Wearable sensor technology and medical robotics for fatigue assessment using electromyography signal processing

Byeon, Haewon; Seno, Mohammed E.; Yajid, Mohd Shukri Ab; Shabaz, Mohammad; Rajiv, Asha; Kaur, Amarjeet; Sivaranjani, R.; Abdurakhimova, Dilora; Soni, Mukesh

doi:10.1007/s11760-024-03505-6

SIViP

2024

DOI: 10.1007/s11760-024-03505-6

|View full text |Cite

Wearable sensor technology and medical robotics for fatigue assessment using electromyography signal processing

Haewon Byeon,

Mohammed E. Seno,

Mohd Shukri Ab Yajid

et al.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Multivariable analysis for predicting lower limb muscular strength with a hip-joint exoskeleton

Kang,

Lee,

Kim

et al. 2024

Front. Bioeng. Biotechnol.

View full text Add to dashboard Cite

IntroductionAdvancements in exercise science have highlighted the importance of accurate muscular strength assessments for optimizing performance and preventing injuries.MethodsWe propose a novel approach to measuring muscular strength in young, healthy individuals using Bot Fit, a hip-joint exoskeleton, during resistance exercises. In this study, we introduced performance metrics to evaluate exercise performance during both short and extended durations of three resistance exercises: squats, knee-ups, and reverse lunges. These metrics, derived from the robot’s motor signals and sEMG data, include initial exercise speed, the number of repetitions, and muscle engagement. We compared these metrics against baseline muscular strength, measured using standard fitness equipment such as one-repetition maximum (1RM) and isometric contraction tests, conducted with 30 participants aged 23 to 30 years.ResultsOur results revealed that initial exercise speed and the number of repetitions were significant predictors of baseline muscular strength. Using statistical multivariable analysis, we developed a highly accurate model (R=0.884, adj. R2=0.753, p-value <0.001) and an efficient model (with all models achieving R>0.87) with strong explanatory power.ConclusionThis model, focusing on a single exercise (squat) and a key performance metric (initial speed), accurately represents the muscular strength of Bot Fit users across all three exercises. This study expands the application of hip-joint exoskeleton robots, enabling efficient estimation of lower limb muscle strength through resistance exercises with Bot Fit.

show abstract

Multivariable analysis for predicting lower limb muscular strength with a hip-joint exoskeleton

Kang,

Lee,

Kim

et al. 2024

Front. Bioeng. Biotechnol.

View full text Add to dashboard Cite

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Wearable sensor technology and medical robotics for fatigue assessment using electromyography signal processing

Cited by 1 publication

References 27 publications

Multivariable analysis for predicting lower limb muscular strength with a hip-joint exoskeleton

Multivariable analysis for predicting lower limb muscular strength with a hip-joint exoskeleton

Contact Info

Product

Resources

About