# Extracting Sensory Preferability from Motor Streams

## TL;DR

This study shows how sensory preferences can be inferred from walking patterns under different conditions.

## Contribution

A novel signal processing method is introduced to extract sensory preferences from motor data.

## Key findings

- Variations in walking patterns revealed how participants perceived sensory inputs.
- Optimal sensory-motor system performance was identified under specific audio and visual conditions.
- Results suggest that contextual sensory inputs can be extracted from movement data.

## Abstract

(1) Background: Based on the reafference principle, our system creates an efferent signal copy to distinguish external inputs from our activities in the afferent signal. According to this principle, sensory and motor information from the outside world travel together from the periphery to the brain. (2) Methods: This work introduces signal processing methods that extract contextual sensory preferences from motor streams. Speed and acceleration data were collected as participants walked under different conditions: in silence (with open and closed eyes), while listening to two different songs (each with open and closed eyes), and finally while walking to their favorite song. Ten individuals completed a total of seven conditions. (3) Results: Variations in the walking patterns of each participant were identified, revealing the sensory inputs they perceived. The results also indicated the audio and visual conditions that optimized the participant’s sensory–motor system performance. (4) Conclusions: The outcomes suggest that we can extract from motor stream particulars that go beyond an individual’s movement qualities and toward the contextual sensory inputs accompanying the movement data, even when participants execute the very same task of walking.