Real-Time EMG Signal Processing with Implementation of PID Control for Upper-Limb Prosthesis

Sattar, Neelum Yousaf; Usama, Syed Ali; Muhammad, Shaheer; Kausar, Zareena

doi:10.1109/aim.2019.8868796

Cited by 16 publications

(18 citation statements)

References 15 publications

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“…We implemented the proportional-integral-derivative (PID) controller [34][35] [36] to update the DBS frequency based on the system output, z (Equation 9). The updated DBS frequency 𝑢(𝑡) is computed as follows,…”

Section: The Pid Controller That Updates the Dbs Frequencymentioning

confidence: 99%

“…We use a biophysically-reasonable simulation study including models of DBS, Vim, motor cortex, motor neurons in the spinal cord, and muscle fibers to generate muscle activities (represented by EMG). To link Vim-DBS to EMG signals in our model-based control framework, model-predicted EMG signals, generated by our simulation study, are used to calculate the feedback biomarker by a polynomial fit, which is processed and implemented in a proportional-integral-derivative (PID) controller [34][35][36] that automatically updates the appropriate DBS frequency. Our model-predicted EMG can replicate the essential tremor symptoms during DBS-OFF, and is consistent with clinical observations of tremor during different frequencies of Vim-DBS.…”

Section: Introductionmentioning

confidence: 99%

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Model-Based Closed-Loop Control of Thalamic Deep Brain Stimulation

Tian,

Saradhi,

Bello

et al. 2023

Preprint

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Closed-loop control of deep brain stimulation (DBS) is crucial for effective and automatic treatments of various neurological disorders like Parkinson’s disease (PD) and essential tremor (ET). Manual (open-loop) DBS programming solely based on clinical observations relies on neurologists’ expertise and patients’ experience. The continuous stimulation in open-loop DBS may decrease battery life and cause side effects. On the contrary, a closed-loop DBS system utilizes a feedback biomarker/signal to track worsening (or improving) patient’s symptoms and offers several advantages compared to open-loop DBS. Existing closed-loop DBS control systems do not incorporate physiological mechanisms underlying the DBS or symptoms, for example how DBS modulates dynamics of synaptic plasticity. In this work, we proposed a computational framework for development of a model-based DBS controller where a biophysically-reasonable model can describe the relationship between DBS and neural activity, and a polynomial-based approximation can estimate the relationship between the neural and behavioral activity. A controller is utilized in our model in a quasi-real-time manner to find DBS patterns that significantly reduce the worsening of symptoms. These DBS patterns can be tested clinically by predicting the effect of DBS before delivering it to the patient. We applied this framework to the problem of finding optimal DBS frequencies for essential tremor given EMG recordings solely. Building on our recent network model of ventral intermediate nuclei (Vim), the main surgical target of the tremor, in response to DBS, we developed a biophysically-reasonable simulation in which physiological mechanisms underlying Vim-DBS are linked to symptomatic changes in EMG signals. By utilizing a PID controller, we showed that a closed-loop system can track EMG signals and adjusts the stimulation frequency of Vim-DBS so that the power of EMG in [2, 200] Hz reaches a desired target. We demonstrated that our model-based closed-loop control system of Vim-DBS finds an appropriate DBS frequency that aligns well with clinical studies. Our model-based closed-loop system is adaptable to different control targets, highlighting its potential usability for different diseases and personalized systems.

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Section: The Pid Controller That Updates the Dbs Frequencymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Model-Based Closed-Loop Control of Thalamic Deep Brain Stimulation

Tian,

Saradhi,

Bello

et al. 2023

Preprint

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“…In recent decade, the research of upper limb prosthesis control has been widely proposed. Different studies focused on improving robust prosthesis control in real time [3,4]. Consequently, pattern recognition control systems have been developed extensively in academic research and have been seen in commercial production recently [3,5,6].…”

Section: Introductionmentioning

confidence: 99%

Hand Recognition System Based on Electromyography for Real-Time Hand Prosthetic Control

Jong

Sa-e²,

Tirasor³

et al. 2023

AEJ

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Upper limb amputation is a significant limitation for achieving routine activities. Myoelectric signals detected by electrodes well-known as Electromyography (EMG) have been targeted to control upper limb prostheses of such lost limbs. Unfortunately, the acquisition, processing and use of such myoelectric signals are sophisticated. Furthermore, it necessarily requires complex computation to fulfil accuracy, robustness, and time-consumption execution for the real-time prosthesis application. Thus, machine learning schemes for pattern recognition are a potential approach to improve the traditional control for hand prostheses due to the movement of users and muscle contraction. This paper presents real-time hand posture recognition based on three hand postures using surface EMG (sEMG) signals. sEMG signals are acquired by the electrode channel and simultaneously collected while making a hand posture. Performance evaluation relies on classification accuracy and time consumption. The performance of six real-time recognition models is evaluated which combine two projection techniques and three classifiers. Results indicate that EMG-based pattern recognition (EMG-PR) control outperforms the traditional control for hand prostheses in real-time application. The highest classification accuracy is approximately 96%, whereas the lowest time consumption is 4 ms. In addition, the accuracy is dropped when the number of electrodes decreases nearly to 3%. These outcomes can apply to real-time hand prostheses to alleviate the limited prostheses available.

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“…Signal acquisition and processing are a great challenge in the control of above elbow amputation due to few or no amount of residual muscle and weak muscle activity [ 4 , 10 , 11 ]. Furthermore, remaining muscle sites for the prosthetic control are not physiologically identified to the distal arm functions [ 12 ].…”

Section: Introductionmentioning

confidence: 99%

fNIRS-Based Upper Limb Motion Intention Recognition Using an Artificial Neural Network for Transhumeral Amputees

Sattar

Kausar

Usama

et al. 2022

Sensors

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Prosthetic arms are designed to assist amputated individuals in the performance of the activities of daily life. Brain machine interfaces are currently employed to enhance the accuracy as well as number of control commands for upper limb prostheses. However, the motion prediction for prosthetic arms and the rehabilitation of amputees suffering from transhumeral amputations is limited. In this paper, functional near-infrared spectroscopy (fNIRS)-based approach for the recognition of human intention for six upper limb motions is proposed. The data were extracted from the study of fifteen healthy subjects and three transhumeral amputees for elbow extension, elbow flexion, wrist pronation, wrist supination, hand open, and hand close. The fNIRS signals were acquired from the motor cortex region of the brain by the commercial NIRSport device. The acquired data samples were filtered using finite impulse response (FIR) filter. Furthermore, signal mean, signal peak and minimum values were computed as feature set. An artificial neural network (ANN) was applied to these data samples. The results show the likelihood of classifying the six arm actions with an accuracy of 78%. The attained results have not yet been reported in any identical study. These achieved fNIRS results for intention detection are promising and suggest that they can be applied for the real-time control of the transhumeral prosthesis.

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Real-Time EMG Signal Processing with Implementation of PID Control for Upper-Limb Prosthesis

Cited by 16 publications

References 15 publications

Model-Based Closed-Loop Control of Thalamic Deep Brain Stimulation

Model-Based Closed-Loop Control of Thalamic Deep Brain Stimulation

Hand Recognition System Based on Electromyography for Real-Time Hand Prosthetic Control

fNIRS-Based Upper Limb Motion Intention Recognition Using an Artificial Neural Network for Transhumeral Amputees

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