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The use of wearable technologies for the monitoring of human movement has increased considerably in the past few years, with applications to sports and other physical activities. Energy expenditure, walking and running distance, step count, and heart rate are some of the metrics provided by such devices via smart phone applications. Most of the research studies have involved validating the accuracy and reliability of the activity monitors by using the post-processed data from the device. The aim of this preliminary study was to determine if we can trust sensor data obtained from an Apple watch. This study evaluated the pre-processed data from the watch through step counting and heart rate measurements, and compared it with known validated devices (in-house 9DOF inertial sensor and Polar H10TM). Repeated activities (walking, jogging, and stair climbing) of varying duration and intensity were conducted by participants of varying age and body mass index (BMI). Pearson correlation (r > 0.95) and Bland–Altman statistical analyses were applied to the data to determine the level of agreement between the validated devices and the watch. The sensors from the Apple watch counted steps and measured heart rate with a minimum error and performed as expected.

The ability to measure and classify spin has been of great interest to cricket organizations, coaches, and athletes. While video is common, an alternative approach is to use 3D motion capture analysis with reflective spheres, which changes the aerodynamics of the ball. An instrumented cricket ball has proved to be effective in measuring high-speed spin rates using gyroscopes. In this study, an instrumented ball with a 3-axis accelerometer, 3-axis gyroscope, and 3-axis magnetometer located at the center mass of the ball was constructed. The spin rate was calculated using the magnetometer, and two spin types (off-spin and leg-spin) were classified. The sensor data was validated using motion capture. In addition, inertial measurement units (IMUs) mounted on the wrist and elbow of a wrist-spin and off-spin bowler were used to verify and validate the spin classification. The magnetometer can be effectively used in conjunction with conventional IMU sensors on the bowler’s arm to tailor training sessions by addressing deficiencies identified in a bowler’s spinning technique and to monitor their performance.

A CSMA algorithm was implemented and tested using 2.45 GHz wearable sensors on five human subjects moving in formation. The algorithm forces the nodes to repeatedly sense the carrier frequency to recognize when other nodes are in/out of coverage. The paper reports different sink nodes location on the body and the effect of transmitter power on the reliability. The results showed 100% successful wireless communications between sink nodes within coverage. Allowing this information to be available in real time have a significant importance on measuring up athlete proximity and performance with respect to time spent on one or multiple locations.

Every second year, the International Sports Engineering Association (ISEA) conducts a conference somewhere around the world. In 2018, for the first time in 18 years, the conference was held in the Southern Hemisphere in Brisbane, Queensland, Australia. Following the ISEA2016 meeting in Delft, Holland, there is no doubt that a two year period results in remarkable changes in sporting research and technology and the 2018 meeting brought together over 250 world leading experts in the field of sports engineering to present their latest research results, the latest research directions and to interact with commercial organisations who introduce the latest technologies in the market place.