Bram Koopman

Oscillator-based assistance of cyclical movements: model-based and model-free approaches.

In this article, we propose a new method for providing assistance during cyclical movements. This method is trajectory-free, in the sense that it provides user assistance irrespective of the performed movement, and requires no other sensing than the assisting robot’s own encoders. The approach is based on adaptive oscillators, i.e., mathematical tools that are capable of learning the high level features (frequency, envelope, etc.) of a periodic input signal. Here we present two experiments that we recently conducted to validate our approach: a simple sinusoidal movement of the elbow, that we designed as a proof-of-concept, and a walking experiment. In both cases, we collected evidence illustrating that our approach indeed assisted healthy subjects during movement execution. Owing to the intrinsic periodicity of daily life movements involving the lower-limbs, we postulate that our approach holds promise for the design of innovative rehabilitation and assistance protocols for the lower-limb, requiring little to no user-specific calibration.

Sensing Pressure Distribution on a Lower-Limb Exoskeleton Physical Human-Machine Interface

A sensory apparatus to monitor pressure distribution on the physical human-robot interface of lower-limb exoskeletons is presented. We propose a distributed measure of the interaction pressure over the whole contact area between the user and the machine as an alternative measurement method of human-robot interaction. To obtain this measure, an array of newly-developed soft silicone pressure sensors is inserted between the limb and the mechanical interface that connects the robot to the user, in direct contact with the wearer’s skin. Compared to state-of-the-art measures, the advantage of this approach is that it allows for a distributed measure of the interaction pressure, which could be useful for the assessment of safety and comfort of human-robot interaction. This paper presents the new sensor and its characterization, and the development of an interaction measurement apparatus, which is applied to a lower-limb rehabilitation robot. The system is calibrated, and an example its use during a prototypical gait training task is presented.

Oscillator-based walking assistance: A model-free approach

In this paper, we further develop our framework to design new assistance and rehabilitation protocols based on motor primitives. In particular, we extend our recent results of oscillator-based assistance to the case of walking. The adaptive oscillator used in this paper is capable of predicting the angular position of the users joints in the future, based on the pattern learned during preceding cycles. Assistance is then provided by attracting the joints to this future position using a force field in a compliant lower-limb exoskeleton. To demonstrate the method efficiency, we computed the rate of metabolic energy expended by the participants during a walking task, with and without assistance. Results show a significant decrease of energy expenditure with the assistance switched on, although not to a point to entirely compensate for the burden due to the exoskeleton lack of transparency. The results further show changes in the kinematics: with assistance, the participants walked with a faster cadence and ampler movements. These results tend to prove the relevance of designing assistance protocols based on adaptive oscillators (or primitives in general) and pave the way to the design of new rehabilitation protocols.

Selective control of gait subtasks in robotic gait training: foot clearance support in stroke survivors with a powered exoskeleton

BackgroundRobot-aided gait training is an emerging clinical tool for gait rehabilitation of neurological patients. This paper deals with a novel method of offering gait assistance, using an impedance controlled exoskeleton (LOPES). The provided assistance is based on a recent finding that, in the control of walking, different modules can be discerned that are associated with different subtasks. In this study, a Virtual Model Controller (VMC) for supporting one of these subtasks, namely the foot clearance, is presented and evaluated.MethodsThe developed VMC provides virtual support at the ankle, to increase foot clearance. Therefore, we first developed a new method to derive reference trajectories of the ankle position. These trajectories consist of splines between key events, which are dependent on walking speed and body height. Subsequently, the VMC was evaluated in twelve healthy subjects and six chronic stroke survivors. The impedance levels, of the support, were altered between trials to investigate whether the controller allowed gradual and selective support. Additionally, an adaptive algorithm was tested, that automatically shaped the amount of support to the subjects’ needs. Catch trials were introduced to determine whether the subjects tended to rely on the support. We also assessed the additional value of providing visual feedback.ResultsWith the VMC, the step height could be selectively and gradually influenced. The adaptive algorithm clearly shaped the support level to the specific needs of every stroke survivor. The provided support did not result in reliance on the support for both groups. All healthy subjects and most patients were able to utilize the visual feedback to increase their active participation.ConclusionThe presented approach can provide selective control on one of the essential subtasks of walking. This module is the first in a set of modules to control all subtasks. This enables the therapist to focus the support on the subtasks that are impaired, and leave the other subtasks up to the patient, encouraging him to participate more actively in the training. Additionally, the speed-dependent reference patterns provide the therapist with the tools to easily adapt the treadmill speed to the capabilities and progress of the patient.

Soft artificial tactile sensors for the measurement of human-robot interaction in the rehabilitation of the lower limb

A new and alternative method to measure the interaction force between the user and a lower-limb gait rehabilitation exoskeleton is presented. Instead of using a load cell to measure the resulting interaction force, we propose a distributed measure of the normal interaction pressure over the whole contact area between the user and the machine. To obtain this measurement, a soft silicone tactile sensor is inserted between the limb and commonly used connection cuffs. The advantage of this approach is that it allows for a distributed measure of the interaction pressure, which could be useful for rehabilitation therapy assessment purposes, or for control. Moreover, the proposed solution does not change the comfort of the interaction; can be applied to connection cuffs of different shapes and sizes; and can be manufactured at a low cost. Preliminary results during gait assistance tasks show that this approach can precisely detect changes in the pressure distribution during a gait cycle.

Selective and adaptive robotic support of foot clearance for training stroke survivors with stiff knee gait

Interactive control schemes are rapidly gaining popularity in the control of robotic gait trainers. Interactive control allows for the modification of the support level based on the patients performance. However, only few algorithms exist that adapt the support to the patients needs. The aim of this study was to assess the feasibility of an adaptive and selective method to support a specific subtask of walking. In this study we focused on providing assistance during foot clearance and analyzed the effects in four chronic stroke survivors whose gait is characterized as stiff knee gait. We recently introduced a method to selectively support the foot clearance by defining a virtual spring between the desired and the actual ankle height. Here, this method was extended with an algorithm that automatically adapts the stiffness of the virtual spring, and consequently, adapts the amount of support to the experienced movement error in the previous steps. The results showed that the stiffness profile converged to a subject specific pattern that varied over the gait cycle and was according to the subjects requirements. The proposed algorithm was used in a training study that specifically aimed at increasing the foot clearance. Preliminary results demonstrated that the training resulted in improved foot clearance, which was accompanied by an increased walking speed. This proposed algorithm reduces the need for the therapist/operator to set the amount of support on a trial and error basis and decreases the chances of reliance on the robotic support.

Body weight support by virtual model control of an impedance controlled exoskeleton (LOPES) for gait training.

The feasibility of an alternative method to support body weight in a powered exoskeleton is demonstrated. Instead of using an overhead suspension system, body weight is supported by augmenting the joint moments through virtual model control. The advantages of this novel method is that it allows for independent support of the left and right leg, and does not interfere with the excitation of cutanous afferents and balance of the body or trunk. Results show that after a short familiarization period the activity of muscles during initial stance reduces and kinematics become close to normal.

Rendering potential wearable robot designs with the LOPES gait trainer

In recent years, wearable robots (WRs) for rehabilitation, personal assistance, or human augmentation are gaining increasing interest. To make these devices more energy efficient, radical changes to the mechanical structure of the device are being considered. However, it remains very difficult to predict how people will respond to, and interact with, WRs that differ in terms of mechanical design. Users may adjust their gait pattern in response to the mechanical restrictions or properties of the device. The goal of this pilot study is to show the feasibility of rendering the mechanical properties of different potential WR designs using the robotic gait training device LOPES. This paper describes a new method that selectively cancels the dynamics of LOPES itself and adds the dynamics of the rendered WR using two parallel inverse models. Adaptive frequency oscillators were used to get estimates of the joint position, velocity, and acceleration. Using the inverse models, different WR designs can be evaluated, eliminating the need to build several prototypes. As a proof of principle, we simulated the effect of a very simple WR that consisted of a mass attached to the ankles. Preliminary results show that we are partially able to cancel the dynamics of LOPES. Additionally, the simulation of the mass showed an increase in muscle activity but not in the same level as during the control, where subjects actually carried the mass. In conclusion, the results in this paper suggest that LOPES can be used to render different WRs. In addition, it is very likely that the results can be further optimized when more effort is put in retrieving proper estimations for the velocity and acceleration, which are required for the inverse models.

Estimation of Human Hip and Knee Multi-Joint Dynamics Using the LOPES Gait Trainer

In this study, we present and evaluate a novel method to estimate multi-joint leg impedance, using a robotic gait training device. The method is based on multi-input-multi-output system identification techniques and is designed for continuous torque perturbations at the hip and knee joint simultaneously. Eight elderly subjects (age 67-82) performed relax and position tasks in three different leg orientations. Multi-joint impedance was estimated nonparametrically and was subsequently modeled in terms of inertia and (inter)joint stiffness and damping. The results indicate that all stiffness and damping parameters were significantly higher during the position task compared to the relax task. The majority of the stiffness and damping parameters were not significantly affected by leg orientation. The results also emphasize the importance of considering the visco-elastic coupling between joints when modeling multi-joint dynamics. Measuring joint stiffness with the same device that is used for robotic gait training allows convenient testing of joint properties in conjunction with the robotic gait training protocol. These measures might serve as a good basis for quantitative assessment and follow up of patients with abnormal joint stiffness due to neurological disorders, and may reveal how changes in these joint properties affect their gait function.

Effectiveness of the LOwer Extremity Powered ExoSkeleton (LOPES) Robotic Gait Trainer on Ability and Quality of Walking in SCI Patients

Robotic gait trainers are promising tools to provide intensive, repetitive and goal oriented training to neurological patients without placing high physical demands on therapists. The first generation robotic devices typically enforce a specific walking pattern upon the patient. Recently, “assist-as-needed” algorithms have been introduced to control the robotic gait trainers. These algorithms promote active participation of the patients by applying assistive forces, where and when subjects need it. The purpose of this study was to assess the effectiveness of robot aided gait training using an “assistas- needed” algorithm for hip flexion on the ability and quality of walking in spinal cord injured patients. Twelve motor-incomplete SCI patients followed an eight week gait training program using the LOPES. Before and after the training period clinical tests were assessed and 3D kinematics during normal overground walking was measured. A statistical significant improvement in walking ability, total hip range of motion and most spatiotemporal parameters was found after training. These results indicate that training in the LOPES, using the “assist-as-needed” algorithm for hip flexion, improves the ability and quality of walking in the selected SCI patients.