A Systematic Literature Review of Intent Sensing for Control of Medical Devices

Russell, Joseph; Bergmann, Jeroen

doi:10.1109/tmrb.2021.3135704

Cited by 10 publications

(12 citation statements)

References 52 publications

(51 reference statements)

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“…The objective of this experiment was to use the onset movement to predict the task goal in the planar space. In a sense, it can be classified as an inference of the task goal by a short-term motion [ 35 ]. Therefore, the input dataset was extracted on the four onset time windows (OTW), listed as 50, 100, 150, and 200 ms.…”

Section: Methodsmentioning

confidence: 99%

Learning-Based Motion-Intention Prediction for End-Point Control of Upper-Limb-Assistive Robots

Yang

Garg

Gao

et al. 2023

Sensors

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The lack of intuitive and active human–robot interaction makes it difficult to use upper-limb-assistive devices. In this paper, we propose a novel learning-based controller that intuitively uses onset motion to predict the desired end-point position for an assistive robot. A multi-modal sensing system comprising inertial measurement units (IMUs), electromyographic (EMG) sensors, and mechanomyography (MMG) sensors was implemented. This system was used to acquire kinematic and physiological signals during reaching and placing tasks performed by five healthy subjects. The onset motion data of each motion trial were extracted to input into traditional regression models and deep learning models for training and testing. The models can predict the position of the hand in planar space, which is the reference position for low-level position controllers. The results show that using IMU sensor with the proposed prediction model is sufficient for motion intention detection, which can provide almost the same prediction performance compared with adding EMG or MMG. Additionally, recurrent neural network (RNN)-based models can predict target positions over a short onset time window for reaching motions and are suitable for predicting targets over a longer horizon for placing tasks. This study’s detailed analysis can improve the usability of the assistive/rehabilitation robots.

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Section: Methodsmentioning

confidence: 99%

Learning-Based Motion-Intention Prediction for End-Point Control of Upper-Limb-Assistive Robots

Yang

Garg

Gao

et al. 2023

Sensors

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“… Framework representing the three aspects of intent sensing. Adapted from [ 1 ]. Grey regions indicate stages not utilised in this study.…”

Section: Figurementioning

confidence: 99%

“…Most currently available devices, from personal computers or mobile phones to home virtual assistants, require direct cues from the user for control. This might be through a touch interface, a voice command, or even a gesture, but all require a conscious input from the user to operate the device [ 1 ].…”

Section: Introductionmentioning

confidence: 99%

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Towards Dynamic Multi-Modal Intent Sensing Using Probabilistic Sensor Networks

Russell

Bergmann

Nagaraja

2022

Sensors

Self Cite

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Intent sensing—the ability to sense what a user wants to happen—has many potential technological applications. Assistive medical devices, such as prosthetic limbs, could benefit from intent-based control systems, allowing for faster and more intuitive control. The accuracy of intent sensing could be improved by using multiple sensors sensing multiple environments. As users will typically pass through different sensing environments throughout the day, the system should be dynamic, with sensors dropping in and out as required. An intent-sensing algorithm that allows for this cannot rely on training from only a particular combination of sensors. It should allow any (dynamic) combination of sensors to be used. Therefore, the objective of this study is to develop and test a dynamic intent-sensing system under changing conditions. A method has been proposed that treats each sensor individually and combines them using Bayesian sensor fusion. This approach was tested on laboratory data obtained from subjects wearing Inertial Measurement Units and surface electromyography electrodes. The proposed algorithm was then used to classify functional reach activities and compare the performance to an established classifier (k-nearest-neighbours) in cases of simulated sensor dropouts. Results showed that the Bayesian sensor fusion algorithm was less affected as more sensors dropped out, supporting this intent-sensing approach as viable in dynamic real-world scenarios.

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“…Russell provided a definition for motion intent, aimed to systematically review current methods for sensing intent with a comparison of different methods, and proposed the possibility of combining the methods. This provides essential information on addressing task goal interference or predicting future actions [6]. Since sEMG has the characteristics of being advanced, highly correlated, safe, and easy to obtain, it was studied regarding the motion recognition of wrist, shoulder, and elbow joints of upper limbs, and some achievements were obtained.…”

Section: Introductionmentioning

confidence: 99%

RBF Sliding Mode Control Method for an Upper Limb Rehabilitation Exoskeleton Based on Intent Recognition

2022

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Aiming at the lack of active willingness of patients to participate in the current upper limb exoskeleton rehabilitation training control methods, this study proposed a radial basis function (RBF) sliding mode impedance control method based on surface electromyography (sEMG) to identify the movement intention of upper limb rehabilitation. The proposed control method realizes the process of active and passive rehabilitation training according to the wearer’s movement intention. This study first established a joint angle prediction model based on sEMG for the problem of poor human–machine coupling and used the least-squares support vector machine method (LSSVM) to complete the upper limb joint angle prediction. In addition, in view of the problem of poor compliance in the rehabilitation training process, an adaptive sliding mode controller based on the RBF network approximation system model was proposed. In the process of active training, an impedance model was added based on the position loop control, which could dynamically adjust the motion trajectory according to the interaction force. The experiment results showed that the impedance control method based on the RBF could effectively reduce the interaction force between the human and machine to improve the compliance of the exoskeleton manipulator and achieve the purpose of stabilizing the impedance characteristics of the system.

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A Systematic Literature Review of Intent Sensing for Control of Medical Devices

Cited by 10 publications

References 52 publications

Learning-Based Motion-Intention Prediction for End-Point Control of Upper-Limb-Assistive Robots

Learning-Based Motion-Intention Prediction for End-Point Control of Upper-Limb-Assistive Robots

Towards Dynamic Multi-Modal Intent Sensing Using Probabilistic Sensor Networks

RBF Sliding Mode Control Method for an Upper Limb Rehabilitation Exoskeleton Based on Intent Recognition

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