Dresp-Langley, B.; Nageotte, F.; Zanne, P.; Mathelin, M. Correlating Grip Force Signals from Multiple Sensors Highlights Prehensile Control Strategies in a Complex Task-User System. Bioengineering2020, 7, 143.
Dresp-Langley, B.; Nageotte, F.; Zanne, P.; Mathelin, M. Correlating Grip Force Signals from Multiple Sensors Highlights Prehensile Control Strategies in a Complex Task-User System. Bioengineering 2020, 7, 143.
Dresp-Langley, B.; Nageotte, F.; Zanne, P.; Mathelin, M. Correlating Grip Force Signals from Multiple Sensors Highlights Prehensile Control Strategies in a Complex Task-User System. Bioengineering2020, 7, 143.
Dresp-Langley, B.; Nageotte, F.; Zanne, P.; Mathelin, M. Correlating Grip Force Signals from Multiple Sensors Highlights Prehensile Control Strategies in a Complex Task-User System. Bioengineering 2020, 7, 143.
Abstract
Biosensors and wearable sensor systems with transmitting capabilities are currently developed and used for the monitoring of health data, exercise activities, and other performance data. Unlike conventional approaches, these devices enable convenient, continuous, and unobtrusive monitoring of a user’s behavioral signals in real time. Examples include signals relative to hand an finger movement/pressure control reflected by individual grip force data. As will be shown here, these 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. On the basis of thousands of sensor data from multiple sensor locations, individual grip force profiles of an task expert, a trained user and a highly proficient user (expert) performing an image-guided and robot-assisted precision task with the dominant or the non-dominant hand are analyzed in several steps following Tukey’s “detective work” approach. Correlation analyses (Person’s Product Moment) reveal skill-specific differences in individual grip force profiles across multiple sources of variation, functionally mapped to the somatosensory brain networks which ensure grip force control and its evolution with control expertise. Implications for the real-time monitoring of individual grip force profiles and their evolution with training in complex task-user systems are brought forward.
Keywords
wearable biosensors; wireless technology; human grip force; motor control; complex task-user systems; expertise; multivariate data; correlation analysis; functional analysis
Subject
Engineering, Automotive Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
