The purpose of his task is a test of group entrainment. There are two different groups, and the aim is to maximise entrainment and tempo stability within groups, while minimising entrainment and influence between groups.


Participants are divided into two groups. In each group, a group leader has a metronome. The two metronomes are set to different (non-harmonic) tempi, e.g. 85 and 100 BPM. The game has three stages.

Stage 1:

Stage 2:

Stage 3: 

Each stage could take 1-5 minutes. 

Pilot data

Here we demonstrate some of the analyses that can be useful in analysing group synchrony. These results are from just one performance, to give a taste of the type of data that the game produces. In this game, there were 10 participants that had five accelerometers each; on their left and right wrists and ankles, and on their chests. These ten participants were divided into two teams. In addition to the "captured" participants, there were two other participants. in each team.  

For the following analysis, we have extracted a 10-second sample from approximately the middle of each phase. This allows us to see how the phases differ from each other and how e.g. synchrony within and between groups evolves over the course of the game. 

Data processing 

The acceleration data from the 50 sensors is synchronised, and re-sampled to have a constant sample rate of 100 Hz. The original data consists of accelerations along three axes, with positive and negative values depending on the direction of the acceleration. However, as the accelerometers change their orientation during the recording (e.g. a wrist sensor's X-axis can point to any direction depending on the posture of the participant), these directions are not relevant. Therefore we compute the absolute acceleration, that combines the three axes and only contains positive values. Thus, the data represents acceleration to any direction, or acceleration along the trajectory of movement.  This data is then low-pass filtered so that we can get rid of the high-frequency components that are mostly noise, e.g. generated because the accelerometers or their straps can move a bit etc. As in this analysis, we want to focus on gross movements and their synchrony, we set the threshold of the filter quite low, to 2 Hz, this should emphasise the movements that occur at the beat-level at the expense of faster movements and jitters, that might be interesting for other analyses.  

Amount of movement 

A plot of the raw accelerometer data from each phase shows that the amount of movement increases in every step as is expected. In the second phase, participants start performing their rhythm louder, which requires larger accelerations. In the third phase, participants are moving around the room, which of course results in more movement (larger accelerations) still (click on the figures to embiggen them).

Figure 1. Acceleration data from all sensors, in the three sample windows.  

Let us then take a look at an individual participant, and how their five sensors are related. 

Figure 2. Individual participant; feet 

In phases one and two, participants remain still and huddled in a circle. However, many participants were using their feet for the rhythms they were creating. Figure 2 shows data from one participant. They do a stepping motion, with acceleration peaks alternating between the two sensors. In the third phase, they are walking around the room. This seems to disturb the pattern somewhat, as especially the left foot is less consistent than in the second phase. Banking the basic beat to large body parts and large muscles is a good idea. Here we can also easily estimate the tempo from the figure. In the first and second phase, there are 7 peaks per foot, in the ten-second window. This indicates a tempo of 14 * 6 = 84 beats per minute. In the third phase, the tempo has increased somewhat, as there are now clearly 15 peaks, indicating a tempo of 90 bpm. This is of course a very rough measure, kin to estimating your HR by counting heartbeats for ten seconds and multiplying this by 6. Just as we have accurate HR-monitors, we can get a more accurate tempo estimates by using autocorrelation-based periodicity analysis.   

Figure 3: Hands and chest 

Looking at the movements of hands and chests, we can see that much of the increased movement in phase three comes actually from increased hand movements, at least for this participant. While different hands and feet tend to alternate in their movements, the chest seems to be the most consistent, and clearly periodic signal from the beginning to the end. It seems to capture the main beat and ignore the "frills". This is very clear for this participant but tends to be the case more generally. Therefore for a simple synchronisation analysis, using data from chest sensors is probably a good idea. 

(more soon...)

Figure 4. Periodicity as measured from individual sensors of one participant

Using the autocorrelation method to estimate periodicities, we see that different limbs move in slightly different tempi. Figure 4 shows the average periodicities for the three phases, for the individual participant. 

(more soon)


Lucas, G., Clayton, M., & Leante, L. (2011). Inter-group entrainment in Afro-Brazilian Congado ritual. Empirical Musicology Review, 6(2), 75–102.

Spiro, N. & Himberg, T. (2012). Empathy and Resisting Entrainment: Mapping the dimensions of pairwise rhythmic interaction. 16th Annual Symposium for Music Scholars in Finland, Jyväskylä, Finland.