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

45 Participants Needed

Trial Summary

What is the purpose of this trial?

The purpose of this study is to improve control of myoelectrically-controlled advanced orthotic devices (an exoskeleton device that use the body's muscle signals to drive movements of a robotic brace) by using advanced predictive decode algorithms, and the use of high count (\> 8) surface electromyographic (sEMG) electrodes.

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 data supports the effectiveness of the treatment Advanced Prosthetic Control Algorithm for Limb Weakness?

Research shows that continuous myoelectric control, which is part of the advanced prosthetic control algorithm, allows amputees to adapt their muscle activation to improve prosthetic function, especially with visual feedback. Additionally, pattern recognition myoelectric control, another component, has been found to outperform direct control in terms of efficiency and speed, suggesting its potential effectiveness in prosthetic applications. ^{1 2 3 4 5}

What makes the Advanced Prosthetic Control Algorithm treatment unique for limb weakness?

The Advanced Prosthetic Control Algorithm is unique because it uses myoelectric signals (electrical signals from muscles) to control prosthetic limbs, allowing for more precise and adaptable movement control compared to traditional mechanical or microprocessor-controlled prostheses. This approach enhances the functionality of prostheses by enabling better control during both weight-bearing and non-weight-bearing activities. ^{3 6 7 8 9}

Eligibility Criteria

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 had a stroke more than 6 months ago.

Exclusion Criteria

You are currently in jail or prison.

Timeline

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

Treatment Details

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.

Participant Groups

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+

Findings from Research

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]

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]

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]

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]

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 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]

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]

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]

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]

References

1. United States pubmed.ncbi.nlm.nih.gov

A novel myoelectric training device for upper limb prostheses. [2014]

2. United States pubmed.ncbi.nlm.nih.gov

Locomotor Adaptation by Transtibial Amputees Walking With an Experimental Powered Prosthesis Under Continuous Myoelectric Control. [2017]

3. United States 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. [2020]

4. England pubmed.ncbi.nlm.nih.gov

Activities of daily living with bionic arm improved by combination training and latching filter in prosthesis control comparison. [2021]

5. United States pubmed.ncbi.nlm.nih.gov

Evaluating Internal Model Strength and Performance of Myoelectric Prosthesis Control Strategies. [2019]

6. England pubmed.ncbi.nlm.nih.gov

Non-weight-bearing neural control of a powered transfemoral prosthesis. [2021]

7. England pubmed.ncbi.nlm.nih.gov

Myoelectric control of robotic lower limb prostheses: a review of electromyography interfaces, control paradigms, challenges and future directions. [2021]

8. United States pubmed.ncbi.nlm.nih.gov

Myoelectric control of prostheses. [2004]

9. United States pubmed.ncbi.nlm.nih.gov

A comparison of direct and pattern recognition control for a two degree-of-freedom above elbow virtual prosthesis. [2020]