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Raw data for: Biomechanical demands of percussive techniques in the context of early stone toolmaking

Cite this dataset

Macchi, Robin et al. (2021). Raw data for: Biomechanical demands of percussive techniques in the context of early stone toolmaking [Dataset]. Dryad.


Recent discoveries in archaeology and palaeoanthropology highlight that stone stool knapping could have emerged first within the genera Australopithecus or Kenyanthropus rather than Homo. To explore the implications of this hypothesis  determining the physical demands and motor control needed for performing the percussive movements during the oldest stone toolmaking technology (i.e. Lomekwian) would help. We analysed the joint-angle patterns and muscle activity of a knapping expert using three stone tool replication techniques: unipolar flaking on passive hammer (PH), bipolar flaking on anvil (BP), multidirectional and multifacial flaking with free hand (FH). PH presents high levels of activity for Biceps brachii and the wrist extensors and flexors. By contrast, BP and FH are characterised by high solicitation of forearm pronation. The synergy analyses depict a high muscular and kinematic coordination. Whereas the muscle pattern is very close between the techniques, kinematic pattern is more variable, especially for PH. FH displays better muscle coordination and conversely lesser joint-angle coordination. These observations suggest that the transition from anvil and hammer to freehand knapping techniques in early hominins have been made possible by the acquisition of a behavioural repertoire producing an evolutionary advantage that gradually would have been beneficial for stone tool production.


The dataset was collected at the University of Poitiers (PPrime Institute) and has been accepted for publication in the Journal of the Royal Society Interface.

Usage notes

Please find attached the raw EMG and kinematic data for each trial of each technique. In addtion, there are the 5th metacarpal bone marker (R_MC1) files to detect each strike cycle.

The readme file contains an explanation of each dataset and how to process them with the matlab codes avaible from this link :