A three-stage trajectory generation method for robot-assisted bilateral upper limb training with subject-specific adaptation

https://doi.org/10.1016/j.robot.2018.03.010Get rights and content

Highlights

  • A three-stage trajectory generation method for robotic bilateral arm training.

  • The method is able to be subject-specific based on the height of users.

  • Seven participants gave positive feedback on this new technique.

Abstract

Robot-assisted bilateral upper limb training typically requires two robotic devices that work cooperatively with a human user. The determination of appropriate movement trajectories is essential to avoid interference of the robotic systems and ensure an appropriate intersecting workspace with user limbs. This paper proposes a new three-stage trajectory generation method for bilateral upper limb training using interference analysis. These three stages include workspace analysis of robots and human hands, trajectory generation within the intersecting workspace, and interference analysis for training safety verification. This trajectory generation method is also implemented with subject-specific adaptation based on anthropometry of an individual. Experiments were conducted on seven healthy subjects with a variety of body sizes. All participants gave positive feedback on the suitability of the predefined training trajectories, and no robot interference was detected. This three-stage method provides guidelines for the standardization of robot-assisted bilateral upper limb training protocols. Future work will focus on proposing an adaptive trajectory generation algorithm with efficacy evaluation on a larger sample of subjects.

Introduction

Stroke is the second leading cause for acquired disability in adults [[1], [2]]. These survivors are mostly left with disabilities, with the most common being motor impairment of upper limbs [3], which represents difficulties in performing activities of daily living (ADLs). Longitudinal studies indicated that a range from 30% to 66% of stroke survivors do not have full arm function six months post-stroke [4]. However, evidences have suggested that these survivors’ upper limb motor skills can be improved by following rehabilitation interventions [[5], [6], [7]]. While a gold standard for treating mild post-stroke upper limb impairment is constraint-induced movement therapy [4], alternative treatments are needed for targeting impaired subjects with varying levels of disability.

Bilateral upper limb training has been widely investigated as a rehabilitation intervention with great potential for clinical applications [[8], [9], [10]]. Activating the primary motor cortex and supplementary motor area of the intact limb can increase the likelihood of voluntary muscle contractions of the impaired limb when symmetrical movements are executed [11]. Studies also indicated that task-oriented repetitive training therapy plays a positive role on the improvement of movement abilities [[12], [13]]. ADLs-based tasks mostly require bilateral cooperation of human limbs.

Robot-assisted upper limb rehabilitation techniques have advanced rapidly in the past few decades [[14], [15], [16], [17]]. With respect to traditional rehabilitation interventions, robotic systems can provide more intensive physical therapy with implementation of various interactive strategies. Stoykov and Corcos [10] found that most bilateral studies involved the use of a device which provides varying levels of assistance to the paretic arm so that it can perform symmetrical or asymmetrical movements more efficiently. The device can be a mechanical one or a computer controlled robot. These robotic systems allow practice with varying levels of assistance including active, passive and active assisted movement modes, and these strategies can be adjusted adaptively on a computer depending on the disability level of an individual.

Robot-assisted bilateral upper limb training aims to stimulate coordinated use of human arms for rehabilitation purpose. A common way is based on master–slave strategy by healthy limbs guiding impaired ones for specific tasks. Li, et al. [18] proposed a master–slave control method on a robot to implement bilateral arm training. The corresponding force provided for the impaired limb is from the healthy limb. In a similar way, Rashedi, et al. [19] implemented the master–slave control on a hand robot to achieve the mirror-image motion pattern. To achieve patient cooperative control, Trlep, et al. [20] developed an adaptive assistance strategy on a bilateral robotic system to adjust the contribution of the unaffected arm for reducing the load on the paretic arm. Trials on four chronic stroke patients showed that participants were all able to apply forces with the paretic arm similar to the forces of the unaffected arm. Trlep, et al. [21] further applied the scaled forces of both arms to produce the control force on the robotic device.

These bilateral robotic devices can be divided into two categories in terms of motion path. One kind is the robots with fixed motion paths, such as the Reha-Slide Duo [22] and a force-induced isokinetic arm robotic trainer [23]. The Reha-Slide Duo consists of a board with two sledges running on parallel tracks. Tung, et al. [23] constructed the bilateral robotic trainer by using two motors and two parallel roller guides. This device was implemented with four different modes, including bilateral passive, bilateral active passive, bilateral reciprocal, and bilateral symmetric upper limb movement. For a specific joint of the human upper limb, Rashedi, et al. [24] developed a hand robotic rehabilitation system to deliver bilateral training of forearm pronation–supination and wrist flexion–extension. In general, these robotic prototypes are characterized with fixed motion paths by setting interference-free movement tracks mechanically.

The other kind of bilateral robots enables continuous planar [25] or spatial [26] workspace for human upper limbs. The training trajectories with these robotic systems need to be programmed on a computer. Miao, et al. [25] implemented bilateral upper limb training by the use of an H-Bot mechanism, where the movement trajectory can be arbitrarily defined inside a plane by programming, not just follow mechanical tracks as those in [[22], [23]]. The motion module of this robotic system consists of two mutually perpendicular linear slide systems and two Maxon motors for actuation. Another example of bilateral upper limb training is the two-arm exoskeleton robot developed by Perry, et al. [26]. This system can provide three-dimensional continuous workspace, with each arm having seven single-axis revolute joints (shoulder abduction–adduction, flexion–extension and internal–external rotation, elbow flexion–extension, and wrist pronation–supination, flexion–extension, and radio-ulnar deviation). While these robotic systems can deliver physical therapy training with customized trajectories, this requires the generation of appropriate movement paths to guarantee safety and efficacy, especially for robotic systems with continuous spacial workspace capacity. However, no studies have been found specifically investigating trajectory generation techniques for robot-assisted bilateral upper limb training.

The determination of appropriate robot-assisted bilateral training trajectories requires the knowledge of many factors, including human arm workspace, robot workspace, and interference analysis of robot links. This paper proposes a new three-stage trajectory generation method, being the first attempt to standardize trajectory generation techniques for robot-assisted bilateral upper limb training. This study uses two universal robots as the robot-assisted bilateral upper limb rehabilitation system.

Section snippets

Methods

This section presents a detailed description of the proposed three-stage trajectory generation method for robot-assisted bilateral upper limb training. It consists of three processes, where Stage 1 conducts workspace analysis of the robotic system and human arms, and works out their intersection, Stage 2 is to determine training trajectories for both human hands, and the trajectory evaluation is finally conducted in Stage 3 based on interference analysis among robot links.

This robotic system

Experimental results

The robotic system is presented in Fig. 7 with a healthy human subject (29 years old, height 1.75 m, and weight 90 kg) sitting on a height-fixed chair of 0.58 m. To connect human hands with the robotic system, two handles are rigidly installed on the robot end effectors. The handle is designed with two free motions to ensure comfortable training. The part A is rigidly connected to the robot, and the remaining portion of the handle can rotate about the dotted line in black. The part B can rotate

Discussion

Robot-assisted bilateral upper limb training is an emerging form of stroke rehabilitation. A commonly-used method to achieve bilateral training is based on master–slave control by healthy limbs guiding impaired limbs for specific tasks. However, this kind of training strategy may not be safe for the impaired limb when controlled to follow the healthy side since an impaired limb has less workspace compared to that of a healthy limb [[31], [32]].

In terms of the movement path for bilateral

Conclusion

This paper proposes a new three-stage trajectory generation method for robot-assisted bilateral upper limb training. This strategy involves workspace analysis of robots and human arms, trajectory generation within their intersection, and interference analysis for training safety verification. It is also implemented with subject-specific adaptation based on anthropometry of an individual. To the authors’ knowledge, this is the first attempt in determining appropriate trajectories for

Qing Miao was born in Hubei of China in 1988. He received the M.E. in Control Science and Control Engineering from Wuhan University of Technology, Hubei, Wuhan, China, in 2014. He is the Ph.D. student in Mechanical and Electrical Engineering from Wuhan University of Technology, Wuhan, China, at present. Since November of 2016, he has been researching as an exchanging Ph.D. student in the Department of Mechanical Engineering at University of Auckland. His research includes medical, intelligent

References (35)

  • TaubE.

    Technique to improve chronic motor deficit after stroke)

    Arch. Phys. Med. Rehabil.

    (1993)
  • WhitallJ. et al.

    Repetitive bilateral arm training with rhythmic auditory cueing improves motor function in chronic hemiparetic stroke

    Stroke

    (2000)
  • RenY. et al.

    Developing a multi-joint upper limb exoskeleton robot for diagnosis, therapy, and outcome evaluation in neurorehabilitation

    IEEE Trans. Neural Syst. Rehabil. Eng.

    (2013)
  • McCombe WallerS. et al.

    Bilateral arm training: why and who benefits?

    NeuroRehabilitation

    (2008)
  • StoykovM.E. et al.

    A review of bilateral training for upper extremity hemiparesis

    Occupational Therapy Int.

    (2009)
  • ColomboR.

    Robotic techniques for upper limb evaluation and rehabilitation of stroke patients

    IEEE Trans. Neural Syst. Rehabil. Eng.

    (2005)
  • ReinkensmeyerD.J. et al.

    Robotics, motor learning, and neurologic recovery

    Annu. Rev. Biomed. Eng.

    (2004)
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      Citation Excerpt :

      Robot-assisted bilateral upper limb training is an emerging form of stroke rehabilitation. Most of previous studies used joint space symmetry (mirror) training and visual symmetry modes based on two robotic devices or one device with an external element [14–17,19]. It is worth mentioning that using multiple devices makes the training system more complex and expensive.

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    Qing Miao was born in Hubei of China in 1988. He received the M.E. in Control Science and Control Engineering from Wuhan University of Technology, Hubei, Wuhan, China, in 2014. He is the Ph.D. student in Mechanical and Electrical Engineering from Wuhan University of Technology, Wuhan, China, at present. Since November of 2016, he has been researching as an exchanging Ph.D. student in the Department of Mechanical Engineering at University of Auckland. His research includes medical, intelligent control system and rehabilitation robotics.

    Andrew McDaid gained a BE with first class honours and PhD from the University of Auckland in 2008 and 2012 respectively. He is a senior lecturer at the University of Auckland, New Zealand. He has published more than 60 research papers and has three provisional patents. His main research interests include design and control of medical devices, functional electrical stimulation of neuroprosthetics, smart materials, soft robotics, physiological control (EEG, HD-EMG), gamification and developing intelligent medical robotics for people who have suffered injury, stroke or have disabilities.

    Mingming Zhang was born in Henan of China in 1985. He received the M.E. in mechatronics from Chongqing University, Chongqing, China, in 2012, and the Ph.D. degree in mechanical engineering from the University of Auckland, Auckland, New Zealand, in 2016. Since October of 2015, he has been working as a research fellow in the Department of Mechanical Engineering at the University of Auckland. His research interests include mechatronics, medical and rehabilitation robotics, biomechanics, and advanced control system. He has published over 20 academic papers and hold five patents.

    Parham Kebria has received his B.Sc. and M.Sc. in electrical engineering from Amirkabir University of Technology (Tehran Polytechnique), Tehran, Iran. He is currently working towards his Ph.D. degree in Control and Robotics with the Institute for Intelligent Systems Research and Innovations (IISRI), Deakin University, Australia. Parham’s research interests include control theory, adaptive algorithms, optimization, and Networked intelligent systems.

    Hongsheng Li received the M.E. in computer application from Wuhan University, Wuhan, Hubei Province, China, in 1987, and the Ph.D. degree in mechanical manufacturing and automation from the Huazhong University of Science and Technology, Wuhan, Hubei Province, China, in 2000. Since 2002, he has been working as a professor in the Department of Mechanical and Electrical Engineering at the Wuhan University of Technology. His research include remote control and intelligent diagnosis, intelligent control, pattern recognition, signal processing and digital manufacture.

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