Upper Limb Neuroprostheses: Recent Advances and Future Directions

Koutsou, Aikaterini D.; Summa, Susanna; Nasser, Bilal; Gutierrez, J.; Thangaramanujam, Muthukumaran

doi:10.1007/978-3-642-38556-8_11

Cited by 9 publications

(4 citation statements)

References 88 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Here, the value of 50 is selected as the data segment length (L=50 ms) under the sampling rate f sample of 1 kHz. In addition, the latency between the controller and controlee is within 350 ms, which is adequate for real-time neural prostheses control [32]. Thus the maximum filter order M is 6 according to the following equation:…”

Section: Analysis Of Gs-apef Denoising Artifacts Under Differential Frequency Stimulationmentioning

confidence: 99%

Real-Time Artifact Removal System for Surface EMG Processing During Ten-Fold Frequency Electrical Stimulation

Wang

Fan

et al. 2021

IEEE Access

View full text Add to dashboard Cite

In this paper, three easily implemented hardware algorithms, including the adaptive prediction error filter based on the Gram-Schmidt algorithm (GS-APEF), the least mean square adaptive filter and the comb filter, are extensively investigated for artifact denoising on a constructed semi-simulated database with varied ten-fold frequency stimulation. By implementing the GS-APEF in the fieldprogrammable gate array (FPGA) and using the edge noise mitigating technique, a stimulation artifact denoising system is designed to realize real-time stimulation artifact removal under varied ten-fold frequency functional electrical stimulation. Good performance of the artifact denoising is demonstrated in proof-of-concept experiments on able-bodied subjects with a mean correlation coefficient between the root mean square profile of denoised surface electromyography and volitional force of 0.94, verifying the validity of the proposed prototype.INDEX TERMS Functional electrical stimulation (FES), stimulus artifact removal (SAR), surface electromyography (sEMG), adaptive filter, field-programmable gate array (FPGA)

show abstract

Section: Analysis Of Gs-apef Denoising Artifacts Under Differential Frequency Stimulationmentioning

confidence: 99%

Real-Time Artifact Removal System for Surface EMG Processing During Ten-Fold Frequency Electrical Stimulation

Wang

Fan

et al. 2021

IEEE Access

View full text Add to dashboard Cite

show abstract

“…In addition, in the clinical trials, the successful reconstruction of gestures in hemiplegic patients under the control of able-bodied volunteers indicates that the wearable EMGB can provide an effective means of gesture training. In addition, the bridging test results from both the test with the able-bodied volunteers and the clinical trials indicate that the delay time between the controlling limb and the controlled limb is approximately 300 ms, satisfying the requirements for a real-time neural prosthesis [1], [25]. However, there were also some unsuccessful cases in the randomized controlled trials in the latter half of the bridging test with the ablebodied volunteers.…”

Section: Discussionmentioning

confidence: 72%

“…The associate editor coordinating the review of this manuscript and approving it for publication was Yizhang Jiang . limb motor function in patients with stroke and spinal cord injury (SCI) [1], [2]. Enhancing patients' volitional control in FES is key to improving its efficacy [3], [4].…”

Section: Introductionmentioning

confidence: 99%

Wearable EMG Bridge—A Multiple-Gesture Reconstruction System Using Electrical Stimulation Controlled by the Volitional Surface Electromyogram of a Healthy Forearm

Wang

et al. 2020

IEEE Access

View full text Add to dashboard Cite

In this study, a wearable prototype system was developed for multiple-gesture rehabilitation using electrical stimulation controlled by a volitional surface electromyography (sEMG) scan of a healthy forearm. The purpose of the prototype system is to reconstruct multiple gestures of a paralysed limb and to simplify the positioning of sEMG detection sites on a healthy forearm. A self-designed eight-channel sEMG detection armband was used to detect the sEMG signal distributions of the muscle groups in healthy forearms. Linear discriminant analysis (LDA) was used to classify the sEMG signal distributions corresponding to different gestures, and then the classification results were mapped to corresponding stimulation channels. The sEMG signal with the maximum root mean square (RMS) was used as the source of stimulus coding for each gesture. Our proposed mean absolute value (MAV)/number of slope sign changes (NSS) dual-coding (MNDC) algorithm was used to encode the sEMG signal into an electrical stimulus with a dynamic pulse width and frequency. The constant-current stimulation armband electrically stimulated multiple muscles in the affected forearm by means of a circuit designed with a time-division multiplexed stimulation channel. An experiment involving 6 able-bodied volunteers showed that when the detection armband was located near the middle of the forearm, the gesture classification accuracy was greater than 90%, and each active sEMG signal was high. Gesture bridge experiments, including grasping, wrist flexion, wrist extension and finger extension, were carried out among six hemiplegic subjects and between one able-bodied volunteer acting as a controller and each of six stroke patients as the controllee. Both sets of results show that the proposed system can reconstruct these four gestures in the controlled subject with a delay of at most 360 ms and with a correlation coefficient of > 0.72.

show abstract

“…Functional electrical stimulation (FES) is a clinical therapeutic technique that can help paralyzed patients to recover limb motor function lost due to stroke or spinal cord injury [ 1 ]. The electrical stimulation pulses act on the nerve or the target muscle through surface [ 2 – 4 ] or implanted electrodes [ 5 ] to induce artificial limb movements.…”

Section: Introductionmentioning

confidence: 99%

Electrode placement on the forearm for selective stimulation of finger extension/flexion

et al. 2018

View full text Add to dashboard Cite

It is still challenging to achieve a complex grasp or fine finger control by using surface functional electrical stimulation (FES), which usually requires a precise electrode configuration under laboratory or clinical settings. The goals of this study are as follows: 1) to study the possibility of selectively activating individual fingers; 2) to investigate whether the current activation threshold and selective range of individual fingers are affected by two factors: changes in the electrode position and forearm rotation (pronation, neutral and supination); and 3) to explore a theoretical model for guidance of the electrode placement used for selective activation of individual fingers. A coordinate system with more than 400 grid points was established over the forearm skin surface. A searching procedure was used to traverse all grid points to identify the stimulation points for finger extension/flexion by applying monophasic stimulation pulses. Some of the stimulation points for finger extension and flexion were selected and tested in their respective two different forearm postures according to the number and the type of the activated fingers and the strength of finger action response to the electrical stimulation at the stimulation point. The activation thresholds and current ranges of the selectively activated finger at each stimulation point were determined by visual analysis. The stimulation points were divided into three groups (“Low”, “Medium” and “High”) according to the thresholds of the 1st activated fingers. The angles produced by the selectively activated finger within selective current ranges were measured and analyzed. Selective stimulation of extension/flexion is possible for most fingers. Small changes in electrode position and forearm rotation have no significant effect on the threshold amplitude and the current range for the selective activation of most fingers (p > 0.05). The current range is the largest (more than 2 mA) for selective activation of the thumb, followed by those for the index, ring, middle and little fingers. The stimulation points in the “Low” group for all five fingers lead to noticeable finger angles at low current intensity, especially for the index, middle, and ring fingers. The slopes of the finger angle variation in the “Low” group for digits 2~4 are inversely proportional to the current intensity, whereas the slopes of the finger angle variation in other groups and in all groups for the thumb and little finger are proportional to the current intensity. It is possible to selectively activate the extension/flexion of most fingers by stimulating the forearm muscles. The physiological characteristics of each finger should be considered when placing the negative electrode for selective stimulation of individual fingers. The electrode placement used for the selective activation of individual fingers should not be confined to the location with the lowest activation threshold.

show abstract

Upper Limb Neuroprostheses: Recent Advances and Future Directions

Cited by 9 publications

References 88 publications

Real-Time Artifact Removal System for Surface EMG Processing During Ten-Fold Frequency Electrical Stimulation

Real-Time Artifact Removal System for Surface EMG Processing During Ten-Fold Frequency Electrical Stimulation

Wearable EMG Bridge—A Multiple-Gesture Reconstruction System Using Electrical Stimulation Controlled by the Volitional Surface Electromyogram of a Healthy Forearm

Electrode placement on the forearm for selective stimulation of finger extension/flexion

Contact Info

Product

Resources

About