Embodimetrics: A Principal Component Analysis Study of the Combined Assessment of Cardiac, Cognitive and Mobility Parameters

Embodimetrics: A Principal Component Analysis Study of the Combined Assessment of Cardiac, Cognitive and Mobility Parameters

There is a growing body of literature investigating the relationship between the frequency domain analysis of heart rate variability (HRV) and cognitive Stroop task performance. We proposed a combined assessment integrating trunk mobility in 72 healthy women to investigate the relationship between c...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Vol. 24; no. 6; p. 1898
Main Authors: Chellini, Andrea, Salmaso, Katia, Di Domenico, Michele, Gerbi, Nicola, Grillo, Luigi, Donati, Marco, Iosa, Marco
Format: Journal Article
Language:English
Published: Switzerland MDPI AG 01-03-2024
MDPI
Subjects:
artificial intelligence
Cognition
Cognition - physiology
Cognitive load
embodiment
Female
Heart
Heart beat
Heart rate
heart rate variability
Humans
motor control
Nervous system
Neural networks
Physiology
Principal Component Analysis
Principal components analysis
Range of motion
Sensors
Stroop task
Stroop Test
Task Performance and Analysis
Wearable computers
Stroop task
embodiment
heart rate variability
motor control
artificial intelligence
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Summary:There is a growing body of literature investigating the relationship between the frequency domain analysis of heart rate variability (HRV) and cognitive Stroop task performance. We proposed a combined assessment integrating trunk mobility in 72 healthy women to investigate the relationship between cognitive, cardiac, and motor variables using principal component analysis (PCA). Additionally, we assessed changes in the relationships among these variables after a two-month intervention aimed at improving the perception-action link. At baseline, PCA correctly identified three components: one related to cardiac variables, one to trunk motion, and one to Stroop task performance. After the intervention, only two components were found, with trunk symmetry and range of motion, accuracy, time to complete the Stroop task, and low-frequency heart rate variability aggregated into a single component using PCA. Artificial neural network analysis confirmed the effects of both HRV and motor behavior on cognitive Stroop task performance. This analysis suggested that this protocol was effective in investigating embodied cognition, and we defined this approach as "embodimetrics".
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ISSN:1424-8220
1424-8220
DOI:10.3390/s24061898