Correlating grip force signals from multiple sensors highlights prehensile control strategies in a complex task-user system

TL;DR

This study analyzes grip force signals from multiple sensors to understand prehensile control strategies during complex tasks, revealing skill-specific patterns and implications for real-time performance monitoring.

Contribution

It introduces a comprehensive statistical approach to correlate grip force profiles from multiple sensors, linking them to neural coding principles and skill levels in complex task execution.

Findings

Skill-specific covariation patterns in grip force profiles

Correlation patterns reflect neural coding principles

Implications for real-time grip force monitoring and training

Abstract

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 noninvasive 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 handed exper, 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 Tukeys detective work principle, guided by explicit functional assumptions relating to somatosensory receptive field organization in the human brain. Correlation analyses in terms of Person Product Moments reveal skill specific differences in covariation 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.