Recent Trends for Auditory-Motor Synchronization (AMS) and Related Development

[featured_image]
  • Version
  • Download 78554
  • File Size 0.00 KB
  • File Count 1
  • Create Date March 21, 2025
  • Last Updated March 21, 2025

Recent Trends for Auditory-Motor Synchronization (AMS) and Related Development

Hiroshi Bando1,2iD*, Akiyo Yoshioka2, Yu Nishikiori2
1Medical Research/Tokushima University, Tokushima, Japan
2Integrative Medicine Japan (IMJ), Shikoku Island division, Tokushima, Japan

Corresponding Author: Hiroshi Bando ORCID iD
Address: Tokushima University /Medical Research, Nakashowa 1-61, Tokushima 770-0943, Japan.
Received date: 19 January 2025; Accepted date: 15 February 2025; Published date: 22 February 2025

Citation: Bando H, Yoshioka A, Nishikiori Y. Recent Trends for Auditory-Motor Synchronization (AMS) and Related Development. J Health Care and Research. 2025 Feb 22;6(1):12-15.

Copyright © 2025 Bando H, Yoshioka A, Nishikiori Y. 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.

Keywords: Auditory-Motor Synchronization, Primary Motor Cortex, Intrinsic Clock, Rhythmic Auditory Stimulation, Artificial Intelligence

Abbreviations: AMS: Auditory-Motor Synchronization; M1: Primary Motor Cortex; RAS: Rhythmic Auditory Stimulation; AI: Artificial Intelligence

Abstract

Auditory-Motor Synchronization (AMS) has recently attracted attention. It coordinates motor actions with rhythmic auditory stimuli, applying to dancing, running, playing music, communicating, and conversations. Several brain regions are involved in this mechanism, such as the primary motor cortex (M1), supplementary motor area (SMA), premotor area (PMA), and basal ganglia. Temporal prediction and timing control are necessary for AMS execution. Medical applications include Parkinson’s disease (PD) and stroke cases for rhythmic auditory stimulation (RAS). Future developments in collaboration with artificial intelligence (AI) are expected. AI can learn human AMS patterns and allow robots to synchronize similar movements with more natural micro-movements.