitation system that combines serious gaming and augmented reality (AR) with the LegUp parallel robot, developed for the spatial rehabilitation of the hip, knee, and ankle in bed-ridden patients. The system aims to improve patient outcomes and actively involve patients in their therapy. Electro-goniometers and a HoloLens 2 device are used to provide immediate feedback about the position of the patient’s joints, forming the basis of an interactive game in which the patient moves their leg to reach various targets. Two game modes were developed, each targeting different aspects of neuromotor rehabilitation, such as coordination, strength, and flexibility. Preliminary findings suggest that combining RAT with augmented reality-based serious gaming can increase patient motivation and engagement. Furthermore, the personalized and interactive nature of the therapy holds the potential to improve rehabilitation outcomes by fostering sustained engagement and effort.

The control system in surgical robots must ensure patient safety and real time control. As such, all the uncertainties which could appear should be considered into an extended model of the plant. After such an uncertain plant is formed, an adequate controller which ensures a minimum set of performances for each situation should be computed. As such, the continuous-time robust control paradigm is suitable for such scenarios. However, the problem is generally solved only for linear and time invariant plants. The main focus of the current paper is to include m-link serial surgical robots into Robust Control Framework by considering all nonlinearities as uncertainties. Moreover, the paper studies an incipient problem of numerical implementation of such control structures.