Comparison of Muscular Activity and Movement Performance in Robot-Assisted and Freely Performed Exercises.

End-effector-based robotic systems are, in particular, suitable for extending physical therapy in stroke rehabilitation. An adequate therapy and thus the recovery of movement can only be guaranteed if the physiological muscular activation and movement performance are influenced as little as possible by the robot itself. Yet, this relation has not been investigated in the literature. Therefore, 20 healthy subjects performed free and robot-assisted exercises under different control settings supported by an end-effector-based system. The control settings differed concerning changes in the end-effector velocity and the stiffness of the robot joints. During the exercises, data from inertial measurement unit sensors, robot kinematics, and surface electromyography were collected for the upper limbs. The results showed an increase in muscular activity during robot-assisted movements compared to freely performed movements and also differences in movement performance. The change of the control setting influenced the muscular activation, but not the movement performance. The results of the study revealed that the robot could not be regarded as only a passive element. This should be kept in mind in future robotic rehabilitation systems in order to reduce the influences of the robot itself and thus to optimize the therapy.

The relationship between functionality and erector spinae activity in patients with specific low back pain during dynamic and static movements.

BACKGROUND: Alterations in the activity of the lumbar erector spinae (LES) muscles on both sides of the spine have been inconsistently reported in patients with specific low back pain (sLBP) after measuring the muscular activity with surface electromyography (sEMG). It also remains unclear whether these alterations in LES activity can be related to the functional level of patients with sLBP. RESEARCH QUESTION: This study investigated the LES activity in patients with sLBP during activities of daily living (ADL) which included dynamic and static movement tasks. Moreover, the alterations in LES activity were correlated with the first seven questions of the Zurich Claudication Questionnaire (ZCQ-SS). METHODS: Thirty patients with specific LBP and twenty healthy subjects were recruited to perform five ADLs including 'static waist flexion', 'sit to stand',' 30-seconds standing', '6-minutes walking' and 'climbing stairs'. sEMG sensors were mounted on the left and right LES muscles. The integrated EMG (IEMG) was calculated from the preprocessed sEMG data as statistical comparison criteria. RESULTS: LES activity was significantly higher in patients during 'sit to stand',' 30-seconds standing' and 'climbing stairs' and significantly lower during 'static waist flexion' compared to healthy controls. All tasks showed a significant correlation with the ZCQ-SS score except for '6-minutes walking', whereby LES activity and ZCQ-SS score correspondingly increased during 'sit to stand' and 'climbing stairs' and the LES activity decreased with an increasing ZCQ-SS score during 'static waist flexion' and' 30-seconds standing'. SIGNIFICANCE: There was a high correlation between alterations in LES activity and the level of functionality in LBP patients. However, the LES activity showed an opposite behavior during static and dynamic movement tasks. The methodology presented can be a useful tool for quantifying improvements in functionality after rehabilitation processes.

Interpretation of surface EMGs in children with cerebral palsy: An initial study using a fuzzy expert system.

Surface EMG detected simultaneously at different muscles has become an important tool for analysing the gait of children with cerebral palsy (CP), as it offers essential information about muscular coordination. However, the interpretation of surface EMG is a difficult task that assumes extensive knowledge and experience. As such, this noninvasive procedure is not frequently used in the general clinical routine. An Artificial Intelligence (AI) system for interpreting surface EMG signals and the resulting muscular coordination patterns could overcome these limitations. To support such interpretation, an expert system based on fuzzy inference methodology was developed. The knowledge-base of the system implemented 15 rules, from which the fuzzy inference methodology performs a prediction of the effectiveness of the muscular coordination during gait. Our aim was to assess the feasibility and value of such an expert system in clinical applications. Surface EMG signals were recorded from the tibialis anterior, soleus muscle, and gastrocnemius muscles of children with CP to assess muscular coordination patterns of ankle movement during gait. Nineteen children underwent 114 surface EMG measurements. Simultaneously, the gait cycles of each patient were determined using foot switches and videotapes. From the EMG signals, the effectiveness of the ankle movement was predicted by the expert system, and predictions were classified using a three-point ordinal scale. In 91 cases (80%), the clinical findings matched the predictions of the expert system. In 23 cases (20%) the predictions of the expert system differed from the clinical findings with 12 cases revealing worse and 11 cases revealing better results in comparison to the clinical findings. As this study is a first attempt to verify the feasibility and correctness of this expert system, the results are promising. Further study is required to assess the correlation with the kinematic data and to include the whole leg.