Sports Technology

Technology plays an important part in monitoring and developing the health and athleticism of New Zealanders. The Sports Technology Research Group aims to develop and review applied technology and processes that improve sporting and human performance.

The research group brings together experts from different disciplines - engineers, designers, computer scientists, performance analysts, and sport and exercise scientists - to develop and validate innovative products and services for the high-performance athlete, the high-end recreational sports as well as the fitness and wellbeing markets.

Our activities and students

The Sports Technology Research Group aims to become a driver of innovation and to provide a research platform fostering intense collaboration. This would result in significant advancements and contributions placing SPRINZ as a front runner in innovation research and the number one research provider and developer of innovative products and services for the high-performance athlete, the high-end recreational sports enthusiast as well as the fitness and wellbeing seeker.

Research activity involves a number of products being developed for a range of applications. This involves design, research, consultation and the development of ideas and concepts, which articulate into models for trial use and application.

  • Chris Juneau (Master's candidate)
    Topic: Plantarflexion assessment using load cell technology: Reliability and Limb Asymmetry
    Supervisors: Jono Neville, John Cronin
  • Trey Job (MPhil candidate)
    Topic: The Acute Effects of Wearable Resistance on Throwing Velocity in Baseball Pitchers
    Supervisors: Jono Neville, Frank Bourgeois, Micheál Cahill
  • Matoko Noudehou (Master's candidate)
    Topic: The Relationship Between Single-Leg Lateral Jump Ability and Performance in Single-Leg Lateral Change of Direction Tasks in Youth Athletes
    Supervisors: Jono Neville, Frank Bourgeois, John Cronin
  • Joey McGrath (PhD candidate)
    Topic: Can a low-cost IMU in combination with machine learning accurately measure fast-bowling workload in cricket?
    Supervisors: Jono Neville, Tom Stewart, John Cronin
  • Ben Reynolds (PhD candidate)
    Topic: Can a greater understanding of workload in netball be achieved through activity detection algorithms and machine learning techniques?
    Supervisors: Jono Neville, Tom Stewart, John Cronin

Research group leader

  • Jono Neville

Members