Research Aims and Objectives

The main objective of the SESAME project is to conduct high-quality scientific research to produce deployable systems that have a positive and measurable impact on the training of elite athletes. This project is application-led and multidisciplinary and has the following aims:

• To understand coaching requirements by working with coaches throughout the lifetime of the project.

• To support both athletes' and coaches' training and education through:
  • capturing fine-grained data about athletes' actions and performance using wireless sensors, extensible middleware and networking platforms;
  • processing these data to extract information that is meaningful to coaches, using a newly developed biomechanical model to guide this;
  • identifying deviations between captured data and an idealised model of movement, supporting real-time corrective feedback;
  • storing the data and information in a long-term data store and performing trend analyses;
  • visualising this information in a form that is meaningful to athlete and coach.
• To explore the feasibility of correcting actions and building 'muscle memory' (the proprioceptive sense) through the use of real-time non-invasive wireless signalling.
• To compare and evaluate different athletes' actions and performance, identifying advantages and disadvantages and informing customised feedback for individuals.
• To place the UK firmly at the forefront of sporting technology.

The R&D process within the SESAME project is driven by a set of critical technical objectives. Consequently, the project activities have been structured as a set of interlinked milestones; these will be tackled by interdisciplinary interinstitutional teams, led by scientists and engineers with world-leading expertise.

Contributions

In addition to domain-specific contributions, we expect to make research advances in:

• sensor system design for monitoring human activities;
• biomechanical modelling and its symbiosis with the modelling of human activity in sensor-enabled applications;
• filtering and fusion to support quality of service/reliability requirements with high-volume sensor data capture;
• engineering of interactive sensor-driven applications;
• integration of sensor monitoring data and derived guidance information within electronic health records;
• autoconfiguration for sensor systems;