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Effects of control strategies on gait in robot-assisted post-stroke lower limb rehabilitation: a systematic review
Journal of NeuroEngineering and Rehabilitation volume 19, Article number: 52 (2022)
Abstract
Background
Stroke related motor function deficits affect patients' likelihood of returning to professional activities, limit their participation in society and functionality in daily living. Hence, robot-aided gait rehabilitation needs to be fruitful and effective from a motor learning perspective. For this reason, optimal human–robot interaction strategies are necessary to foster neuroplastic shaping during therapy. Therefore, we performed a systematic search on the effects of different control algorithms on quantitative objective gait parameters of post-acute stroke patients.
Methods
We conducted a systematic search on four electronic databases using the Population Intervention Comparison and Outcome format. The heterogeneity of performance assessment, study designs and patients’ numerosity prevented the possibility to conduct a rigorous meta-analysis, thus, the results were presented through narrative synthesis.
Results
A total of 31 studies (out of 1036) met the inclusion criteria, without applying any temporal constraints. No controller preference with respect to gait parameters improvements was found. However, preferred solutions were encountered in the implementation of force control strategies mostly on rigid devices in therapeutic scenarios. Conversely, soft devices, which were all position-controlled, were found to be more commonly used in assistive scenarios. The effect of different controllers on gait could not be evaluated since conspicuous heterogeneity was found for both performance metrics and study designs.
Conclusions
Overall, due to the impossibility of performing a meta-analysis, this systematic review calls for an outcome standardisation in the evaluation of robot-aided gait rehabilitation. This could allow for the comparison of adaptive and human-dependent controllers with conventional ones, identifying the most suitable control strategies for specific pathologic gait patterns. This latter aspect could bolster individualized and personalized choices of control strategies during the therapeutic or assistive path.
Introduction
In the context of the digital revolution, there is a new paradigm for which digitalisation is approached in a sustainable and accessible way. Data is seen as a resource with great potential for the improvement of social and economic problems, as well as the growth of productivity and innovation.
In this framework, robotics has an important role in collecting new patient-specific data and using it to provide support during therapy or daily life assistance, especially when leveraging exoskeletons with embedded Artificial Intelligence (AI) algorithms. Nowadays, AI algorithms are increasing the implementation efficacy of learning processes and are capable of collecting and labelling new data almost instantly. This, viewed through the iron triangle framework of healthcare systems, could bolster accessibility, improving quality while cutting costs [1, 2]. In healthcare, the application of such a new framework could lead to improvements in terms of personalised therapies or innovative treatments. Moreover, in an assistive context, user-tailored devices could promote their accessibility and distribution in daily life, fostering the long-term improvement of the quality of life of patients in chronic conditions.
Stroke
There is already consistent evidence of the beneficial effects of robot-aided treatments of the lower limbs after stroke [3]. Such evidence is paving the way for commercial and research solutions that show positive effects on the recovery of patients during their acute or chronic post-stroke phase [4].
In an attempt to define stroke, in the 70 s, the World Health Organization gave the following definition: “neurological deficit of cerebrovascular cause that persists beyond 24 h or is interrupted by death within 24 h” [5]. Either due to a lack of blood flow (ischemic) or due to bleeding (haemorrhagic), a stroke can have serious consequences on the patient, making it the fifth cause of death and first for long-term disability [6, 7]. On this matter, neuroplastic shaping has been found to be fundamental for improving functional outcomes after a stroke [8, 9]. From the classical work of Wolpert et al., [10] it is known how learning through repetitions speeds up the formation of priors and how including rest periods and spacing rehabilitative sessions improves learning rates and reduces retentions rates [11, 12]. Neurologically, high-dose rehabilitation programs are most likely to induce permanent modifications in neural plasticity [13] and increase cortical excitability [14], even if the exact dose still must be defined according to the stage of the post-stroke recovery [15].
One of the pillar arguments in this field is the Cochrane review from Mehrholz et al. [3], which highlights, for all the previous reasons and many more, the importance of structured evidence for assessing the best conditions to provide the treatment. Indeed, among the key aspects for a beneficial recovery, there is the manner with which the rehabilitation treatment is delivered both in terms of intensity (duration, repetitions and frequency) and modalities [16].
Control strategies
In the case of robot-assisted treatment it is crucial how the physical human–robot interaction is handled [2]; three factors concur to the motion of a combined dynamic system as the patient-exoskeleton: (1) the rigidity of the link (2) the mechanical response of both components and (3) the control laws of the active parts. The rigidity of the devices is inherently connected to the mechanical structure. Indeed, while soft materials intrinsically have varying compliance, rigid ones present a generally constant component of stiffness. On the other hand, the patients’ mechanical responses are supposed to vary during the treatment (improvement on gait phases, higher muscle force, etc.…) or in some cases, within the rehabilitation session (increase in fatigue levels, falls).
Henceforth, the only way to cope with a varying stiffness of the links and with always changing user motion intentions is a well-versatile and adaptive control strategy.
The mechanical structure of robots is highly associated with these aspects, thus, it is important to differentiate between end-effector robots and exoskeletons. More specifically, in end-effector devices, movement is initiated through a unique distal contact point. Then, movement is indirectly transferred to all adjacent joints. Exoskeletons, on the other hand, wear the user and, after proper alignment of the rotation axis of the device and user’s joints, directly provide movement onto the joints.
Given the multiple contact points of the exoskeleton with the human, control strategy design for the physical human–robot interaction is inherently more challenging in the case of exoskeletons, rather than end-effector ones [17]. Also, end-effector robots are known to suffer from a scarce control of the proximal joints in the limb (located between end-effector connection and trunk), which could result in abnormal or even dangerous movement patterns. This results in an inherent advantage of exoskeletons, namely the presence of mechanical endstops concurring in containing joint hyperextension. For this reason and the inherently different underlying control problem, we focussed only on exoskeleton devices, both treadmill-based or leg-orthotics ones.
Strategies have been previously classified in position and force controllers. Position control drives the gait onto a fixed mode, forcing the user to follow a pre-defined or adaptive trajectory, usually with rather low compliance. On the other hand, force control relies on a force signal produced by the limb contraction and its interaction with the mechanical parts of the device. Force or torque sensing devices have high determinacy, making the force control of exoskeleton devices steady and reliable. Conversely, these sensors often require rigid mechanical structures to produce an accurate force estimate, which makes this strategy not very common in modern soft exoskeletons. Position controllers, on the other hand, are strongly influenced by small errors on relative position variations, which may yield significant contact forces, if the interaction stiffness is not too low. Therefore, by adding the knowledge of these forces, the robot task space could be split into two subspaces, as in the Lokomat [18, 19], achieving a higher cooperative robot behaviour with a hybrid force-position control. In the family of force control strategies, we must include impedance control. It aims to control the rigidity and damping between the device and the user, avoiding excessive forces at the interface. Furthermore, specific types of controller modalities have to be highlighted. A bang-bang controller is a feedback controller switching between two different states (also called on–off controller). Assist-as-needed (AAN) on the other hand, provides the minimal amount of robotic assistance required to fulfil the movement trajectory. The latter results in a commonly used strategy, maximizing the effort made by the patient, promoting his/her active participation. Lastly, tunnel or path control allows freedom of movement within a virtual tunnel of adjustable size around the predefined joint trajectory. The latter differs from pure position controllers by enforcing an error margin around the trajectory, increasing safety levels and compliance of the device mechanical response.
In addition, to enhance participation, augment human–robot interaction and promote adaptive neuroplasticity shaping, strategies based on biological signals have been developed (surface electromyogram, sEMG and electroencephalogram, EEG) [20]. sEMG is used to record the surface component of activity produced by the skeletal muscle [21]. It gives a non-invasive measure of human motor activity and, in opposition with the force sensing, it provides information about specific muscle groups' activity and not about the combination of all muscle groups. Furthermore, sEMG, or more in general EMG signals, allow investigating active motion intentions and synergies by evaluating activation timing and intensity of connected muscle groups. For what concerns stroke, EEG-based prosthetic control was not found in an extensive number of applications, due to the high likelihood of a lesion being in the brain motor function area, making it unable to produce regular EEG signals.
Currently, several review papers are available either on an effective comparison between robot-assisted treatment and conventional therapy or the screening of the most used control strategies. However, up to our knowledge, none of them is addressing the association between controllers technical requirements and their expected outcome.
For this reason, we systematically reviewed the control strategies currently used in lower-limb rehabilitation robots for stroke patients, providing a classification of the control strategies and the outcome measures adopted. A comparison of control techniques and mechanical requirements needed in assistive and therapeutic environments is performed. Furthermore, we investigated whether a preferred association exists between the different solutions designs and the outcome measure used to assess treatment benefit. The remaining of the paper is organized as follows. Section 2 describes the methods used for the review. Section 3 reports the results on the included papers, while in Section 4 we discuss the results and the limitations of the study. In Section 5, a brief conclusion is given and future outlooks are summarised.
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