MARS, a Multi-Agent System for Assessing Rowers’ Coordination via Motion-Based Stigmergy

Abstract

A crucial aspect in rowing is having a synchronized, highly-efficient stroke. This is very difficult to obtain, due to the many interacting factors that each rower of the crew must perceive. Having a system that monitors and represents the crew coordination would be of great help to the coach during training sessions. In the literature, some methods already employ wireless sensors for capturing motion patterns that affect rowing performance. A challenging problem is to support the coach's decisions at his same level of knowledge, using a limited number of sensors and avoiding the complexity of the biomechanical analysis of human movements. In this paper, we present a multi-agent information-processing system for on-water measuring of both the overall crew asynchrony and the individual rower asynchrony towards the crew. More specifically, in the system, the first level of processing is managed by marking agents, which release marks in a sensing space, according to the rowers' motion. The accumulation of marks enables a stigmergic cooperation mechanism, generating collective marks, i.e., short-term memory structures in the sensing space. At the second level of processing, information provided by marks is observed by similarity agents, which associate a similarity degree with respect to optimal marks. Finally, the third level is managed by granulation agents, which extract asynchrony indicators for different purposes. The effectiveness of the system has been experimented on real-world scenarios. The study includes the problem statement and its characterization in the literature, as well as the proposed solving approach and initial experimental setting.

Monitoring elderly behavior via indoor position-based stigmergy

“…Hence, if the user is still in a specific position, new marks at the end of each period will superimpose on the old marks, thus increasing the intensity up to a stationary level. It can be demonstrated that the exact superimposition of a sequence of marks yields the maximum intensity level to converge to the stationary level I MAX /τ [46]. If the person moves to other locations, consecutive marks will be partially superimposed and intensities will decrease with the passage of time without being reinforced.…”

A Systematic Review of Performance Analysis in Rowing Using Inertial Sensors

“…Signal processing algorithms used on data, KNN (K-nearest Neighbours); DTW (Dynamic Time Warping); SVM (Support Vector Machine). [12] Rowing × × × [13] Rowing × × × [14] Rowing × × [15] Rowing × × × [16] Rowing…”

“…Kayaking/Canoeing Citation Sport Validation Technology [12] Rowing Optical motion capture [14] Rowing Coach qualitative assessment [17] Rowing Manual feature labelling [18] Rowing Controlled laboratory validation test [20] Rowing Force plates [25] Rowing GPS [27] Rowing GPS [30] Rowing GPS [32] Rowing Navilock-550 ERS [33] Rowing Optical motion capture [38] Rowing GPS [39] Rowing GPS [40] Rowing Optical motion capture [41] Rowing Optical motion capture [42] Canoeing Video Camera [43] Rowing GPS and stroke coach monitor [44] Rowing GPS [46] Rowing Reference measures from rowing simulator [47] Rowing Peach innovations measurement oarlock…”

Measuring Physical Activity of Older Adults via Smartwatch and Stigmergic Receptive Fields

“…The Trail captures a coarse spatio-temporal structure in a segment of the domain space (multistep sliding time window), robust to noise and variability of samples at the micro-level (Avvenuti, 2013). Subsequently, a degree of similarity can be computed comparing two trails generated with different sample streams.…”