Upper extremity function is affected by a variety of neurological conditions. Robotic exoskeletons offer a potential solution for motor restoration. However, their systematic adoption is limited by challenges relative to human intention detection and device control. This position article offers a focused perspective on this topic. That is, on how knowledge gained from the design and implementation of human-machine interfaces (HMIs) for bionic arms can benefit the field of rehabilitation exoskeletons. Three broadly used HMIs in bionic arms are here investigated, including surface electromyography, impedance, and body-powered control. We propose that combinations of these HMIs could push forward upper extremity exoskeleton development. In this context, we provide concrete applicative examples in two selected clinical scenarios, including post-stroke and Duchenne muscular dystrophy individuals. The discussed solutions can open new avenues for the translation of robotic exoskeletons in a large set of clinical settings and enable a class of exoskeleton technologies that could support a broader range of impairment and disease types.@en

This paper presents an overview of admittance control as a method of physical interaction control between machines and humans. We present an admittance controller framework and elaborate control scheme that can be used for controller design and development. Within this framework, we analyze the influence of feed-forward control, post-sensor inertia compensation, force signal filtering, additional phase lead on the motion reference, internal robot flexibility, which also relates to series elastic control, motion loop bandwidth, and the addition of virtual damping on the stability, passivity, and performance of minimal inertia rendering admittance control. We present seven design guidelines for achieving high-performance admittance controlled devices that can render low inertia, while aspiring coupled stability and proper disturbance rejection.

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Robotic devices that are able to manipulate the fingers can support the study of robot-Assisted motor learning. Currently no devices are available that provide a transparent haptic environment and provide a platform to study motor learning. To cut down on costs it is proposed to use remote control (RC) servos with admittance control. In this study five RC servos are tested to evaluate their controller and passive dynamic properties. Frequency and step response are evaluated and passive dynamics are estimated using a model fit. With a fitted frequency response, system stability is evaluated for different human impedances. The high speed servos have lowest passive inertia (2 à 10-4 kgm2) and highest bandwidth (20 Hz). The communication protocol of RC servos causes a delay of more than 5 ms from change in setpoint to change in output. Stability analysis shows that the high speed servos have largest stability regions. Simulations show that reducing the virtual inertia and damping makes the system more susceptible to unstable behavior. At this moment however the passive dynamics of the setup are more transparent than the virtual inertia (1 · 10-3 kgm2) and damping that can be simulated with an admittance controller. A possible cause lies with the communication delay and high gearing present in RC servos.

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In recent years, we have seen the emergence of numerous robotic technologies that focus on assisting individuals during overground gait and balance therapy following neurological injury and diseases. In general, these systems provide patients active body-weight support used for fall protection, to enhance postural stability, and to compensate for bilateral weakness during overground gait and balance training. As a result, such systems allow individuals the ability to practice the types of activities they will need to be competent in before returning to their home and into the community. The ability to walk overground, practice standing up and sitting down, climbing stairs, and other functional tasks are critical components of achieving functional independence yet are often difficult to safely practice for patients with significant levels of impairment. Not only is the patient at risk for injury but so too is the therapist. The integration of robotic technologies into neurorehabilitation can play a critical role in the safe and effective delivery of gait and balance therapy. The focus of this chapter is to present a range of robotic and non-robotic technologies that support overground gait and balance training, discuss the potential advantages and disadvantages of each, and provide a framework for how each may be useful in the clinical setting. Since the area of rehabilitation robotics is quickly expanding with many devices being developed in laboratories around the world, it is not possible for us to detail every technology. Instead, we will highlight a few of the devices and use them for providing a rationale for their usefulness in neurorehabilitation.

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Passive assistive devices that compensate gravity can reduce human effort during transportation of heavy objects. The additional reduction of inertial forces, which are still present during deceleration when using gravity compensation, could further increase movement performance in terms of accuracy and duration. This study investigated whether position dependent damping forces (PDD) around targets could assist during planar reaching movements. The PDD damping coefficient value increased linearly from 0 Ns/m to 200 Ns/m over 18 cm (long PDD) or 9 cm (short PDD). Movement performance of reaching with both PDDs was compared against damping free baseline conditions and against constant damping (40 Ns/m). Using a Fitts' like experiment design 18 subjects performed a series of reaching movements with index of difficulty: 3.5, 4.5 and 5.5 bits, and distances 18, 23 and 28 cm for all conditions. Results show that PPD reduced (compared to baseline and constant damping) movement times by more than 30% and reduced the number of target reentries, i.e. increasing reaching accuracy, by a factor of 4. Results were inconclusive about whether the long or short PDD conditions achieved better task performance, although mean human acceleration forces were higher for the short PDD, hinting at marginally faster movements. Overall, PDD is a useful haptic force to get humans to decrease their reaching movement times while increasing their targeting accuracy.

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