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.
References
[1]
Baudouin, A.; Hawkins, D. Investigation of biomechanical factors affecting rowing performance. J. Biomech. 2004, 37, 969–976.
[2]
Fok, C.L.; Balasubramanian, D.; Tamola, M.; Lu, C. TinyCoxswain: Using a Sensor Network to Enhance Crew Performance. Technical Report; Washington University in St. Louis School of Engineering & Applied Science, Department of Computer Science & Engineering: St. Louis, MO, USA, 2004.
Mpimis, A.; Gikas, V. Monitoring and evaluation of rowing performance using mobile mapping data. Arch. Photogramm. Cartogr. Remote Sens. 2011, 22, 337–349.
[5]
Cirmirakis, D.; Pollard, J. Rowing Optimisation. Proceedings of the IEEE International Workshop on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS 2009), Cosenza, Italy, 21–23 September 2009; pp. 159–161.
[6]
Kleshnev, V. Rowing Biomechanics: Technology and Technique. Technical Report; Biorow Ltd.: Slough, Berkshire, UK, 2004.
Abbate, S.; Avvenuti, M.; Light, J. MIMS: A minimally invasive monitoring sensor platform. IEEE Sens. J. 2012, 12, 677–684.
[9]
Abbate, S.; Avvenuti, M.; Bonatesta, F.; Cola, G.; Corsini, P.; Vecchio, A. A smartphone-based fall detection system. Pervasive Mob. Comput. 2012, 8, 883–899.
[10]
Fortino, G.; Gravina, R.; Guerrieri, A. Agent-oriented Integration of Body Sensor Networks and Building Sensor Networks. Proceedings of the 2012 Federated Conference on Computer Science and Information Systems, (FedCSIS), Wroc？aw, Poland, 9–12 September 2012; pp. 1207–1214.
[11]
Patel, R.; Jain, D. A Multiagent System for Distributed Sensor Networks. Proceedings of the International Conference on Advances in Computing, Control, Telecommunication Technologies (ACT'09), Trivandrum, Kerala, India, 28–29 December 2009; pp. 823–826.
[12]
Rajkovi？, Z.M.; Ili？, D.B.; Mrdakovi？, V.D.; Mitrovi？, D.M.; Jankovi？, N.N. Evaluation of learning rowing technique in a twelve-oared school boat gallery. Facta Univ. Series Phys. Educ. Sport 2011, 9, 329–347.
[13]
Llosa, J.; Vilajosana, I.; Vilajosana, X.; Navarro, N.; Surinach, E.; Marquès, J.M. REMOTE, a wireless sensor network based system to monitor rowing performance. Sensors 2009, 9, 7069–7082.
[14]
Bettinelli, S.; Placido, A.; Susmel, L.; Tovo, R. An integrated data acquisition system for on-water measurement of performance in rowing. Strain 2010, 46, 493–509.
[15]
Dubus, G. Evaluation of four models for the sonification of elite rowing. J. Multimodal User Interfaces 2012, 5, 143–156.
[16]
Vernon, D.; Metta, G.; Sandini, G. A survey of artificial cognitive systems: Implications for the autonomous development of mental capabilities in computational agents. Trans. Evol. Comput. 2007, 11, 151–180.
[17]
Heylighen, F.; Gershenson, C. The meaning of self-organization in computing. IEEE Intell. Syst. Section Trends Controv. Self-Organ. Inf. Syst. 2003, 18, 72–75.
[18]
Barron, P. Using Stigmergy to Build Pervasive Computing Environments. Ph.D. Thesis, University of Dublin, Trinity College, Dublin, Ireland, 2005.
[19]
Cimino, M.G.C.A.; Marcelloni, F. Autonomic tracing of production processes with mobile and agent-based computing. Inf. Sci. 2011, 181, 935–953.
[20]
Rao, V.S. Multi-agent distributed data mining: An overview. Int. J. Rev. Comput. 2010, 3, 83–92.
[21]
Van Dyke Parunak, H. A Survey of Environments and Mechanisms for Human-Human Stigmergy. In Environments for Multi-Agent Systems II; Weyns, D., Dyke Parunak, H., Michel, F., Eds.; Springer: Berlin/Heidelberg, Germany, 2006; Volume 3830, pp. 163–186.
[22]
Shi, K.; Qin, X. Stigmergy Based Autonomous Shop Floor Control with Wireless Sensor Networks. Proceedings of the 2011 9th IEEE International Conference on Industrial Informatics (INDIN), Caparica, Lisbon, Portugal, 26–29 July 2011; pp. 375–380.
[23]
Karuna, H.; Valckenaers, P.; Kollingbaum, M.; van Brussel, H. Multi-agent coordination and control using stigmergy. Comput. Ind. 2004, 53, 75–96.
[24]
Castellano, G.; Cimino, M.G.C.A.; Fanelli, A.M.; Lazzerini, B.; Marcelloni, F.; Torsello, M.A. A collaborative situation-aware scheme based on an emergent paradigm for mobile resource recommenders. J. Ambient Intell. Hum. Comput. 2013, 4, 421–437.
[25]
Mattes, K.; Schaffert, N. A new measuring and on water coaching device for rowing. J. Hum. Sport Exerc. Univ. Alicant. 2010, 5, 226–239.
[26]
Tessendorf, B.; Gravenhorst, F.; Arnrich, B.; Troster, G. An IMU-based Sensor Network to Continuously Monitor Rowing Technique on the Water. Proceedings of the 2011 Seventh International Conference on Intelligent Sensors, Sensor Networks and Information Processing, (ISSNIP), Melbourne, Australia, 6–9 December 2011; pp. 253–258.
[27]
Schaffert, N.; Mattes, K. Acoustic Feedback Training in Adaptive Rowing. Proceedings of the 18th International Conference on Auditory Display (ICAD2012), Atlanta, GA, USA, 18–22 June 2012; pp. 83–88.
[28]
Avvenuti, M.; Casella, A.; Cesarini, D. Using gait symmetry to virtually align a triaxial accelerometer during running and walking. Electron. Lett. 2013, 49, 120–121.
[29]
McGookin, D.; Brewster, S. Earcons. In The Sonification Handbook; Hermann, T., Hunt, A., Neuhoff, J.G., Eds.; Logos Publishing House: Berlin, Germany, 2011. Chapter 14; pp. 339–361.
[30]
Pedrycz, W. Knowledge-Based Clustering: From Data to Information Granules; John Wiley & Sons, Inc: Hoboken, NJ, USA, 2005.
[31]
Pedrycz, W.; Gacek, A. Temporal granulation and its application to signal analysis. Inf. Sci. 2002, 143, 47–71.
[32]
Dombois, F.; Eckel, G. Audification. In The Sonification Handbook; Hermann, T., Hunt, A., Neuhoff, J.G., Eds.; Logos Publishing House: Berlin, Germany, 2011. Chapter 12; pp. 301–324.
