Reliability and validity of assessing lower-limb muscle architecture of patients with cerebral palsy (CP) using ultrasound: A systematic review.

AIMS: To investigate the reliability, validity, and level of evidence of applying ultrasound in assessing the lower-limb muscles of patients with cerebral palsy (CP). METHOD: Publications in Medline, PubMed, Web of Science, and Embase were searched on May 10, 2023, to identify and examine relevant studies investigating the reliability/validity of ultrasound in evaluating the architecture of CP lower-limb muscles systematically, following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis 2020 guidelines. RESULTS: Out of 897 records, 9 publications with 111 CP participants aged 3.8-17.0 years were included (8 focused on intra-rater and inter-rater reliability, 2 focused on validity, and 4 were with high quality). The ultrasound-based measurements of muscle thickness (intra-rater only), muscle length, cross-sectional area, muscle volume, fascicle length, and pennation angle showed high reliability, with the majority of intraclass correlation coefficient (ICC) values being larger than 0.9. Moderate-to-good correlations between ultrasound and magnetic resonance imaging measurements existed in muscle thickness and cross-sectional area (0.62 ≤ ICC ≤ 0.82). INTERPRETATION: Generally, ultrasound has high reliability and validity in evaluating the CP muscle architecture, but this is mainly supported by moderate and limited levels of evidence. More high-quality future studies are needed.

Controlling Upper Limb Prostheses Using Sonomyography (SMG): A Review.

This paper presents a critical review and comparison of the results of recently published studies in the fields of human-machine interface and the use of sonomyography (SMG) for the control of upper limb prothesis. For this review paper, a combination of the keywords "Human Machine Interface", "Sonomyography", "Ultrasound", "Upper Limb Prosthesis", "Artificial Intelligence", and "Non-Invasive Sensors" was used to search for articles on Google Scholar and PubMed. Sixty-one articles were found, of which fifty-nine were used in this review. For a comparison of the different ultrasound modes, feature extraction methods, and machine learning algorithms, 16 articles were used. Various modes of ultrasound devices for prosthetic control, various machine learning algorithms for classifying different hand gestures, and various feature extraction methods for increasing the accuracy of artificial intelligence used in their controlling systems are reviewed in this article. The results of the review article show that ultrasound sensing has the potential to be used as a viable human-machine interface in order to control bionic hands with multiple degrees of freedom. Moreover, different hand gestures can be classified by different machine learning algorithms trained with extracted features from collected data with an accuracy of around 95%.

A Compact and Lightweight Rehabilitative Exoskeleton to Restore Grasping Functions for People with Hand Paralysis.

Millions of individuals suffer from upper extremity paralysis caused by neurological disorders including stroke, traumatic brain injury, or spinal cord injury. Robotic hand exoskeletons can substitute the missing motor control and help restore the functions in daily operations. However, most of the hand exoskeletons are bulky, stationary, and cumbersome to use. We have modified a recent existing design (Tenoexo) to prototype a motorized, lightweight, fully wearable rehabilitative hand exoskeleton by combining rigid parts with a soft mechanism capable of producing various grasps needed for the execution of daily tasks. Mechanical evaluation of our exoskeleton showed that it can produce fingertip force up to 8 N and can cover 91.5° of range of motion in just 3 s. We further tested the performance of the developed robotic exoskeleton in two quadriplegics with chronic hand paralysis and observed immediate success on independent grasping of different daily objects. The results suggested that our exoskeleton is a viable option for hand function assistance, allowing patients to regain lost finger control for everyday activities.