Physiological Synchrony in Choir Singers and Surgical Teams

Apit Hemakom's dataset quantifies team cooperation through physiological synchrony measures. It contains respiratory and heart rate variability signals from professional choir singers and surgical teams. The data supports the evaluation of intrinsic synchrosqueezing and nested intrinsic phase synchrony algorithms.

Use Cases

• Analyze respiratory and HRV signal synchrony among bass singers using the intrinsic synchrosqueezing transform (ISC).
• Quantify large-scale cooperation trends in surgical procedures via the nested intrinsic phase synchrony (N-IPS) measure.
• Compare synchrony estimation performance of ISC against continuous wavelet transform (CWT)-based methods on bivariate physiological signals.
• Identify time-frequency localized synchrony in multivariate, nonstationary signals from team settings.

Strengths

• Data validates novel algorithms (ISC and N-IPS) against six other combinations for synchrony estimation.
• Focuses on two distinct, real-world cooperative contexts: professional choir singing and surgical procedures.
• Includes physiological signals (respiratory and HRV) relevant for studying nonverbal team coordination.

Limitations

• Unknown sample size, row count, and specific data format limit immediate assessment of analytical scope.
• Data collection context (specific choir, surgery type, participant count) is not detailed, affecting reproducibility.
• Specialized signal processing knowledge is required to utilize the proposed ISC and N-IPS measures effectively.

Provenance

Source

Dryad

Collection Method

Physiological signal recording (respiratory, heart rate variability) from choir singers and surgical teams.

Time Range

null

Freshness

null

Geography

null