Robot assisted treadmill training: Mechanisms and training strategies

Hussain, Shahid; Xie, Shane; Li, Guangyu

doi:10.1016/j.medengphy.2010.12.010

Cited by 102 publications

(86 citation statements)

References 64 publications

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“…Actuation and control technology plays an important role in the design and functioning of these robotic gait training orthoses [12,13]. The initial prototypes of robotic gait training orthoses were designed using linear actuators such as electric motors which have high endpoint impedance.…”

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confidence: 99%

“…In fact, for an effective treatment, patients should be encouraged to participate actively and the role of rehabilitation robots should be to only scaffold or support when it is required [8]. Keeping this in view, several control strategies, namely, compliant [9], assist-as-needed (AAN) [5,[16][17][18] and patient cooperative approaches [9,19] have been developed in order to regulate robotic assistance with patient's disability levels [12]. A state of the art review of gait rehabilitation robot designs and their control strategies has been provided in [12,20].…”

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confidence: 99%

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Assist-as-Needed Control of an Intrinsically Compliant Robotic Gait Training Orthosis

Hussain

Jamwal

Ghayesh

et al. 2017

IEEE Trans. Ind. Electron.

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Abstract-It is a common hypothesis in the field of robot assisted gait rehabilitation that the active involvement and voluntary participation of neurologically impaired subjects in the robotic gait training process may enhance the outcomes of such therapy. An adaptive seamless assist-as-needed (AAN) control scheme was developed for the robotic gait training. The AAN control scheme learns in real time the disability level of human subjects based on the trajectory tracking errors and adapts the robotic assistance accordingly. The overall AAN control architecture works on the basis of a robust adaptive control approach. The performance of seamless AAN control scheme was evaluated during treadmill training with a compliant robotic orthosis having 6-degrees of freedom (DOF). Two experiments, namely trajectory following experiment and the AAN experiment were carried out to evaluate the performance of seamless adaptive AAN control scheme. It was found that the robotic orthosis is capable of guiding the subjects' limbs on reference trajectories during the trajectory following experiment. Also, a variation in robotic assistance was recorded during the AAN experiment based on the voluntary participation of human subjects. This work is an advance on the current state of the art in the compliant actuation of robotic gait rehabilitation orthoses in the context of seamless AAN gait training.

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confidence: 99%

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Assist-as-Needed Control of an Intrinsically Compliant Robotic Gait Training Orthosis

Hussain

Jamwal

Ghayesh

et al. 2017

IEEE Trans. Ind. Electron.

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“…Casadio et al defines four performance indicators or outcome measures [5] to analyze and assess the hemiplegias' rehabilitation outcomes. Literature [6] completes the rehabilitation evaluation of the effect of robot-assisted gait training through several physical parameters including gait speed, rhythm, and walking duration, etc. Furthermore, the combination of surface EMG and robotic control can be found in [7].…”

Section: Related Workmentioning

confidence: 99%

Rehabilitation Assessment Based on the Complexity and Fluctuation of EMG Signal

Chen

Meng

2012

AEF

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Abstract. The assessment after rehabilitation is an important part of rehabilitation medicine, and of great significance for the basic research and clinical application for rehabilitation medicine. Physician can make objective analysis about patients' present conditions, and then appropriate training program can be developed for patients to recover their body function more effectively. Three kinds of EMG signal, that is, the healthy, patient with myopathy, and patient with neuropathy, are involved. The complexity of the three types of signal is compared and the related variables (embedding dimension and time delay of phase space reconstruction) are determined by singular-value decomposition and mutual information method. Moreover, a novel effective approach improved from Hilbert-Huang Transform (HHT) is proposed here for the further comparision among the three EMG signals. The results show both methods can distinguish the three kinds of EMG signal clearly.

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“…1,2 Robotics can relieve the therapists from the heavy rehabilitation task and facilitate repetitive, systematic and prolonged rehabilitation training. 3 Despite the advantages of assistive robotics in rehabilitation, it is difficult to realize human-active control strategy on robotics. On one hand, to realize the robot-aided rehabilitation training, a human-robot interface must be established to help the robot understand the motion intention of the operator.…”

Section: Introductionmentioning

confidence: 99%

A Subject-Specific EMG-Driven Musculoskeletal Model for Applications in Lower-Limb Rehabilitation Robotics

Ding

Liu

et al. 2016

Int. J. Human. Robot.

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Robotic devices have great potential in physical therapy owing to their repeatability, reliability and cost economy. However, there are great challenges to realize active control strategy, since the operator's motion intention is uneasy to be recognized by robotics online. The purpose of this paper is to propose a subject-specific electromyography (EMG)-driven musculoskeletal model to estimate subject's joint torque in real time, which can be used to detect his/her motion intention by forward dynamics, and then to explore its potential applications in rehabilitation robotics control. The musculoskeletal model uses muscle activation dynamics to extract muscle activation from raw EMG signals, a Hill-type muscle-tendon model to calculate muscle contraction force, and a proposed subject-specific musculoskeletal geometry model to calculate muscular moment arm. The parameters of muscle activation dynamics and muscle-tendon model are identified by off-line optimization methods in order to minimize the differences between the estimated muscular torques and the reference torques. Validation experiments were conducted on six healthy subjects to evaluate the proposed model. Experimental results demonstrated the model's ability to predict knee joint torque in high-accuracy with the coefficient of determination ( 2 R ) value of 0.934 0.013  and the normalized root-mean-square error (RMSE) of 11.58% 1.44%  .

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Robot assisted treadmill training: Mechanisms and training strategies

Cited by 102 publications

References 64 publications

Assist-as-Needed Control of an Intrinsically Compliant Robotic Gait Training Orthosis

Assist-as-Needed Control of an Intrinsically Compliant Robotic Gait Training Orthosis

Rehabilitation Assessment Based on the Complexity and Fluctuation of EMG Signal

A Subject-Specific EMG-Driven Musculoskeletal Model for Applications in Lower-Limb Rehabilitation Robotics

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