BackgroundTask-specific locomotor training has been promoted to improve walking-related outcome after incomplete spinal cord injury (iSCI). However, there is also evidence that lower extremity strength training might lead to such improvements. The aim of this randomized cross-over pilot study was to compare changes in a broad spectrum of walking-related outcome measures and pain between robot-assisted gait training (RAGT) and strength training in patients with chronic iSCI, who depended on walking assistance. We hypothesized that task-specific locomotor training would result in better improvements compared to strength training.MethodsNine participants with a chronic iSCI were randomized to group 1 or 2. Group 1 received 16 sessions of RAGT (45 min each) within 4 weeks followed by 16 sessions of strength training (45 min each) within 4 weeks. Group 2 received the same interventions in reversed order. Main outcome measures were the 10 m Walk Test (10MWT) at preferred and maximal speed. Furthermore, we assessed several measures such as walking speed under different conditions, balance, strength, and 2 questionnaires that evaluate risk of falling and pain. Data were collected at baseline, between interventions after 4 weeks, directly after the interventions and at follow-up 6 months after the interventions. Pain was assessed repeatedly throughout the study.ResultsThere were no significant differences in changes in scores between the 2 interventions, except for maximal walking speed (10MWT), which improved significantly more after strength training than after RAGT. Pain reduced after both interventions.ConclusionIn patients with chronic iSCI dependent on walking assistance, RAGT was not more effective in improving walking-related outcome compared to lower extremity strength training. However, the low sample size limits generalizability and precision of data interpretation.Trial registrationThis study was registered at Clinicaltrials.gov (NCT01087918).

Even though research in the field of functional near-infrared spectroscopy (fNIRS) has been performed for more than 20 years, consensus on signal processing methods is still lacking. A significant knowledge gap exists between established researchers and those entering the field. One major issue regularly observed in publications from researchers new to the field is the failure to consider possible signal contamination by hemodynamic changes unrelated to neurovascular coupling (i.e., scalp blood flow and systemic blood flow). This might be due to the fact that these researchers use the signal processing methods provided by the manufacturers of their measurement device without an advanced understanding of the performed steps. The aim of the present study was to investigate how different signal processing approaches (including and excluding approaches that partially correct for the possible signal contamination) affect the results of a typical functional neuroimaging study performed with fNIRS. In particular, we evaluated one standard signal processing method provided by a commercial company and compared it to three customized approaches. We thereby investigated the influence of the chosen method on the statistical outcome of a clinical data set (task-evoked motor cortex activity). No short-channels were used in the present study and therefore two types of multi-channel corrections based on multiple long-channels were applied. The choice of the signal processing method had a considerable influence on the outcome of the study. While methods that ignored the contamination of the fNIRS signals by task-evoked physiological noise yielded several significant hemodynamic responses over the whole head, the statistical significance of these findings disappeared when accounting for part of the contamination using a multi-channel regression. We conclude that adopting signal processing methods that correct for physiological confounding effects might yield more realistic results in cases where multi-distance measurements are not possible. Furthermore, we recommend using manufacturers’ standard signal processing methods only in case the user has an advanced understanding of every signal processing step performed.

BackgroundRobot-assisted gait therapy is increasingly being used in pediatric neurorehabilitation to complement conventional physical therapy. The robotic device applied in this study, the Lokomat (Hocoma AG, Switzerland), uses a position control mode (Guidance Force), where exact positions of the knee and hip joints throughout the gait cycle are stipulated. Such a mode has two disadvantages: Movement variability is restricted, and patients tend to walk passively. Kinematic variability and active participation, however, are crucial for motor learning. Recently, two new control modes were introduced. The Path Control mode allows the patient to walk within a virtual tunnel surrounding the ideal movement trajectory. The FreeD was developed to support weight shifting through mediolaterally moveable pelvis and leg cuffs. The aims of this study were twofold: 1) To present an overview of the currently available control modes of the Lokomat. 2) To evaluate if an increase in kinematic variability as provided by the new control modes influenced leg muscle activation patterns and intensity, as well as heart rate while walking in the Lokomat.MethodsIn 15 adolescents with neurological gait disorders who walked in the Lokomat, 3 conditions were compared: Guidance Force, Path Control, and FreeD. We analyzed surface electromyographic (sEMG) activity from 5 leg muscles of the more affected leg and heart rate. Muscle activation patterns were compared with norm curves.ResultsSeveral muscles, as well as heart rate, demonstrated tendencies towards a higher activation during conditions with more kinematic freedom. sEMG activation patterns of the M.rectus femoris and M.vastus medialis showed the highest similarity to over-ground walking under Path Control, whereas walking under FreeD led to unphysiological muscle activation in the tested sample.ConclusionsResults indicate that especially Path Control seems promising for adolescent patients undergoing neurorehabilitation, as it increases proximal leg muscle activity while facilitating a physiological muscle activation. Therefore, this may be a solution to increase kinematic variability and patients’ active participation in robot-assisted gait training.Electronic supplementary materialThe online version of this article (doi:10.1186/s12984-017-0287-1) contains supplementary material, which is available to authorized users.

BackgroundRehabilitation services use outcome measures to track motor performance of their patients over time. State-of-the-art approaches use mainly patients’ feedback and experts’ observations for this purpose. We aim at continuously monitoring children in daily life and assessing normal activities to close the gap between movements done as instructed by caregivers and natural movements during daily life. To investigate the applicability of body-worn sensors for motor assessment in children, we investigated changes in movement capacity during defined motor tasks longitudinally.MethodsWe performed a longitudinal study over four weeks with 4 children (2 girls; 2 diagnosed with Cerebral Palsy and 2 with stroke, on average 10.5 years old) undergoing rehabilitation. Every week, the children performed 10 predefined motor tasks. Capacity in terms of quality and quantity was assessed by experts and movement was monitored using 10 ETH Orientation Sensors (ETHOS), a small and unobtrusive inertial measurement unit. Features such as smoothness of movement were calculated from the sensor data and a regression was used to estimate the capacity from the features and their relation to clinical data. Therefore, the target and features were normalized to range from 0 to 1.ResultsWe achieved a mean RMS-error of 0.15 and a mean correlation value of 0.86 (p<0.05 for all tasks) between our regression estimate of motor task capacity and experts’ ratings across all tasks. We identified the most important features and were able to reduce the sensor setup from 10 to 3 sensors. We investigated features that provided a good estimate of the motor capacity independently of the task performed, e.g. smoothness of the movement.ConclusionsWe found that children’s task capacity can be assessed from wearable sensors and that some of the calculated features provide a good estimate of movement capacity over different tasks. This indicates the potential of using the sensors in daily life, when little or no information on the task performed is available. For the assessment, the use of three sensors on both wrists and the hip suffices. With the developed algorithms, we plan to assess children’s motor performance in daily life with a follow-up study.