A few decades ago, robotics started to be implemented in the medical field, especially in the rehabilitation of patients with different neurological diseases that have led to neuromuscular disorders. The main concern regarding medical robots is their safety assurance in the medical environment. The goal of this paper is to assess the risk of a medical robotic system for elbow and wrist rehabilitation in terms of robot and patient safety. The approached risk assessment follows the ISO12100:2010 risk management chart in order to determine, identify, estimate, and evaluate the possible risk that can occur during the use of the robotic system. The result of the risk assessment process is further analyzed using a fuzzy logic system in order to determine the safety degree conferred during the use of the robotic system. The innovative process concerning the risk assessment allows the achievement of a reliable medical robotic system both for the patient and the clinicians as well. The clinical trials performed on a group of 18 patients validated the functionality and the safe behavior of the robotic system.

Motor disability is a key feature of many neurological diseases, influencing the social roles of affected patients and their ability to perform daily life activities. Current rehabilitation capacities are overwhelmed by the age-related increase of motor dysfunctions seen, for example, in stroke, extrapyramidal or neuromuscular diseases. As the patient to rehabilitation personnel ration increases, robotic solutions might establish the possibility to rapidly satisfy the increasing demand for rehabilitation. This paper presents an inaugural exploratory study which investigates the interchangeability of a novel experimental robotic rehabilitation device system with classical physical therapy, using a multimodal neurophysiological assessment of the motor system—quantitative electroencephalogram (EEG), motor conduction times and turn/amplitude analysis. Preliminary results show no significant difference between the two methods; however, a significant effect of the therapy was found on different pathologies (beneficial for vascular and extrapyramidal, or limited, and only on preventing reduction of joint movements in neuromuscular).

The use of robotic systems in physical rehabilitation protocols has become increasingly attractive and has been given more focus in the last decade as a result of the high prevalence of motor deficits in the population, which is linked to an overburdened healthcare system. In accordance with current trends, three robotic devices have been designed, called ParReEx Elbow, ParReEx Wrist, and ASPIRE, which were designed to improve upper-limb medical recovery (shoulder, elbow, forearm, and wrist). The three automated systems were tested in a hospital setting with 23 patients (12 men and 11 women) suffering from motor deficits caused by various neurological diseases such as stroke, Parkinson’s disease, and amyotrophic lateral sclerosis (ALS). The patients were divided into three groups based on their pathology (vascular, extrapyramidal, and neuromuscular). Objective clinical measures, such as the Medical Research Council (MRC) scale, goniometry, and dynamometry, were used to compare pre- and post-rehabilitation assessments for both robotic-aided and manual physical rehabilitation therapy. The results of these tests showed that, with the exception of a few minor differences in muscular strength recovery, the robotic-assisted rehabilitation methods performed equally as well as the manual techniques, though only minor improvements were validated during short-term rehabilitation. The greatest achievements were obtained in the goniometric analysis where some rehabilitation amplitudes increased by over 40% in the vascular group, but the same analysis returned regressions in the neuromuscular group. The MRC scale analysis returned no significant differences, with most regressions occurring in the neuromuscular group. The dynamometric analysis mostly returned improvements, but the highest value evolution was 19.07%, which also in the vascular group. While the results were encouraging, more research is needed with a larger sample size and a longer study period in order to provide more information regarding the efficacy of both rehabilitation methods in neurological illnesses.

Our main purpose was to investigate if the chronic treatment with the disease-modifying drug natalizumab shows quantifiable effect on BDNF levels in multiple sclerosis patients. BDNF plasma concentration was evaluated using enzyme-linked immunosorbent assay in healthy individuals, not treated multiple sclerosis patients and patients treated with natalizumab. Multiple sclerosis patients have a significantly lower amount of peripheral BDNF than healthy individuals. Patients treated with natalizumab have significantly higher BDNF levels than not treated patients. Chronic natalizumab treatment is associated with significantly increased plasma BDNF concentration in multiple sclerosis.

This paper presents the development of a fuzzy-PID control able to adapt to several robot–patient interaction modes by monitoring patient evolution during the rehabilitation procedure. This control system is designed to provide targeted rehabilitation therapy through three interaction modes: passive; active–assistive; and resistive. By integrating a fuzzy inference system into the classical PID architecture, the FPID controller dynamically adjusts control gains in response to tracking error and patient effort. The simulation results indicate that, in passive mode, the FPID controller achieves a 32% lower RMSE, reduced overshoot, and a faster settling time compared to the conventional PID. In the active–assistive mode, the FPID demonstrates enhanced responsiveness and reduced error lag when tracking a sinusoidal reference, while in resistive mode, it more effectively compensates for imposed load disturbances. A rehabilitation scenario simulating repeated motion cycles on a healthy subject further confirms that the FPID controller consistently produces a lower overall RMSE and variability.