Category Ranking

98%

Total Visits

921

Avg Visit Duration

2 minutes

Citations

20

Article Abstract

Finger technique is a crucial aspect of piano learning, and hand exoskeleton mechanisms effectively assist novice piano players in maintaining correct finger technique consistently. Addressing current issues with exoskeleton robots, such as the inability to provide continuous correction of finger technique and their considerable weight, a novel hand exoskeleton robot has been developed to enhance finger technique through continuous correction and reduced weight. Initial data are gathered using finger joint angle sensors to analyze movements during piano playing, focusing on the trajectory and angular velocity of key strikes. This analysis informs the design of a 6-bar double-closed-loop mechanism with an end equivalent sliding pair, using analytical methods to establish the relationship between motor extension and input rod rotation. Simulation studies assess the exoskeleton's motion space and dynamics, confirming its capability to meet structural and functional demands for accurate key striking. Prototype testing validates the exoskeleton's ability to maintain correct finger positioning and mimic natural strike speeds, thus improving playing technique while ensuring comfort and safety.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11274512PMC
http://dx.doi.org/10.3390/biomimetics9070385DOI Listing

Publication Analysis

Top Keywords

finger technique
16
hand exoskeleton
12
piano playing
8
exoskeleton robot
8
correct finger
8
continuous correction
8
finger
6
technique
5
design verification
4
piano
4

Similar Publications

Deep Learning-Assisted Organogel Pressure Sensor for Alphabet Recognition and Bio-Mechanical Motion Monitoring.

Nanomicro Lett

September 2025

Nanomaterials & System Lab, Major of Mechatronics Engineering, Faculty of Applied Energy System, Jeju National University, Jeju, 63243, Republic of Korea.

Wearable sensors integrated with deep learning techniques have the potential to revolutionize seamless human-machine interfaces for real-time health monitoring, clinical diagnosis, and robotic applications. Nevertheless, it remains a critical challenge to simultaneously achieve desirable mechanical and electrical performance along with biocompatibility, adhesion, self-healing, and environmental robustness with excellent sensing metrics. Herein, we report a multifunctional, anti-freezing, self-adhesive, and self-healable organogel pressure sensor composed of cobalt nanoparticle encapsulated nitrogen-doped carbon nanotubes (CoN CNT) embedded in a polyvinyl alcohol-gelatin (PVA/GLE) matrix.

View Article and Find Full Text PDF

Purpose: To investigate the role of personal risk factors in the occurrence of the vascular, neurological and fibroproliferative disorders of the hand-arm vibration syndrome (HAVS) in workers groups exposed to hand-transmitted vibration (HTV).

Methods: HAVS prevalence and incidence data were pooled across a series of cross-sectional studies (total sample: 1272 HTV workers, 579 controls) and prospective cohort studies (total sample: 377 HTV workers, 138 controls) conducted in Central and North-Eastern Italy. The pooled studies included detailed individual-level information about HTV exposure, personal risk factors, medical comorbidities and HAVS disorders.

View Article and Find Full Text PDF

Background: Cortisol and growth hormone are important for sleep regulation and cognition. Sleep is critical for cognitive functioning, and memory consolidation. Patients with pituitary disease experience hormonal dysregulation, impaired sleep quality, and cognitive dysfunction.

View Article and Find Full Text PDF

Background: Reticular pseudodrusen (RPD) signify a critical phenotype driving vision loss in age-related macular degeneration (AMD). This study sought to develop and externally test a deep learning (DL) model to detect RPD on optical coherence tomography (OCT) scans with expert-level performance.

Methods: RPD were manually segmented in 9800 OCT B-scans from individuals enrolled in a multicentre randomised trial.

View Article and Find Full Text PDF

This study explores deep feature representations from photoplethysmography (PPG) signals for coronary artery disease (CAD) identification in 80 participants (40 with CAD). Finger PPG signals were processed using multilayer perceptron (MLP) and convolutional neural network (CNN) autoencoders, with performance assessed via 5-fold cross-validation. The CNN autoencoder model achieved the best results (recall 96.

View Article and Find Full Text PDF