Carmen Delconte

Purpose: Cycle ergometer training improves gait in the elderly, but its effect in patients with Parkinson’s disease (PD) is not completely known. Methods: Twenty-nine PD inpatients were randomized to treadmill ( n = 13, PD-T) or cycle ergometer ( n = 16, PD-C) training for 3 weeks, 1 hour/day. Outcome measures were distance travelled during the 6-min walking test (6MWT), spatio-temporal variables of gait assessed by baropodometry, the Timed Up and Go (TUG) duration, the balance score through the Mini-BESTest, and the score of the Unified Parkinson’s Disease Rating Scale (UPDRS). Results: Sex, age, body mass index, disease duration, Hoehn-Yahr staging, comorbidity and medication did not differ between groups. At end of training, ANCOVA showed significant improvement, of similar degree, in both groups for 6MWT, speed, step length and cadence of gait, TUG, Mini-BESTest and UPDRS. Conclusions: This pilot study shows that cycle ergometer training improves walking parameters and reduces clinical signs of PD, as much as treadmill training does. Gait velocity is accompanied by step lengthening, making the gait pattern close to that of healthy subjects. Cycle ergometer is a valid alternative to treadmill for improving gait in short term in patients with PD.

The purpose of this study was to determine whether a conventional robot-assisted therapy of the upper limb was able to improve proprioception and motor recovery of an individual after stroke who exhibited proprioceptive deficits. After robotic sensorimotor training, significant changes were observed in kinematic performance variables. Two quantitative parameters evaluating position sense improved after training. Range of motion during shoulder and wrist flexion improved, but only wrist flexion remained improved at 3-month follow-up. These preliminary results suggest that intensive robot-aided rehabilitation may play an important role in the recovery of sensory function. However, further studies are required to confirm these data.

Corticobasal syndrome is characterized by asymmetric cortical sensorimotor dysfunction and parkinsonism; an altered cortical excitability has been reported. We explored with transcranial magnetic stimulation the motor cortical excitability in corticobasal syndrome, and the effects of slow repetitive transcranial magnetic stimulation. With transcranial magnetic stimulation, we studied two corticobasal syndrome patients. We determined bilaterally from the first dorsal interosseous muscle: relaxed threshold, and contralateral and ipsilateral silent period. We also evaluated the contralateral silent period after active/sham slow repetitive transcranial magnetic stimulation on the most affected side. At T0 the silent period was bilaterally short. On the most affected side, active slow repetitive transcranial magnetic stimulation induced a short lasting prolongation of the contralateral silent period. In corticobasal syndrome, transcranial magnetic stimulation showed a reduction cortical inhibitory phenomenon potentially reversed transiently by slow repetitive transcranial magnetic stimulation.

Rehabilitation robots have built-in technology and sensors that allow accurate measurement of movement kinematics and kinetics, which can be used to derive measures related to upper limb performance and highlight changes in motor behavior due to rehabilitation. This study aimed to assess the test-retest reliability of some robot-measured parameters by analyzing their intra-session and inter-session (day-by-day) variability. The study was carried out in two groups: 31 patients after stroke and 15 healthy subjects. Both groups practiced two different motor tasks consisting of point-to-point reaching movements in the shape of two geometrical figures that were selected for the assessment of global and directional (eight directions of the workspace) test-retest reliability. The reliability of six parameters measuring movement velocity, accuracy, efficiency and smoothness was assessed intra-session and inter-session by the ICC, SEM, and CV. Healthy subjects exhibited very high ICC values ( > 0.85) and low SEM for all parameters. Patients had high ICC values and low SEM but their global reliability was generally lower compared to healthy subjects. In addition, their inter-session reliability showed very high ICC values ( > 0.91) and low SEM for all parameters. Direction analysis showed that in some parameters the reliability was generally high but not homogeneous in all directions. In addition, some directions showed systematic error. This study demonstrates that robot-measured parameters are reliable and can be considered ideal candidates for use in combination with impairment and functional clinical scales to evaluate motor improvement during robot-assisted neurorehabilitation.

BACKGROUND: Robotic neurorehabilitation, thanks to high dosage/intensity training protocols, has the potential for a greater impact on impairment. OBJECTIVE: We aimed to analyze how time since the acute event may influence the motor recovery process

In robot-assisted neurorehabilitation, matching the task difficulty level to the patient's needs and abilities, both initially and as the relearning process progresses, can enhance the effectiveness of training and improve patients' motivation and outcome. This study presents a Progressive Task Regulation algorithm implemented in a robot for upper limb rehabilitation. It evaluates the patient's performance during training through the computation of robot-measured parameters, and automatically changes the features of the reaching movements, adapting the difficulty level of the motor task to the patient's abilities. In particular, it can select different types of assistance (time-triggered, activity-triggered, and negative assistance) and implement varied therapy practice to promote generalization processes. The algorithm was tuned by assessing the performance data obtained in 22 chronic stroke patients who underwent robotic rehabilitation, in which the difficulty level of the task was manually adjusted by the therapist. Thus, we could verify the patient's recovery strategies and implement task transition rules to match both the patient's and therapist's behavior. In addition, the algorithm was tested in a sample of five chronic stroke patients. The findings show good agreement with the therapist decisions so indicating that it could be useful for the implementation of training protocols allowing individualized and gradual treatment of upper limb disabilities in patients after stroke. The application of this algorithm during robot-assisted therapy should allow an easier management of the different motor tasks administered during training, thereby facilitating the therapist's activity in the treatment of different pathologic conditions of the neuromuscular system.

Patient motivation is an important factor in rehabilitation. The difficulty level of the motor task, the awareness of the performance obtained, and the quantity and quality of feedbacks presented to the patient can influence patient motivation and produce different ways of acting and different performances. This study presents a Progressive Task Regulation algorithm able to evaluate the patient's performance during training and automatically change the features of the reaching movement, so as to adapt automatically the difficulty level of the motor task to the patient's ability. Use of the progressive task regulation algorithm should promote patient motivation throughout the course of treatment.

The status of motor function in stroke survivors and the effect of any therapeutic intervention are generally measured by physiotherapists using clinical assessment scales that probe specific aspects of a subject's motor behavior. Although they are widely accepted, these measurement tools are limited by interrater and intrarater reliability and are time-consuming to apply. This paper analyzes the changes in movement kinematics and kinetics during robot-aided neurorehabilitation of subjects after stroke and verifies the possibility of estimating outcome measures by means of a set of robot measured parameters.

The aim of this study was to describe in detail a new method, called normalized force control parameter (nFCP), to measure changes in movement dynamics obtained during robot-aided neurorehabilitation, and to evaluate its ability to estimate the clinical scales. The study was conducted in a group of 18 subjects after chronic stroke who underwent robot therapy of the upper limb. We used two different measures of movement dynamics to assess patients' performance during each session of training: the nFCP and force directional error (FDE), both measuring the directional error of the patient-exerted force applied to the end-effector of the robot device. Both metrics exhibited significant changes over the three-week course of treatment. The comparison between nFCP and FDE slopes showed a significant and high correlation (r = 0.79; p < 0.001), indicating that the two parameters are closely correlated. The FDE informed on the direction of the force error, while the nFCP showed a better performance in predicting the clinical scale values. Assessment of the time course of recovery showed that nFCP, FDE and the movement smoothness improved quickly at first and then plateaued, while steady gains in mean velocity of movement took place over a longer time course. These data may be helpful to the therapist in developing more effective robot-based therapy protocols.