Advanced Prosthetic Control Algorithm for Limb Weakness

Overseen By Jacob Wilson, BS

Age: 18+

Sex: Any

Trial Phase: Academic

Sponsor: University of Utah

Disqualifiers: Incarceration

No Placebo Group All trial participants will receive the active study treatment (no placebo)

Compensation: Varies

Number of Visits: 1

Duration: 2 hours

What You Need to Know Before You Apply

What is the purpose of this trial?

This trial aims to improve how people with limb weakness control advanced orthotic devices (robotic braces) using muscle signals. Researchers are testing a new algorithm, the Experimental Control Algorithm, which could enhance the devices' responsiveness to muscle movements. Participants will use either a current control system or the new experimental one. This study suits individuals who had their first stroke at least six months ago and experience chronic weakness on one side of their body. As an unphased trial, it offers participants the chance to contribute to innovative research that could enhance future treatments for limb weakness.

Do I need to stop my current medications for this trial?

The trial information does not specify whether you need to stop taking your current medications.

What prior data suggests that this advanced prosthetic control algorithm is safe for limb weakness?

Research has shown that high-density electromyography (HD-EMG) control systems have been well-received in past studies. These systems use muscle signals to operate devices like exoskeletons and have improved the control of prosthetic devices without causing major side effects.

In some studies, HD-EMG enhanced device performance by increasing responsiveness and reliability. Although no serious side effects were reported, some individuals might experience slight discomfort from the electrodes, which resemble sticky patches on the skin that read muscle signals. Overall, these treatments have shown promise in safely improving device control. ^{1 2 3 4 5}

Why are researchers excited about this trial?

Researchers are excited about the High-Density EMG Control Algorithm because it offers a novel way to enhance prosthetic limb control for individuals with limb weakness. Unlike standard control systems like MyoPro, which rely on binary muscle signals, this new algorithm uses advanced predictive algorithms to map residual muscle activity to intended movements. This approach could significantly improve precision and responsiveness, offering users a more natural and intuitive prosthetic experience.

What evidence suggests that this trial's algorithms could be effective for limb weakness?

Research has shown that the High-Density EMG Control Algorithm, which participants in this trial may receive, can significantly enhance the control of prosthetic devices. High-density electromyography (HDEMG) records detailed muscle activity, aiding in accurate movement prediction. Studies indicate that this method allows for more precise control of electronic devices, such as robotic braces, by linking muscle activity to intended actions. As a result, individuals might experience better control and use of their limbs. Early findings suggest that this technology could improve the quality of life for those with weak limbs. Meanwhile, the Clinically Available Control Algorithm, serving as the control condition in this trial, uses a commercially available device, the MyoPro, for binary control of the orthosis. ^{1 2 3 6 7}

Are You a Good Fit for This Trial?

This trial is for individuals who have had their first-ever stroke at least 6 months ago, resulting in paresis or hemiparesis. They must be able to move the arm opposite of the affected side but cannot currently be incarcerated.

Inclusion Criteria

My opposite arm moves normally.

I have had one stroke in my life.

I have long-term weakness on one side of my body.

Exclusion Criteria

You are currently in jail or prison.

Timeline for a Trial Participant

Screening

Participants are screened for eligibility to participate in the trial

2-4 weeks

Treatment

Participants use the orthotic device with different control algorithms for up to 2 hours

2 hours

1 visit (in-person)

Follow-up

Participants are monitored for safety and effectiveness after using the device

4 weeks

What Are the Treatments Tested in This Trial?

Interventions

• Commercially Available Control Algorithm
• Experimental Control Algorithm

Trial Overview

The study tests a new control algorithm against a standard one used in advanced myoelectric orthotic devices that help people with limb paralysis by using muscle signals to control a robotic brace.

How Is the Trial Designed?

Group I: High-Density EMG Control Algorithm Experimental Treatment 1 Intervention

Control of the orthosis is based on residual muscle activity mapped to intended movement using advanced predicted algorithms. This condition is a novel algorithm and serves as the experimental condition.

Group II: Clinically Available Control Algorithm (MyoPro) Active Control 1 Intervention

Binary control of the orthosis is based on a clinically available control algorithm. This condition serves as a control. Participants will use a commercially available device, the MyoPro.

Who Is Running the Clinical Trial?

University of Utah

Lead Sponsor

Trials

1,169

Recruited

1,623,000+

Published Research Related to This Trial

Filtered control with filtered feedback showed better short-term performance in myoelectric prosthesis control, indicating it is effective for immediate task execution.

Raw control with raw feedback led to stronger internal model development, which is crucial for improving long-term performance, suggesting that richer feedback can enhance user understanding of the control system.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Evaluating Internal Model Strength and Performance of Myoelectric Prosthesis Control Strategies. Shehata, AW., Scheme, EJ., Sensinger, JW.[2019]

A new training system for myoelectric prosthetic hands was developed to help upper limb amputees learn control schemes and muscle isolation, using a remote-controlled car for practice.

Preliminary testing with eight nonamputee volunteers showed that users improved their skills significantly during training, indicating the system's potential effectiveness for future use with amputees.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

A novel myoelectric training device for upper limb prostheses. Clingman, R., Pidcoe, P.[2014]

Recent advances in pattern recognition control for myoelectric prostheses show promise for improving control in transhumeral amputees, even those who have not undergone reinnervation surgery.

A study with able-bodied control subjects demonstrated that a two degree-of-freedom pattern recognition system can be accurately trained using just four EMG channels, offering better real-time control compared to traditional amplitude control methods.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

A comparison of direct and pattern recognition control for a two degree-of-freedom above elbow virtual prosthesis. Toledo, C., Simon, A., Muñoz, R., et al.[2020]

A new control system for lower limb prostheses uses myoelectric signals from nine residual thigh muscles to accurately control knee and ankle movements, achieving over 90% accuracy in both virtual and real environments.

Only five of the nine muscles are necessary for effective control, indicating a simplified approach to myoelectric control that could enhance the usability of prostheses for individuals with transfemoral amputation.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Non-weight-bearing neural control of a powered transfemoral prosthesis. Hargrove, LJ., Simon, AM., Lipschutz, R., et al.[2021]

Recent advancements in myoelectric control systems for powered limb prostheses focus on improving control through enhanced signal processing techniques.

The review highlights historical challenges in myoelectric signal processing and discusses various strategies to address these issues, paving the way for future developments in prosthetic technology.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Myoelectric control of prostheses. Parker, PA., Scott, RN.[2004]

A comparison of 12 continuous decoders for prosthetic control showed that training on complex movements (like simultaneous grasp and wrist flexion) significantly improved accuracy and speed of control, highlighting the importance of training data in enhancing prosthesis functionality.

User preferences leaned towards the convolutional neural network and modified Kalman filter, while performance metrics were similar across all algorithms, suggesting that while advanced algorithms can improve user experience, simpler models like the modified Kalman filter may offer practical advantages in terms of training speed and computational efficiency.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Activities of daily living with bionic arm improved by combination training and latching filter in prosthesis control comparison. Paskett, MD., Brinton, MR., Hansen, TC., et al.[2021]

Using electromyographic (EMG) signals from residual muscles can significantly enhance the control of robotic lower limb prostheses, allowing for more natural and adaptable movement for amputee users.

This review identifies both the potential benefits and challenges of EMG-based control, emphasizing the need for collaboration among researchers and clinicians to develop advanced bionic limbs that improve motor function.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Myoelectric control of robotic lower limb prostheses: a review of electromyography interfaces, control paradigms, challenges and future directions. Fleming, A., Stafford, N., Huang, S., et al.[2021]

A new evaluation tool was developed to objectively assess the performance of myoelectric control strategies for upper limb prostheses, showing that it effectively follows Fitts' law for both direct and pattern recognition control methods.

Pattern recognition control significantly outperformed direct control in terms of throughput, indicating it may provide better real-time control for users of myoelectric prostheses, while maintaining similar completion rates and path efficiencies.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Real-time comparison of conventional direct control and pattern recognition myoelectric control in a two-dimensional Fitts' law style test. Wurth, SM., Hargrove, LJ.[2020]

In a study with five unilateral transtibial amputees, providing real-time visual feedback of their myoelectric control signals significantly improved their ability to increase peak prosthetic ankle power and positive work during walking (p = 0.02).

Without visual feedback, the amputees could not enhance their prosthetic ankle power during push-off, indicating that real-time feedback is crucial for effective control of powered prostheses.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Locomotor Adaptation by Transtibial Amputees Walking With an Experimental Powered Prosthesis Under Continuous Myoelectric Control. Huang, S., Wensman, JP., Ferris, DP.[2017]

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High-density electromyography (HDEMG) can detect myoelectric activity as control inputs to a variety. of electronically-controlled devices.