Development and increasing acceptance of rehabilitation robots as well as advances in technology allow new forms of therapy for patients with neurological disorders. Robot-assisted gait therapy can increase the training duration and the intensity for the patients while reducing the physical strain for the therapist. Optimal training effects during gait therapy generally depend on appropriate feedback about performance. Compared to manual treadmill therapy, there is a loss of physical interaction between therapist and patient with robotic gait retraining. Thus, it is difficult for the therapist to assess the necessary feedback and instructions. The aim of this study was to define a biofeedback system for a gait training robot and test its usability in subjects without neurological disorders.
To provide an overview of biofeedback and motivation methods applied in gait rehabilitation, previous publications and results from our own research are reviewed. A biofeedback method is presented showing how a rehabilitation robot can assess the patients' performance and deliver augmented feedback. For validation, three subjects without neurological disorders walked in a rehabilitation robot for treadmill training. Several training parameters, such as body weight support and treadmill speed, were varied to assess the robustness of the biofeedback calculation to confounding factors.
The biofeedback values correlated well with the different activity levels of the subjects. Changes in body weight support and treadmill velocity had a minor effect on the biofeedback values. The synchronization of the robot and the treadmill affected the biofeedback values describing the stance phase.
Robot-aided assessment and feedback can extend and improve robot-aided training devices. The presented method estimates the patients' gait performance with the use of the robot's existing sensors, and displays the resulting biofeedback values to the patients and therapists. The therapists can adapt the therapy and give further instructions to the patients. The feedback might help the patients to adapt their movement patterns and to improve their motivation. While it is assumed that these novel methods also improve training efficacy, the proof will only be possible with future in-depth clinical studies.
The aim of this study was to develop a biofeedback system for a gait training robot and test its usability in subjects without neurological disorders.
Feedback and motivation
Apart from its instructional aspect, feedback is also important for motivation. Keeping patients informed about their progress usually translates into greater effort during task practice [chapter 10 of ref. ]. This higher effort, e.g. in terms of enhanced endurance or higher compliance, might help to improve training outcomes. Pursuing and achieving goals usually motivates the subjects. This requires measurements to compare the current status with the desired goal. It is important to know the quantity and quality of the movements performed by the patient.
In neuro-rehabilitation, the neurological disorder can increase the need for artificial feedback. For people with neurological disorders, interpretation of intrinsic feedback could be difficult or incorrect due to impaired somatosensory pathways.
During manual training therapists can estimate the patients' performance in several ways. Apart from visual observation therapists can base this estimation on the amount of external assistance needed to perform the movement correctly. However, because the therapist will usually increase the assistance to maintain a physiological gait pattern when the patient's performance reduces, the patient does not have to walk with maximum effort (see also comments on motivation above). Conversely, many individuals with neurological disorders ambulate independently and might still benefit from training. For these individuals, assistance might be beneficial to achieve higher gait quality and delivers a basis for feedback. In conclusion, the estimation of (maximum) walking capability of the patient might be difficult with this assistance-based method. However, the estimation will reflect the current performance correctly. The feedback of this performance estimation might already be sufficient to enhance the training.
This approach based on required assistance can be translated to rehabilitation robots that are equipped with force sensors. However, the problems described above for the estimation by the therapist basically also apply to robotic implementation. With the most commonly used position-controlled strategies, these force sensors register the amount of robot-generated force assisting the patient to follow the predefined gait pattern. The use of these force or torque signals has an advantage over electromyographic muscle recording or standard videographic gait analysis, because no additional time or equipment is needed. Furthermore, electromyographic recordings register muscle activity. The movement resulting from this activity is usually difficult to identify especially when many muscles act onto the same joint and in dynamic situations like walking. Videographic gait analysis is limited by visual obstruction of the one leg by the other, or the rehabilitation device. Additionally, when position control strategies are applied, the visual gait analysis will mainly identify the underlying predefined trajectory. Therefore, we chose a force-based strategy described below for implementing a biofeedback for a gait rehabilitation robot.
Force-based biofeedback in a rehabilitation robot
One specific strategy presented in this paper is based on a driven gait-orthosis DGO  (Lokomat® Pro Version 4, by Hocoma AG, Volketswil, Switzerland). The DGO is a bilateral robotic orthosis that is used in conjunction with a body-weight support system to control the patient's leg movements in the sagittal plane (Fig. 1). The DGO's hip and knee joints are actuated by linear drives, which are integrated in an exoskeletal structure. A passive foot lifter induces an ankle dorsiflexion during the swing phase. The legs of the patient are moved with highly repeatable predefined hip and knee joint trajectories on the basis of an impedance control strategy . Knee and hip joint torques of the patient are determined from force sensors integrated in the drives of the DGO.
Biofeedback values are calculated for stance and swing phase of the gait cycle as weighted averages of the torques measured in the corresponding joint drives [39,40]. The appropriate selection of the weight functions leads to positive biofeedback values when the patient performs therapeutically desirable activities. Specifically, active hip flexion is required to bring the leg forward during the swing phase, active knee flexion during early swing phase and knee extension during late swing phase. During the stance phase, the most important activity is weight bearing by continuous, almost isometric knee extension, whereas hip extension results from a combination of muscle activity and passive motion of the treadmill. This means that for each joint, except the knee joint during stance phase, a torque pointing against the direction of movement should produce a negative feedback, one pointing parallel to the direction of motion a positive feedback. Mathematically this can be implemented by multiplication of the measured force and a weighting function for each time during the gait cycle. Integration of joint torques weighed according to this principle during phases of the gait cycle delivers values that are comprehensive in summarizing the performance in the specific gait phase and that are more robust against noise than the continuous signal. Similar scaling for all values is obtained by normalization (For the mathematical formula see ). Because weighting functions that are proportional to the angular velocity follow the described principle, the present implementation employs these functions for hip joint during stance phase and knee joint during swing phase, as well as hip joint during swing phase with a slight modification. This modification was implemented because there is some indication for a passive pendulum-like motion of the leg in mid swing . It reduces the importance of this phase by multiplication of the weighting function with an additional smooth function (quenching). In contrast to these three biofeedback calculations, the weighting function for the knee during stance phase was chosen to be constant because it takes the requirement of constant weight bearing better into account. In summary, this biofeedback approach provides four biofeedback values per stride and per leg that become available immediately after each step.
The most complete display shows all 8 values per stride in an array of line graphs (Fig. 2A), each including the history for a modifiable number of recent strides. This allows monitoring every aspect of gait performance that is evaluated by the biofeedback. For supervision, a similar visualization can be displayed on the therapist's monitor. Many patients understand quickly which movement leads to higher biofeedback values after verbal instruction by their therapists. However, recurrently reminding the patients usually improves their performance. Simultaneously, the visualization for the patient can be adapted to emphasize specific gait performance aspects and to avoid information overload for the patient. Specifically, the display should be accessible in the way that the patients are able to perceive the information displayed to them, i.e. large fonts readable while walking. The display should also be intuitive. Otherwise, additional time would be required for learning to understand and use the display and therefore shorten the available training time. Intuitive displays are even more important in neuro-rehabilitation because some patients with neurological disorders who require gait retraining also sustain cognitive deficits (e.g. after traumatic brain injury). Thus, such patients could benefit from a reduction to one value per gait phase and a visually more appealing display, such as a smiley face (Fig. 2B). The biofeedback values are summarized by averaging the values of a subset selected by the therapist. Averaging results in an overall factor that is relatively unbiased. In this way, the therapist can have the patient focus on specific aspects of walking. The possible performance loss in the remaining aspects of walking that are not selected for the feedback should be monitored by the therapists with the help of the complete display on their monitor. When selected, the smiley is continuously displayed on the monitor in front of the patient and updated every step. The shape of the smiley's mouth (an arc of a circle) is determined from the obtained average biofeedback value for the last step as well as threshold and scaling factors set by the therapist. For averages larger than the therapist's setting, the ends of the mouth point upward (smile), for averages below the threshold, the ends of the arc point downwards (frown). The arc lengthens with larger absolute values resulting in a more prominent smile or frown for high and low values respectively. The scaling factor allows the therapist to adjust the sensitivity of the feedback to the functional abilities of the patient. In conclusion, the smiley display allows for a goal-oriented training with feedback, i.e. the patient should focus on specific movements to reach the "goal" of a full smile.
All subjects had previous experience in walking within the DGO. During recording times of 30 seconds, the subjects were instructed to walk in three different ways: (1) Passive: They should not contribute to the movement. (2) Active: They should walk with the same pattern as the DGO. (3) Exaggerated: They should exaggerate their movements in order to increase the biofeedback values that were displayed as line graphs. With the given time and endurance limitations, not all of the 54 possible combinations could be tested in the single session performed. Subject P1 completed 41, subject P2 45 and subject P3 42 trials. The actual joint angles and the joint moments were digitally recorded with a sampling rate of 1 kHz.
For analysis, biofeedback values were re-calculated offline (using Matlab, Mathworks Inc.) from the recorded torques according to the method described above, i.e. as weighted averages of the force values using the described weighting functions. (The analysis would have been possible by selecting strides from the automatically generated biofeedback file. The recalculation was done for convenience and easier automatic analysis). For illustration, the torques and angles were cut into strides and normalized in time to 100 samples per gait cycle. For purposes of correlation with recorded joint torques and biofeedback values using Spearman correlation in Matlab (Mathworks Inc.), the walking instructions were coded as "passive" = 0, "active" = 1, "exaggerated" = 2.
The correlation of the recorded torques at each time of the gait cycle and the four external parameters, instructed activity, patient coefficient, body weight support and treadmill speed were calculated and are shown in Fig. 4 for the right hip and knee of the three subjects. In all three subjects, the correlation of hip joint torque and instructed activity was high (>0.5) during swing phase ranging from about 55% to 100% of the gait cycle. The correlation of hip torque and activity was inconsistent during stance phase, being close to zero for 2 subjects and smaller than -0.5 for one subject. For the knee joint, the correlation of torque and activity was also small during stance phase. During swing phase, the correlation of knee torque and activity was positive during early swing, when the knee is flexing, and negative (<-0.5) during late swing when the knee is extending.
Changing the synchronization of DGO and treadmill influenced the hip and knee joint torques during the stance phase, especially at its end when the correlation coefficients were >0.5 for the hip and <-0.5 for the knee joint. The correlation coefficients of hip and knee torques and treadmill speed were generally close to zero during stance phase and had a consistent biphasic pattern during swing phase. The correlation coefficients of hip and knee torques and the amount of body weight support were generally closer to zero during the whole gait phase with largest values in the hip during the stance phase.
The correlations of the biofeedback values to the amount of body weight support and to the treadmill speed are relatively small. For the body weight support, the absolute values of the correlation coefficients were on average 0.19 with a maximum of 0.38. For treadmill speed, the absolute values were on average 0.14 with a maximum 0.33.
The influence of gait parameters other than the subject's activity on the biofeedback values is therefore minor for values addressing the swing phase. The stance-phase values are strongly influenced by the synchronization of walking cadence and treadmill speed. The calculation of these values will be updated to improve the robustness against disturbances that is important for quantitative analysis. For the use as a biofeedback, however, this effect is less important because for adapting his or her motor activity the patient will concentrate on the last several steps and will take into account changes in the other parameters. Furthermore, the currently used weighting functions originate from basic biomechanical reasoning (as described above) and can be understood as a first-order approximation to robot-assisted walking.
Biofeedback is a necessary addition to robotic gait training. It can provide an online feedback about the patients' performance to the training and allow the patient and the therapist to assess the walking performance. This can help to adapt and improve the training. The subjects might draw additional motivation from the online feedback on their performance.
Furthermore, the assessment of the patients' performance might be used not solely as online feedback, but also for evaluation of the rehabilitation progress. The integration of robot-aided training with robot-aided assessment and feedback has the potential to improve robotic rehabilitation.
DGO Driven gait orthosis
LL is employed by the University of Zurich via a CTI (Commission for Technology and Innovation) project funded by the Swiss Bureau of Education and Technology and Hocoma AG, Volketswil, Switzerland, which produces the Lokomat.
GC is founder, shareholder and CEO of Hocoma AG, Volketswil, Switzerland, which produces the Lokomat. GC is one of the inventors of the Lokomat.
LL conceived and designed the study, recruited subjects, performed the acquisition and analysis of data, and drafted the manuscript. GC and RR provided expert guidance on experimental design, assisted with data interpretation, helped drafting the manuscript and edited the manuscript.
This work is partially supported by Commission for Technology and Innovation (CTI) projects 6199.1-MTS and 7497.1 LSPP-LS and Swiss National Science Foundation NCCR Neuro (project 7), Switzerland.
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