Human motion intent learning based motion assistance control for a wearable exoskeleton

Long, Yi; Du, Zhijiang; Wang, Weidong; Dong, Wei

doi:10.1016/j.rcim.2017.08.007

Cited by 38 publications

(17 citation statements)

References 18 publications

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“…For the small-sized sensors, four additional sensor placement strategies were used, but this time in a rowwise manner. e small-sized sensor placements are described as row 1 (sensors 1, 5, 9, and 13, named region 5), row 2 (sensors 2, 6, 10, and 14, named region 6), row 3 sensors in a row assume a ring shape around the forearm as shown in Figure 3(c). Using the self-made four-channel acquisition system, the MSC signals could be measured in one row or one column on the forearm.…”

Section: Locations Of the Sensorsmentioning

confidence: 99%

“…In recent years, wearable devices [1,2], such as exoskeletons and prostheses [3,4], have shown a substantial promise in the fields of healthcare and rehabilitation that focus on restoring upper or lower extremity motor functions. More so, advances in technology have led to the development of wearable devices in the form of smart electronics that could continuously monitor different physiological parameters associated with the health status in humans [5,6].…”

Section: Introductionmentioning

confidence: 99%

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Identification of Upper-Limb Movements Based on Muscle Shape Change Signals for Human-Robot Interaction

Huang

Wang

et al. 2020

Computational and Mathematical Methods in Medicine

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Towards providing efficient human-robot interaction, surface electromyogram (EMG) signals have been widely adopted for the identification of different limb movement intentions. Since the available EMG signal sensors are highly susceptible to external interferences such as electromagnetic artifacts and muscle fatigues, the quality of EMG recordings would be mostly corrupted, which may decay the performance of EMG-based control systems. Given the fact that the muscle shape changes (MSC) would be different when doing various limb movements, the MSC signal would be nonsensitive to electromagnetic artifacts and muscle fatigues and maybe promising for movement intention recognition. In this study, a novel nanogold flexible and stretchable sensor was developed for the acquisition of MSC signals utilized for decoding multiple classes of limb movement intents. More precisely, four sensors were used to measure the MSC signals from the right forearm of each subject when they performed seven classes of movements. Also, six different features were extracted from the measured MSC signals, and a linear discriminant analysis- (LDA-) based classifier was built for movement classification tasks. The experimental results showed that using MSC signals could achieve an average recognition rate of about 96.06 ± 1.84% by properly placing the four flexible and stretchable sensors on the forearm. Additionally, when the MSC sampling rate was greater than 100 Hz and the analysis window length was greater than 20 ms, the movement recognition accuracy would be only slightly increased. These pilot results suggest that the MSC-based method should be feasible in movement identifications for human-robot interaction, and at the same time, they provide a systematic reference for the use of the flexible and stretchable sensors in human-robot interaction systems.

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Section: Locations Of the Sensorsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Identification of Upper-Limb Movements Based on Muscle Shape Change Signals for Human-Robot Interaction

Huang

Wang

et al. 2020

Computational and Mathematical Methods in Medicine

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“…That kind of approach can be applied to human intention recognition problem, because it enables adding new goals during the experiment as well as an elegant framework for learning the model parameters online. Assistive technology such as smart homes [23], exoskeletons [24] and semi-autonomous wheelchairs [25] benefit also from precise human intention recognition. In [25], authors propose a POMDP driven algorithm for wheelchair control taking into account the uncertainty of user's inputs because of, e.g., unsteady hands.…”

Section: Related Workmentioning

confidence: 99%

Human intention estimation based on hidden Markov model motion validation for safe flexible robotized warehouses

Petković

Puljiz

Marković

et al. 2019

Robotics and Computer-Integrated Manufacturing

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With the substantial growth of logistics businesses the need for larger warehouses and their automation arises, thus using robots as assistants to human workers is becoming a priority. In order to operate efficiently and safely, robot assistants or the supervising system should recognize human intentions in real-time. Theory of mind (ToM) is an intuitive human conception of other humans' mental state, i.e., beliefs and desires, and how they cause behavior. In this paper we propose a ToM based human intention estimation algorithm for flexible robotized warehouses. We observe human's, i.e., worker's motion and validate it with respect to the goal locations using generalized Voronoi diagram based path planning. These observations are then processed by the proposed hidden Markov model framework which estimates worker intentions in an online manner, capable of handling changing environments. To test the proposed intention estimation we ran experiments in a real-world laboratory warehouse with a worker wearing Microsoft Hololens augmented reality glasses. Furthermore, in order to demonstrate the scalability of the approach to larger warehouses, we propose to use virtual reality digital warehouse twins in order to realistically simulate worker behavior. We conducted intention estimation experiments in the larger warehouse digital twin with up to 24 running robots. We demonstrate that the proposed framework estimates warehouse worker intentions precisely and in the end we discuss the experimental results.

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“…The reason to apply GP and BPNN here is that BPNN has already been widely applied in the fields of human motion intent learning [1], and GP offers superior flexibility compared with traditional modelling methods in learning [16]. There have been successful applications of GP in human motion intent learning and prediction [22,23,44]. Also, unscented Kalman filter (UKF) is used to develop the close loop of the state space model [45].…”

Section: Gp-nrax-and Bpnn-integrated Ssm For Joint Angle Predictionmentioning

confidence: 99%

“…To overcome this limitation, numerous researchers have suggested different sparse approximations, including the subset of data (SoD) approximation, fully independent training conditional (FITC) approximation, and partially independent training conditional (PITC) approximation. One successful application in joint angle prediction can be found in [44] where an online sparse GP algorithm is proposed to learn the human motion intent. In addition, the integration with an evolving system would be another choice [22].…”

Section: Experimental Resulsmentioning

confidence: 99%

Gaussian Process-Integrated State Space Model for Continuous Joint Angle Prediction from EMG and Interactive Force in a Human-Exoskeleton System

2019

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As one of the most direct indicators of the transparency between a human and an exoskeleton, interactive force has rarely been fused with electromyography (EMG) in the control of human-exoskeleton systems, the performances of which are largely determined by the accuracy of the continuous joint angle prediction. To achieve intuitive and naturalistic human intent learning, a state space model (SSM) for continuous angle prediction of knee joint is developed. When the influence of the interactive force is often ignored in the existing models of human-exoskeleton systems, interactive force is applied as the measurement model output of the proposed SSM, and the EMG signal is used as the state model input signal to indicate muscle activation. The forward dynamics of joint motion and the human-machine interaction mechanism, i.e., the biomechanical interpretations of the interactive force generation mechanism, are derived as the bases for the state model and measurement model based on Hill’s muscle model and semiphenomenological (SP) muscular model, respectively. Gaussian process (GP)-based nonlinear autoregressive with the exogenous inputs (NARX) model and back-propagation neural network (BPNN) are applied to provide better adaptivity for the SSM in practical applications. Corresponding experimental results demonstrate the validity and superiority of the method.

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Human motion intent learning based motion assistance control for a wearable exoskeleton

Cited by 38 publications

References 18 publications

Identification of Upper-Limb Movements Based on Muscle Shape Change Signals for Human-Robot Interaction

Identification of Upper-Limb Movements Based on Muscle Shape Change Signals for Human-Robot Interaction

Human intention estimation based on hidden Markov model motion validation for safe flexible robotized warehouses

Gaussian Process-Integrated State Space Model for Continuous Joint Angle Prediction from EMG and Interactive Force in a Human-Exoskeleton System

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