Computerized visual feedback: an adjunct to robotic-assisted gait training

BACKGROUND AND PURPOSE: Robotic devices for walking rehabilitation allow new possibilities for providing performance-related information to patients during gait training. Based on motor learning principles, augmented feedback during robotic-assisted gait training might improve the rehabilitation process used to regain walking function. This report presents a method to provide visual feedback implemented in a driven gait orthosis (DGO). The purpose of the study was to compare the immediate effect on motor output in subjects during robotic-assisted gait training when they used computerized visual feedback and when they followed verbal instructions of a physical therapist. SUBJECTS: Twelve people with neurological gait disorders due to incomplete spinal cord injury participated. METHODS: Subjects were instructed to walk within the DGO in 2 different conditions. They were asked to increase their motor output by following the instructions of a therapist and by observing visual feedback. In addition, the subjects' opinions about using visual feedback were investigated by a questionnaire. RESULTS: Computerized visual feedback and verbal instructions by the therapist were observed to result in a similar change in motor output in subjects when walking within the DGO. Subjects reported that they were more motivated and concentrated on their movements when using computerized visual feedback compared with when no form of feedback was provided. DISCUSSION AND CONCLUSION: Computerized visual feedback is a valuable adjunct to robotic-assisted gait training. It represents a relevant tool to increase patients' motor output, involvement, and motivation during gait training, similar to verbal instructions by a therapist

Modulation of locomotor activity in complete spinal cord injury

The aim of this study was to evaluate the modulation of muscle activity during locomotor-like movements by different walking speeds in subjects with a motor complete spinal cord injury (SCI) compared to actively- and passively-walking control subjects without neurological deficit. Stepping movements on a treadmill were induced and assisted by a driven gait orthosis. Electromyographic (EMG) muscle activity of one leg (rectus and biceps femoris, tibialis anterior and gastrocnemius) was recorded and analyzed at three stepping velocities with similar body weight support in both subject groups. In SCI subjects, the EMG amplitude of biceps femoris, tibialis anterior and gastrocnemius was in general similar or weaker than in passively- and actively-stepping control subjects, but that of rectus femoris was larger. The degree of co-activation between tibialis anterior and gastrocnemius was higher in SCI than in control subjects. A significant velocity-dependent EMG modulation was present in all four-leg muscles in both subject groups. In SCI subjects, this EMG modulation was similar to that in actively stepping control subjects. It is concluded that in complete spastic SCI subjects, spinal neuronal circuits underlying locomotion can to a large extent adequately respond to a change in external drive to adapt the neuronal pattern to a new locomotion speed. The application of various speeds might enhance the effect of locomotor training in incomplete SCI subject

Assessing walking ability using a robotic gait trainer: opportunities and limitations of assist-as-needed control in spinal cord injury

Background: Walking impairments are a common consequence of neurological disorders and are assessed with clinical scores that suffer from several limitations. Robot-assisted locomotor training is becoming an established clinical practice. Besides training, these devices could be used for assessing walking ability in a controlled environment. Here, we propose an adaptive assist-as-needed (AAN) control for a treadmill-based robotic exoskeleton, the Lokomat, that reduces the support of the device (body weight support and impedance of the robotic joints) based on the ability of the patient to follow a gait pattern displayed on screen. We hypothesize that the converged values of robotic support provide valid and reliable information about individuals' walking ability.Methods: Fifteen participants with spinal cord injury and twelve controls used the AAN software in the Lokomat twice within a week and were assessed using clinical scores (10MWT, TUG). We used a regression method to identify the robotic measure that could provide the most relevant information about walking ability and determined the test–retest reliability. We also checked whether this result could be extrapolated to non-ambulatory and to unimpaired subjects.Results: The AAN controller could be used in patients with different injury severity levels. A linear model based on one variable (robotic knee stiffness at terminal swing) could explain 74% of the variance in the 10MWT and 61% in the TUG in ambulatory patients and showed good relative reliability but poor absolute reliability. Adding the variable 'maximum hip flexor torque' to the model increased the explained variance above 85%. This did not extend to non-ambulatory nor to able-bodied individuals, where variables related to stance phase and to push-off phase seem more relevant.Conclusions: The novel AAN software for the Lokomat can be used to quantify the support required by a patient while performing robotic gait training. The adaptive software might enable more challenging training conditions tuned to the ability of the individuals. While the current implementation is not ready for assessment in clinical practice, we could demonstrate that this approach is safe, and it could be integrated as assist-as-needed training, rather than as assessment.Trial registration: ClinicalTrials.gov Identifier: NCT02425332

A two joint neck model to identify malposition of the head relative to the thorax

This article belongs to the Special Issue Impact of Sensors in Biomechanics, Health Disease and RehabilitationNeck pain is a frequent health complaint. Prolonged protracted malpositions of the head are associated with neck pain and headaches and could be prevented using biofeedback systems. A practical biofeedback system to detect malpositions should be realized with a simple measurement setup. To achieve this, a simple biomechanical model representing head orientation and translation relative to the thorax is introduced. To identify the parameters of this model, anthropometric data were acquired from eight healthy volunteers. In this work we determine (i) the accuracy of the proposed model when the neck length is known, (ii) the dependency of the neck length on the body height, and (iii) the impact of a wrong neck length on the models accuracy. The resulting model is able to describe the motion of the head with a maximum uncertainty of 5 mm only. To achieve this high accuracy the effective neck length must be known a priory. If however, this parameter is assumed to be a linear function of the palpable neck length, the measurement error increases. Still, the resulting accuracy can be sufficient to identify and monitor a protracted malposition of the head relative to the thorax

Influence of virtual reality soccer game on walking performance in robotic assisted gait training for children

BACKGROUND: Virtual reality (VR) offers powerful therapy options within a functional, purposeful and motivating context. Several studies have shown that patients' motivation plays a crucial role in determining therapy outcome. However, few studies have demonstrated the potential of VR in pediatric rehabilitation. Therefore, we developed a VR-based soccer scenario, which provided interactive elements to engage patients during robotic assisted treadmill training (RAGT). The aim of this study was to compare the immediate effect of different supportive conditions (VR versus non-VR conditions) on motor output in patients and healthy control children during training with the driven gait orthosis Lokomat*. METHODS: A total of 18 children (ten patients with different neurological gait disorders, eight healthy controls) took part in this study. They were instructed to walk on the Lokomat in four different, randomly-presented conditions: (1) walk normally without supporting assistance, (2) with therapists' instructions to promote active participation, (3) with VR as a motivating tool to walk actively and (4) with the VR tool combined with therapists' instructions. The Lokomat gait orthosis is equipped with sensors at hip and knee joint to measure man-machine interaction forces. Additionally, subjects' acceptance of the RAGT with VR was assessed using a questionnaire. RESULTS: The mixed ANOVA revealed significant main effects for the factor CONDITIONS (p < 0.001) and a significant interaction CONDITIONS x GROUP (p = 0.01). Tests of between-subjects effects showed no significant main effect for the GROUP (p = 0.592). Active participation in patients and control children increased significantly when supported and motivated either by therapists' instructions or by a VR scenario compared with the baseline measurement "normal walking" (p < 0.001). CONCLUSIONS: The VR scenario used here induces an immediate effect on motor output to a similar degree as the effect resulting from verbal instructions by the therapists. Further research needs to focus on the implementation of interactive design elements, which keep motivation high across and beyond RAGT sessions, especially in pediatric rehabilitation

Standardized voluntary force measurement in a lower extremity rehabilitation robot

BACKGROUND: Isometric force measurements in the lower extremity are widely used in rehabilitation of subjects with neurological movement disorders (NMD) because walking ability has been shown to be related to muscle strength. Therefore muscle strength measurements can be used to monitor and control the effects of training programs. A new method to assess isometric muscle force was implemented in the driven gait orthosis (DGO) Lokomat. To evaluate the capabilities of this new measurement method, inter- and intra-rater reliability were assessed. METHODS: Reliability was assessed in subjects with and without NMD. Subjects were tested twice on the same day by two different therapists to test inter-rater reliability and on two separate days by the same therapist to test intra-rater reliability. RESULTS: Results showed fair to good reliability for the new measurement method to assess isometric muscle force of lower extremities. In subjects without NMD, intraclass correlation coefficients (ICC) for inter-rater reliability ranged from 0.72 to 0.97 and intra-rater reliability from 0.71 to 0.90. In subjects with NMD, ICC ranged from 0.66 to 0.97 for inter-rater and from 0.50 to 0.96 for intra-rater reliability. CONCLUSION: Inter- and intra- rater reliability of an assessment method for measuring maximal voluntary isometric muscle force of lower extremities was demonstrated. We suggest that this method is a valuable tool for documentation and controlling of the rehabilitation process in patients using a DGO

The relative timing between eye and hand rapid sequential pointing is affected by time pressure, but not by advance knowledge

The present study examined the effect of timing constraints and advance knowledge on eye-hand coordination strategy in a sequential pointing task. Participants were required to point at two successively appearing targets on a screen while the inter-stimulus interval (ISI) and the trial order were manipulated, such that timing constraints were high (ISI = 300 ms) or low (ISI = 450 ms) and advance knowledge of the target location was present (fixed order) or absent (random order). Analysis of eye and finger onset and completion times per segment of the sequence indicated that oculo-manual behaviour was in general characterized by eye movements preceding the finger, as well as 'gaze anchoring' (i.e. eye fixation of the first target until completion of the finger movement towards that target). Advance knowledge of future target locations lead to shorter latency times of eye and hand, and smaller eye-hand lead times, which in combination resulted in shorter total movement times. There was, however, no effect of advance knowledge on the duration of gaze anchoring. In contrast, gaze anchoring did change as a function of the interval between successive stimuli and was shorter with a 300 ms ISI versus 450 ms ISI. Further correlation analysis provided some indication that shorter residual latency is associated with shorter pointing duration, without affecting accuracy. These results are consistent with a neural mechanism governing the coupling of eye and arm movements, which has been suggested to reside in the superior colliculus. The temporal coordination resulting from this coupling is a function of the time pressure on the visuo-manual system resulting from the appearance of external stimuli

Advanced Robotic Therapy Integrated Centers (ARTIC): an international collaboration facilitating the application of rehabilitation technologies

Background: The application of rehabilitation robots has grown during the last decade. While meta-analyses have shown beneficial effects of robotic interventions for some patient groups, the evidence is less in others. We established the Advanced Robotic Therapy Integrated Centers (ARTIC) network with the goal of advancing the science and clinical practice of rehabilitation robotics. The investigators hope to exploit variations in practice to learn about current clinical application and outcomes. The aim of this paper is to introduce the ARTIC network to the clinical and research community, present the initial data set and its characteristics and compare the outcome data collected so far with data from prior studies. Methods: ARTIC is a pragmatic observational study of clinical care. The database includes patients with various neurological and gait deficits who used the driven gait orthosis Lokomat® as part of their treatment. Patient characteristics, diagnosis-specific information, and indicators of impairment severity are collected. Core clinical assessments include the 10-Meter Walk Test and the Goal Attainment Scaling. Data from each Lokomat® training session are automatically collected. Results: At time of analysis, the database contained data collected from 595 patients (cerebral palsy: n = 208; stroke: n = 129; spinal cord injury: n = 93; traumatic brain injury: n = 39; and various other diagnoses: n = 126). At onset, average walking speeds were slow. The training intensity increased from the first to the final therapy session and most patients achieved their goals. Conclusions: The characteristics of the patients matched epidemiological data for the target populations. When patient characteristics differed from epidemiological data, this was mainly due to the selection criteria used to assess eligibility for Lokomat® training. While patients included in randomized controlled interventional trials have to fulfill many inclusion and exclusion criteria, the only selection criteria applying to patients in the ARTIC database are those required for use of the Lokomat®. We suggest that the ARTIC network offers an opportunity to investigate the clinical application and effectiveness of rehabilitation technologies for various diagnoses. Due to the standardization of assessments and the use of a common technology, this network could serve as a basis for researchers interested in specific interventional studies expanding beyond the Lokomat®

Assessment of walking performance in robot-assisted gait training: A novel approach based on empirical data

Motivation and voluntary drive of patients can be improved by applying biofeedback during robot-assisted rehabilitation trainings. Biofeedback systems were traditionally based on theoretical assumptions. In this paper, we present a novel approach to calculate biofeedback during robot-assisted gait training. Our method was based on empirical data that were obtained from healthy subjects when simulating distinctive degrees of walking performance during robot-assisted gait training. This empirical data-based biofeedback (EDBF) method was evaluated with 18 subjects without gait disorders. A higher correlation between the subjects’ walking performance and biofeedback values was found for the EDBF method compared to a theory-based biofeedback approac

Adaptive support for patient-cooperative gait rehabilitation with the Lokomat

The rehabilitation robot Lokomat allows automated treadmill training for patients with neurological gait disorders. The basic position control approach for the robot has been extended to patient-cooperative strategies. These strategies provide more freedom and allow patients to actively influence their training. However, patients are likely to need additional support during patient-cooperative training. In this paper, we propose an algorithm based on iterative learning control that shapes a supportive torque field. The torque field is supposed to assist the patient as much as needed in performing the desired task. We evaluated the algorithm in a proof-of-concept experiment with 3 healthy subjects. Results showed that the amount of support was automatically adapted to the activity and the individual needs of the subjects. Furthermore, the support improved the performance of the subjects