Dynamic Monitoring Reveals Motor Task Characteristics in Prehistoric Technical Gestures

Abstract

Reconstructing ancient technical gestures associated with simple tool actions is crucial for understanding the co-evolution of the human forelimb and its associated control-related cognitive functions on the one hand, and of the human technological arsenal on the other hand. Although the topic of gesture is an old one in Paleolithic archaeology and in anthropology in general, very few studies have taken advantage of the new technologies from the science of kinematics in order to improve replicative experimental protocols. Recent work in paleoanthropology has shown the potential of monitored replicative experiments to reconstruct tool-use-related motions through the study of fossil bones, but so far comparatively little has been done to examine the dynamics of the tool itself. In this paper, we demonstrate that we can statistically differentiate gestures used in a simple scraping task through dynamic monitoring. Dynamics combines kinematics (position, orientation, and speed) with contact mechanical parameters (force and torque). Taken together, these parameters are important because they play a role in the formation of a visible archaeological signature, use-wear. We present our new affordable, yet precise methodology for measuring the dynamics of a simple hide-scraping task, carried out using a pull-to (PT) and a push-away (PA) gesture. A strain gage force sensor combined with a visual tag tracking system records force, torque, as well as position and orientation of hafted flint stone tools. The set-up allows switching between two tool configurations, one with distal and the other one with perpendicular hafting of the scrapers, to allow for ethnographically plausible reconstructions. The data show statistically significant differences between the two gestures: scraping away from the body (PA) generates higher shearing forces, but requires greater hand torque. Moreover, most benchmarks associated with the PA gesture are more highly variable than in the PT gesture. These results demonstrate that different gestures used in ‘common’ prehistoric tasks can be distinguished quantitatively based on their dynamic parameters. Future research needs to assess our ability to reconstruct these parameters from observed use-wear patterns.

Influence of force and duration on stone tool wear: results from experiments with a force-controlled robot

“…These values are comparable with those measured during scraping experiments involving humans (Pfleging et al, 2015). The force profiles of typical trials are illustrated in Figure 3.…”

Affordable gait analysis using augmented reality markers

“…The achievable accuracy of marker tracking in their results is significantly higher than with the more widespread ARtoolkit framework [19,20], which they graphically present [19]. Another experimental validation of Apriltag’s marker tracking accuracy has been conducted by Pfleging et al with a motion capture system [12]. They found 4.3 (3.2) mm position error and 1.83° (1.77°) orientation error for a 58 mm side length Apriltag marker in a 0.8–1.2 m distance at 1280×720 camera resolution.…”

“…It can identify rotations and translations (3D kinematics) of an AR marker relative to the camera focus point and the image plane by how the corners of the known geometry marker appear on the recorded image. Compared to continuously drifted or zero corrected IMU-s, the 6 degree of freedom tracking of AR markers make them possible to track the absolute position of external objects [12] and body segments if attached to them. Compared to stereophotogrammetry based alternatives [10], AR marker based tracking can work with one camera, although in this case the movement direction can be limited (e.g.…”

Rethinking Use-Wear Analysis and Experimentation as Applied to the Study of Past Hominin Tool Use

“…In order to test the identified patterns, these studies should involve both standardized and archeological-replica tools, where the latter can be tested against artifacts from the archeological record. The experimental design should not only involve the replication of potential past technologies, but human variability can also be tested and measured if technologies such as multi-sensor systems for gesture recognition are included (Key 2016;Pfleging et al 2015;Williams-Hatala et al 2018). Analysis of samples used in the third generation experiments can be conducted blind, meaning that the specific use of each tool will be unknown prior to data collection and the interpretation will be done via the combination of statistical modeling and traditional use-wear observations (Evans 2014;Rots et al 2016).…”

“…From our understanding and implicit for our definition of functional analysis, data obtained by use-wear studies are a significant contributor to understanding tool use. At the same time, other aspects such as the production, durability, and efficiency of a given tool are crucial for addressing questions related to the understanding of fundamental mechanisms within the evolution of human behavior, such as cognitive, symbolical, and stylistic traits (Braun et al 2008;Domínguez-Rodrigo and Pickering 2017;Pfleging et al 2015).…”