Reduction of the effect of arm position variation on real-time performance of motion classification

Geng, Yanjuan; Zhang, Fan; Yang, Lin; Zhang, Yuan-Ting; Li, Guanglin

doi:10.1109/embc.2012.6346539

Cited by 12 publications

(23 citation statements)

References 15 publications

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“…Four of the six sensors were mounted on the remaining arm evenly around the apex of the muscle bulge, 1-2 cm distal to the elbow crease, and another two were placed on the distal end over the flexor muscle and extensor muscle, respectively, as shown in Figure 1. Five arm positions that varied in the sagittal plane and seven classes of forearm movements, which might be commonly involved in daily life activities [23], were considered in the study. The arm positions are as follows:

P1: straight arm reaching forward (horizontal)

P2: arm hanging at side, elbow bent at 135°

P3: arm hanging at side, elbow bent at 90°

P4: arm hanging at side, elbow bent at 45°

P5: straight arm hanging at side

…”

Section: Methodsmentioning

confidence: 99%

“…With a motion-test environment (MTE) developed using MATLAB programing tool [23], each subject was asked to follow a prompt that displays the arm position images in which the seven forearm movements are performed with a moderate muscle contraction force. Each movement was sustained for 4 seconds and repeated twice, resulting in 8-second hybrid signal recordings (EMG and ACC) per motion class in every arm position.…”

Section: Methodsmentioning

confidence: 99%

“…To build a pattern-recognition-based classifier, the EMG and ACC signal recordings were segmented into sequential analysis windows with a time length of 150 ms and a time increment of 100 ms (i.e., 50 ms overlapping) [23, 24]. For EMG signal recordings, four commonly used time-domain features [2], mean absolute value (MAV), number of zeros crossings, number of slope sign changes, and waveform length, were extracted from each analysis window and the features from all the analysis windows were concatenated together to form an EMG feature matrix.…”

Section: Methodsmentioning

confidence: 99%

“…This suggests that using the offline classification performance alone may be insufficient for assessing the usability and clinical viability of EMG-PR-based myoelectric control approaches [42, 43]. Thus it should be required to further examine how variation in arm positions would affect the real-time control performance of EMG-PR and whether the previously proposed approaches [23, 24] are still feasible and robust in reducing the effect of arm position variations in real-time conditions.…”

Section: Introductionmentioning

confidence: 99%

“…In our pilot study conducted with able-bodied subjects, arm position variation was proved to have substantial effect on the real-time motion classification accuracy, and the proposed cascade classifier (CC) method effectively minimized the impact [23]. It was helpful for us to understand the influence of arm position variation on the real-time control performance of EMG-PR and the availability of the proposed CC method in attenuating the influence.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Improving the Robustness of Real-Time Myoelectric Pattern Recognition against Arm Position Changes in Transradial Amputees

Geng

Samuel

Wei

et al. 2017

BioMed Research International

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Previous studies have showed that arm position variations would significantly degrade the classification performance of myoelectric pattern-recognition-based prosthetic control, and the cascade classifier (CC) and multiposition classifier (MPC) have been proposed to minimize such degradation in offline scenarios. However, it remains unknown whether these proposed approaches could also perform well in the clinical use of a multifunctional prosthesis control. In this study, the online effect of arm position variation on motion identification was evaluated by using a motion-test environment (MTE) developed to mimic the real-time control of myoelectric prostheses. The performance of different classifier configurations in reducing the impact of arm position variation was investigated using four real-time metrics based on dataset obtained from transradial amputees. The results of this study showed that, compared to the commonly used motion classification method, the CC and MPC configurations improved the real-time performance across seven classes of movements in five different arm positions (8.7% and 12.7% increments of motion completion rate, resp.). The results also indicated that high offline classification accuracy might not ensure good real-time performance under variable arm positions, which necessitated the investigation of the real-time control performance to gain proper insight on the clinical implementation of EMG-pattern-recognition-based controllers for limb amputees.

show abstract

P1: straight arm reaching forward (horizontal)

P2: arm hanging at side, elbow bent at 135°

P3: arm hanging at side, elbow bent at 90°

P4: arm hanging at side, elbow bent at 45°

P5: straight arm hanging at side

…”

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Improving the Robustness of Real-Time Myoelectric Pattern Recognition against Arm Position Changes in Transradial Amputees

Geng

Samuel

Wei

et al. 2017

BioMed Research International

View full text Add to dashboard Cite

show abstract

Merging Humans and Neuroprosthetics through Regenerative Peripheral Nerve Interfaces

Tian,

Vaskov,

Adidharma

et al. 2024

Semin Plast Surg

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Limb amputations can be devastating and significantly affect an individual's independence, leading to functional and psychosocial challenges in nearly 2 million people in the United States alone. Over the past decade, robotic devices driven by neural signals such as neuroprostheses have shown great potential to restore the lost function of limbs, allowing amputees to regain movement and sensation. However, current neuroprosthetic interfaces have challenges in both signal quality and long-term stability. To overcome these limitations and work toward creating bionic limbs, the Neuromuscular Laboratory at University of Michigan Plastic Surgery has developed the Regenerative Peripheral Nerve Interface (RPNI). This surgical construct embeds a transected peripheral nerve into a free muscle graft, effectively amplifying small peripheral nerve signals to provide enhanced control signals for a neuroprosthetic limb. Furthermore, the RPNI has the potential to provide sensory feedback to the user and facilitate neuroprosthesis embodiment. This review focuses on the animal studies and clinical trials of the RPNI to recapitulate the promising trajectory toward neurobionics where the boundary between an artificial device and the human body becomes indistinct. This paper also sheds light on the prospects of the improvement and dissemination of the RPNI technology.

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Long-term upper-extremity prosthetic control using regenerative peripheral nerve interfaces and implanted EMG electrodes

Vaskov

Lee

et al. 2023

J. Neural Eng.

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Objective. Extracting signals directly from the motor system poses challenges in obtaining both high amplitude and sustainable signals for upper-limb neuroprosthetic control. To translate neural interfaces into the clinical space, these interfaces must provide consistent signals and prosthetic performance. Approach. Previously, we have demonstrated that the Regenerative Peripheral Nerve Interface (RPNI) is a biologically stable, bioamplifier of efferent motor action potentials. Here, we assessed the signal reliability from electrodes surgically implanted in RPNIs and residual innervated muscles in humans for long-term prosthetic control. Main results. RPNI signal quality, measured as signal-to-noise ratio, remained greater than 15 for up to 276 and 1054 days in participant 1 (P1), and participant 2 (P2), respectively. Electromyography from both RPNIs and residual muscles was used to decode finger and grasp movements. Though signal amplitude varied between sessions, P2 maintained real-time prosthetic performance above 94% accuracy for 604 days without recalibration. Additionally, P2 completed a real-world multi-sequence coffee task with 99% accuracy for 611 days without recalibration. Significance. This study demonstrates the potential of RPNIs and implanted EMG electrodes as a long-term interface for enhanced prosthetic control.

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Reduction of the effect of arm position variation on real-time performance of motion classification

Cited by 12 publications

References 15 publications

Improving the Robustness of Real-Time Myoelectric Pattern Recognition against Arm Position Changes in Transradial Amputees

Improving the Robustness of Real-Time Myoelectric Pattern Recognition against Arm Position Changes in Transradial Amputees

Merging Humans and Neuroprosthetics through Regenerative Peripheral Nerve Interfaces

Long-term upper-extremity prosthetic control using regenerative peripheral nerve interfaces and implanted EMG electrodes

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