To enable the broad adoption of wearable robotic exoskeletons in medical and industrial settings, it is crucial they can adaptively support large repertoires of movements. We propose a new human-machine interface to simultaneously drive bilateral ankle exoskeletons during a range of “unseen” walking conditions and transitions that were not used for establishing the control interface. The proposed approach used person-specific neuromechanical models to estimate biological ankle joint torques in real-time from measured electromyograms (EMGS) and joint angles. We call this “neuromechanical model-based control” (NMBC). NMBC enabled six individuals to voluntarily control a bilateral ankle exoskeleton across six walking conditions, including all intermediate transitions, i.e., two walking speeds, each performed at three ground elevations. A single subject case-study was carried out on a dexterous locomotion tasks involving moonwalking. NMBC always enabled reducing biological ankle torques, as well as eight ankle muscle EMGs both within (22% torque;12% EMG) and between walking conditions (24% torque; 14% EMG) when compared to non-assisted conditions. Torque and EMG reductions in novel walking conditions indicated that the exoskeleton operated symbiotically, as an exomuscle controlled by the operator.s neuromuscular system. This opens new avenues for the systematic adoption of wearable robots as part of out-of-the-lab medical and occupational settings.

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.

Various powered wearable lower limb exoskeletons are designed for paraplegics to make them walk again. Control methods are developed and implemented in these exoskeletons to provide active gait assistance in the sagittal plane while active control in the frontal plane is still missing. This paper proposed a control method that provided gait assistance in both lateral and sagittal plane. First, in the lateral plane, the exoskeleton was controlled to support the weight shift during stepping by providing assisting hip ab/adduction torques when the subject initiated a small amount of weight shift to the stance side to trigger a step. Second, the exoskeleton's hip ab/adduction during stepping was controlled to improve lateral stability. This was achieved by altering the amount of hip ab/adduction to change step width at heel strike. Using these controls, an able-bodied subject could walk in the exoskeleton without any external balance aids, i.e. crutches or a walker, where his hip and knee joints were controlled by the exoskeleton and his ankle joints were constrained by the exoskeleton. The next step is to test whether the proposed method improves balance in spinal cord injured subjects.

Inferring temporal derivatives (like velocity and acceleration) from a noisy position signal is a well-known challenge in control engineering, due to the intrinsic trade-off between noise filtering and estimation bandwidth. To tackle this problem, in this paper we propose a new approach specifically designed for periodic movements. This approach uses an adaptive oscillator as fundamental building block. It is a tool capable of synchronizing to a periodic input while learning its features (frequency, amplitude, ...) in dedicated state variables. Since the oscillator's input and output are perfectly synchronized during steady-state regime, a non-delayed estimate of the input temporal derivatives can be obtained simply by deriving the output analytical form. Pending a (quasi-)periodic input signal, these temporal derivatives are thus synchronized with the actual kinematics, while the signal bandwidth can be arbitrarily tuned by the intrinsic dynamics of the oscillator. We further validate this approach by developing an impedance-based strategy for assisting human walking in the LOPES lower-limb exoskeleton. Preliminary results with a single participant give rise to three main conclusions. First, our method indeed provides velocity and acceleration estimates of the participant's joint kinematics which are smoother and less delayed with respect to the actual kinematics than using a standard Kalman filter. Second, closing the human-robot loop with a high-gain impedance field depending on the acceleration is not possible with a Kalman filter approach, due to unstable dynamics. In contrast, our approach tolerates high gains (up to 70% of the nominal walking torque), showing its intrinsic stability. Finally, no clear benefit of the acceleration-dependent field with respect to a simpler position-dependent field is visible regarding the reduction of metabolic cost. This last result illustrates the challenge of designing sound assistive strategies for complex tasks like walking.

To automatically align exoskeleton axes to human anatomical axes, we propose to decouple the joint rotations from the joint translations. Decoupling can reduce setup times and painful misalignment forces, at the cost of increased mechanical complexity and movement inertia. The decoupling approach was applied to the Dampace and Limpact exoskeletons.

Due to aging of the population, growing numbers of people are affected by impairments of their motor system, caused by disorders like stroke. Treatment of stroke patients is very intensive in the amount of training per patient per day. The treatment duration per patient combined with the total number of people suffering from a stroke makes rehabilitation therapy extremely costly. This makes devices that could reduce the costs of therapy attractive to rehabilitation centres. Currently there are robotic rehabilitation devices under development for both the upper and lower extremities. Our group focuses mainly on the recovery of gait after stroke. The LOPES robot is designed for use in training on a treadmill to accommodate for limited space of rehabilitation centres and easy access for the therapist to the patient. As a 'robotic therapist' it is meant to make rehabilitation more effective for patients and less demanding for therapists.

The principle of series elastic actuation offers considerable advantages for haptic displays compared to stiff actuators. The interaction force between motor and load is directly proportional to their relative position, which corresponds to the elongation of the elastic element. This way, the torque control task is transformed to a position control task, which comes natural to traditional DC motors. In this paper, several existing control strategies are analyzed and compared with respect to passivity concerns. Cascaded control with a fast inner velocity loop results to be the best option. Based on the analysis, boundaries for the parameters are presented, such that the force controller may contain integral action without jeopardizing passivity.