A Greedy Assist-as-Needed Controller for Upper Limb Rehabilitation

Luo, Lincong; Peng, Liang; Wang, Chen; Hou, Zeng-Guang

doi:10.1109/tnnls.2019.2892157

Cited by 48 publications

(34 citation statements)

References 39 publications

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“…For example, in Metzger et al (2014), the difficulty of the training task is adjusted based on the completion of the task to maintain the training performance of patients in a certain range. Besides, interaction forces, muscle activity, or other kinematic or physiological parameters have also been used for training challenge adaption (Krebs et al, 2003;Novak et al, 2011;Luo et al, 2019). However, due to the complexity of the training tasks and human-machine systems, the adaptive task adjustment based engagement enhancement methods can hardly find an optimal design of the training tasks, which can be found by the optimization method.…”

Section: Figure 10 |mentioning

confidence: 99%

“…Similarly, in 2014, an intelligent game engine was specifically designed for poststroke rehabilitation, where the game parameters can be adjusted in real time according to patients' performance based on a Bayesian framework (Pirovano et al, 2014). Besides, interaction forces, muscle activity, or other physical or physiological parameters also have been used for training challenge adaption (Krebs et al, 2003;Novak et al, 2011;Luo et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

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Engagement Enhancement Based on Human-in-the-Loop Optimization for Neural Rehabilitation

Wang

Ren

et al. 2020

Front. Neurorobot.

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Enhancing patients' engagement is of great benefit for neural rehabilitation. However, physiological and neurological differences among individuals can cause divergent responses to the same task, and the responses can further change considerably during training; both of these factors make engagement enhancement a challenge. This challenge can be overcome by training task optimization based on subjects' responses. To this end, an engagement enhancement method based on human-in-the-loop optimization is proposed in this paper. Firstly, an interactive speed-tracking riding game is designed as the training task in which four reference speed curves (RSCs) are designed to construct the reference trajectory in each generation. Each RSC is modeled using a piecewise function, which is determined by the starting velocity, transient time, and end velocity. Based on the parameterized model, the difficulty of the training task, which is a key factor affecting the engagement, can be optimized. Then, the objective function is designed with consideration to the tracking accuracy and the surface electromyogram (sEMG)-based muscle activation, and the physical and physiological responses of the subjects can consequently be evaluated simultaneously. Moreover, a covariance matrix adaption evolution strategy, which is relatively tolerant of both measurement noises and human adaptation, is used to generate the optimal parameters of the RSCs periodically. By optimization of the RSCs persistently, the objective function can be maximized, and the subjects' engagement can be enhanced. Finally, the performance of the proposed method is demonstrated by the validation and comparison experiments. The results show that both subjects' sEMG-based motor engagement and electroencephalography based neural engagement can be improved significantly and maintained at a high level.

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Section: Figure 10 |mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Engagement Enhancement Based on Human-in-the-Loop Optimization for Neural Rehabilitation

Wang

Ren

et al. 2020

Front. Neurorobot.

Self Cite

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“…During the replay discussed in the experiment of section 6.2, the patient follows a square path, and if he/she deviates from the specified path, the assistive force returns the patient's hand to the square path, which is "Assist as Needed" therapy (Luo et al, 2019). To implement such therapy with our proposed method, consider a case study with similar participants as section 6.1.…”

Section: Assist As Neededmentioning

confidence: 99%

“…Afterward, in section 5, the decentralized controller based on the intelligence of a multi-agent framework is introduced to solve the problem of time-varying in communication networks while minimizing the number of communication links. Section 6 shows the relevance of the proposed method and the similar existing methods for multilateral teleoperation/telerehabilitation, such as "teach and repeat" and "assist as needed" (Staubli et al, 2009;Babaiasl et al, 2016;Luo et al, 2019). Moreover, it proposes novel schemes for multi-lateral remote rehabilitation systems and experimentally investigates them.…”

Section: Introductionmentioning

confidence: 99%

Multi-Lateral Teleoperation Based on Multi-Agent Framework: Application to Simultaneous Training and Therapy in Telerehabilitation

et al. 2020

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In this paper, a new scheme for multi-lateral remote rehabilitation is proposed. There exist one therapist, one patient, and several trainees, who are participating in the process of telerehabilitation (TR) in this scheme. This kind of strategy helps the therapist to facilitate the neurorehabilitation remotely. Thus, the patients can stay in their homes, resulting in safer and less expensive costs. Meanwhile, several trainees in medical education centers can be trained by participating partially in the rehabilitation process. The trainees participate in a “hands-on” manner; so, they feel like they are rehabilitating the patient directly. For implementing such a scheme, a novel theoretical method is proposed using the power of multi-agent systems (MAS) theory into the multi-lateral teleoperation, based on the self-intelligence in the MAS. In the previous related works, changing the number of participants in the multi-lateral teleoperation tasks required redesigning the controllers; while, in this paper using both of the decentralized control and the self-intelligence of the MAS, avoids the need for redesigning the controller in the proposed structure. Moreover, in this research, uncertainties in the operators' dynamics, as well as time-varying delays in the communication channels, are taken into account. It is shown that the proposed structure has two tuning matrices ( L and D ) that can be used for different scenarios of multi-lateral teleoperation. By choosing proper tuning matrices, many related works about the multi-lateral teleoperation/telerehabilitation process can be implemented. In the final section of the paper, several scenarios were introduced to achieve “Simultaneous Training and Therapy” in TR and are implemented with the proposed structure. The results confirmed the stability and performance of the proposed framework.

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“…Assist-as-needed robotic therapy is currently a driving trend in robot-aided rehabilitation therapy [14] that emphasizes patient active participation [15]. Under the assistive scheme, the patient performs the prescribed task independently whereas the robot should provide assistance to aid the patient only when it is deemed necessary otherwise it withholds the assistance [16]. Some AAN scheme introduces a baseline minimal robotic assistance that is first provided to the patients to assist on the exercise and thereafter the robot decreases the assistance according to the patients' need or movement ability thus adapting assistance to the patient's functional capability [17].…”

Section: Introductionmentioning

confidence: 99%

Assist-as-Needed Robotic Rehabilitation Strategy Based on z-Spline Estimated Functional Ability

2020

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Assist-as-needed (AAN) robotic-rehabilitation therapy is an active area of research which aims to promote neuroplasticity and motor coordination through active participation in functional task. A key component of this strategy is to provide robotic assistance to patients only when needed. To achieve this, accurate estimation of patients' movement/functional ability (FA) is required to evaluate patients' need for robotic assistance and to provide the required amount of assistance, which is still a significant challenge to AAN robotic-rehabilitation therapy. This study proposes an AAN technique based on a new Functional Activity Spline Function (FASF) to estimate patients' FA and to adapt robotic assistance. The FASF is formulated using z-spline curve to estimate patients' movement ability based on the quality-of-movement and the time score of the patient in each functional task. A Linear Quadratic Gaussian Integral (LQGi) torque controller is applied with a FASF-to-torque mapping algorithm to physically provide low-level torque assistance on the elbow/shoulder joints. Fifteen patients were involved in the experimental study which consists of two tasks: (Task1) a pick-and-place task and (Task2) a table-to-mouth reaching task. The results showed that the proposed ANN control strategy has successfully estimated the patients' FA consistently with high repeatability, and able to provide the robotic assistance according to the patients' needs in the task. For different levels of impairment, the average percent-torque assistance across trials relative to the highest possible assistive torque are within the range of 5.43%-24.85% (for the mildly impaired) and 75.14%-97.14% (for the severely impaired) patents in both reaching task consistent with their FA estimation.

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A Greedy Assist-as-Needed Controller for Upper Limb Rehabilitation

Cited by 48 publications

References 39 publications

Engagement Enhancement Based on Human-in-the-Loop Optimization for Neural Rehabilitation

Engagement Enhancement Based on Human-in-the-Loop Optimization for Neural Rehabilitation

Multi-Lateral Teleoperation Based on Multi-Agent Framework: Application to Simultaneous Training and Therapy in Telerehabilitation

Assist-as-Needed Robotic Rehabilitation Strategy Based on z-Spline Estimated Functional Ability

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