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Open AccessArticle

Correlating Grip Force Signals from Multiple Sensors Highlights Prehensile Control Strategies in a Complex Task-User System

1
ICube UMR 7357, Centre National de la Recherche Scientifique (CNRS), 75016 Paris, France
2
ICube UMR 7357 Robotics Department, University of Strasbourg, 67081 Strasbourg, France
*
Author to whom correspondence should be addressed.
Bioengineering 2020, 7(4), 143; https://doi.org/10.3390/bioengineering7040143
Received: 15 October 2020 / Revised: 4 November 2020 / Accepted: 7 November 2020 / Published: 10 November 2020
(This article belongs to the Special Issue Advances in Multivariate Physiological Signal Analysis)
Wearable sensor systems with transmitting capabilities are currently employed for the biometric screening of exercise activities and other performance data. Such technology is generally wireless and enables the non-invasive monitoring of signals to track and trace user behaviors in real time. Examples include signals relative to hand and finger movements or force control reflected by individual grip force data. As will be shown here, these signals directly translate into task, skill, and hand-specific (dominant versus non-dominant hand) grip force profiles for different measurement loci in the fingers and palm of the hand. The present study draws from thousands of such sensor data recorded from multiple spatial locations. The individual grip force profiles of a highly proficient left-hander (expert), a right-handed dominant-hand-trained user, and a right-handed novice performing an image-guided, robot-assisted precision task with the dominant or the non-dominant hand are analyzed. The step-by-step statistical approach follows Tukey’s “detective work” principle, guided by explicit functional assumptions relating to somatosensory receptive field organization in the human brain. Correlation analyses (Person’s product moment) reveal skill-specific differences in co-variation patterns in the individual grip force profiles. These can be functionally mapped to from-global-to-local coding principles in the brain networks that govern grip force control and its optimization with a specific task expertise. Implications for the real-time monitoring of grip forces and performance training in complex task-user systems are brought forward. View Full-Text
Keywords: wearable biosensors; wireless technology; human grip force; motor control; complex task-user systems; expertise; multivariate data; correlation analysis; functional analysis wearable biosensors; wireless technology; human grip force; motor control; complex task-user systems; expertise; multivariate data; correlation analysis; functional analysis
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MDPI and ACS Style

Dresp-Langley, B.; Nageotte, F.; Zanne, P.; Mathelin, M.d. Correlating Grip Force Signals from Multiple Sensors Highlights Prehensile Control Strategies in a Complex Task-User System. Bioengineering 2020, 7, 143. https://doi.org/10.3390/bioengineering7040143

AMA Style

Dresp-Langley B, Nageotte F, Zanne P, Mathelin Md. Correlating Grip Force Signals from Multiple Sensors Highlights Prehensile Control Strategies in a Complex Task-User System. Bioengineering. 2020; 7(4):143. https://doi.org/10.3390/bioengineering7040143

Chicago/Turabian Style

Dresp-Langley, Birgitta; Nageotte, Florent; Zanne, Philippe; Mathelin, Michel d.. 2020. "Correlating Grip Force Signals from Multiple Sensors Highlights Prehensile Control Strategies in a Complex Task-User System" Bioengineering 7, no. 4: 143. https://doi.org/10.3390/bioengineering7040143

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