[ARTICLE] Performance-based robotic assistance during rhythmic arm exercises – Full Text



Rhythmic and discrete upper-limb movements are two fundamental motor primitives controlled by different neural pathways, at least partially. After stroke, both primitives can be impaired. Both conventional and robot-assisted therapies mainly train discrete functional movements like reaching and grasping. However, if the movements form two distinct neural and functional primitives, both should be trained to recover the complete motor repertoire. Recent studies show that rhythmic movements tend to be less impaired than discrete ones, so combining both movement types in therapy could support the execution of movements with a higher degree of impairment by movements that are performed more stably.


A new performance-based assistance method was developed to train rhythmic movements with a rehabilitation robot. The algorithm uses the assist-as-needed paradigm by independently assessing and assisting movement features of smoothness, velocity, and amplitude. The method relies on different building blocks: (i) an adaptive oscillator captures the main movement harmonic in state variables, (ii) custom metrics measure the movement performance regarding the three features, and (iii) adaptive forces assist the patient. The patient is encouraged to improve performance regarding these three features with assistance forces computed in parallel to each other. The method was tested with simulated jerky signals and a pilot experiment with two stroke patients, who were instructed to make circular movements with an end-effector robot with assistance during half of the trials.


Simulation data reveal sensitivity of the metrics for assessing the features while limiting interference between them. The assistance’s effectiveness with stroke patients is established since it (i) adapts to the patient’s real-time performance, (ii) improves patient motor performance, and (iii) does not lead the patient to slack. The smoothness assistance was by far the most used by both patients, while it provided no active mechanical work to the patient on average.


Our performance-based assistance method for training rhythmic movements is a viable candidate to complement robot-assisted upper-limb therapies for training a larger motor repertoire.


Rhythmic and discrete movements have recently been recognized as two of the most fundamental units of the upper- [1] and lower-limb [2] motor repertoire. Rhythmic movements capture periodic movements like hammering or scratching, while discrete movements capture movements between a succession of postures with zero velocity and acceleration, like reaching and pointing [3, 4]. These two fundamental motor primitives are controlled by distinct neural circuitries, at least partially [3, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14]. For example, previous research with healthy subjects showed that (i) discrete movements require more cortical activity than rhythmic ones [7], and (ii) no learning transfer occurs from rhythmic to discrete movements and only a little transfer occurs from discrete to rhythmic movements when they are executed in altered visual or haptic conditions [13, 14].

After a stroke, both rhythmic and discrete movements can be impaired [15, 16, 17, 18, 19, 20, 21, 22, 23]. Recently, we compared the performance in executing both movements in the same stroke population. As a main conclusion, we found that rhythmic arm movements are less affected than discrete ones. In particular, stroke preserved the smoothness of rhythmic movements so that fewer submovements were identified than in the discrete counterparts [24]. However, rhythmic movements were impaired compared to healthy subjects. Stroke patients decelerated more than healthy subjects at the movement reversal, and some patients displayed a larger amount of submovements.

If rhythmic and discrete movements are two distinct primitives, they deserve specific and differentiated training to permit the full recovery of the complete motor repertoire. This is a necessary condition to recover autonomy life activities requiring a combination of rhythmic and discrete movements (such as wiping a table or playing the piano [5]).

Most post-stroke therapies tend to focus on functional and thus mainly discrete movements [25, 26, 27], although some previous contributions did focus on upper-limb rhythmic movement training. Interestingly, they all tended to display an improvement in motor skills [28, 29, 30, 31, 32, 33, 34]. For instance, [28, 29] highlighted that the intensity of the training is critical to enhance motor skills. In [33], the authors compared bilateral arm training with auditory cueing (BATRAC) to dose-matched therapeutic exercises and concluded that none was superior to the other, although the adaptations in brain activation were greater after BATRAC. Whether this result is due to the rhythmic nature of the movement, its bimanual nature, the auditory cueing, or a combination of these features, is however difficult to establish, since these are closely intertwined in BATRAC.

The current state-of-art of rhythmic upper-limb movement therapy calls thus for the development of post-stroke therapies tailored to unilateral rhythmic movement training, in order to study their exact effect on motor skills. The development of such a therapy is presented in this paper.

Robotic devices are particularly suited for implementing post-stroke therapies, with a specific focus on movement intensity. Rehabilitation robots enable patients to practice well-specified motor actions and can deliver an appropriate amount of assistance to help patients in improving their motor behavior [17, 35, 36, 37, 38, 39, 40, 41, 42]. Motor performance can be computed in real-time by the robot controller, allowing for continuous adaptation of the type and amount of assistance. The patient only receives the necessary support and is prevented from slacking [36, 43]. In the literature, this is often referred to as the “slacking hypothesis,” which suggests that too much assistance will cause a progressive decrease in patient effort to accomplish a desired task and reduce motor recovery. This assistance principle is also called “assistance as needed” and has progressively emerged as a hallmark of successful robot-assisted therapies [35, 36, 43, 44]. This principle lies also at the core of the present contribution.

Most upper-limb robot-assisted therapies are designed for discrete movement training and implement the assist-as-needed principle through different strategies. One type of strategy delivers assistance proportional to the trajectory error with respect to a predefined trajectory [42, 45, 46, 47, 48, 49, 50]. Another assistance approach relies on dynamical systems and adapts the assistance parameters as a function of the patient performance [45]. Other approaches tune the amount of assistance across sessions as a function of the performance during the preceding session [45, 51]. Another method [47] performs an online adaptation of the amount of support depending on the activity (for a survey, see [35]).

In contrast with these approaches, a rhythmic movement therapy should exploit the cyclic nature of the movement to anticipate the future trajectory based on previous cycles. This can be achieved by using adaptive oscillators [52]. These mathematical tools are particularly suited to track the main features of a typical rhythmic movement (like amplitude and frequency). This continuous assessment allows the robot to constantly seek to improve movement features with the appropriate amount and type of assistance. Moreover, this approach naturally allows for trajectory-free assistance algorithms so that the therapist does not have to specify an arbitrary target trajectory for the patient to follow. The patient receives assistance to improve the impacted movement features, but is left free to produce any rhythmic trajectory.

Our previous work already paved the way in using adaptive oscillators to deliver trajectory-free assistance for upper- [53] and lower-limb [54, 55] rhythmic movements. These contributions focused on movement assistance for healthy subjects, showing evidence of decreases in metabolic consumption when the assistance was switched on. The present study is the first to propose a metric-based assistance method for patients with motor disorders, with emphasis on the potential to assist different rhythmic movement features as a function of the patient needs.

This paper outlines the performance-based assistance method and its mathematical foundations in details. The method was validated with data from simulations and a pilot study with two stroke patients with upper-limb impairments is also reported. The proposed performance-based assistance method can (i) enhance motor-performance, (ii) give appropriate assistance according to patient performance, and (iii) maintain active patient participation in the task so that no slacking effect occurs.


The main interest of the developed method is that it can independently assist different movement features, only if needed. In particular, the method implements parallel strategies to assist the patient in improving performance regarding movement smoothness, velocity, and amplitude. Therefore, the method requires measuring (Fig. 1 a) and quantifying (Fig. 1 b) the amplitude, velocity, and smoothness features of patient movement in real-time in order to assess the corresponding performance. The method must also compute and deliver the appropriate amount of assistance in amplitude, velocity, and/or smoothness as a function of the performance (Fig. 1 c).

Fig. 1 Methodology. Outline of the overall control strategy of the performance-based assistance. First, the movement features are computed by the adaptive oscillator (a) and serve as input to compute the real-time performance in smoothness, velocity, and amplitude (b). These features are then used to compute the gains to tune the level of the assistance forces in smoothness, velocity, and amplitude (c). These three forces are eventually summed up and delivered to the patient

Continue —> Performance-based robotic assistance during rhythmic arm exercises | Journal of NeuroEngineering and Rehabilitation | Full Text




, , , , , , , , ,

  1. Leave a comment

Leave a Reply

Fill in your details below or click an icon to log in:

WordPress.com Logo

You are commenting using your WordPress.com account. Log Out / Change )

Twitter picture

You are commenting using your Twitter account. Log Out / Change )

Facebook photo

You are commenting using your Facebook account. Log Out / Change )

Google+ photo

You are commenting using your Google+ account. Log Out / Change )

Connecting to %s

%d bloggers like this: