Success Story High Tech
Intel Rolls Out State-of-the-Art IoT Sports Sensors
Intel is one of the world’s largest semiconductor chip makers, on the leading edge of technological innovation and involved in many aspects of the Internet of Things (IoT) including health, fitness, and sports.
Intel created a new line of sophisticated sensors for sports equipment that can collect and analyze data for various use cases. The coin-sized sensors can be mounted on different types of equipment such as cricket and baseball bats to capture real-time data on a number of different players’ bat swings and motions to improve training and skill development.
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The potential applications were unlimited, but the sensors also presented some connectivity challenges that Intel had not anticipated.
Apexon began working with Intel in 2014 and was a trusted digital partner. Intel brought Apexon in to assess the technical requirements of the project and propose a solution – one that would enable the exceptional data access and user experience required to deliver on the potential applications for the sports sensors.
Via real-time testing and optimization of sensors
With new, market-leading product capabilities
In the set-up of physical infrastructure
Accelerating the delivery of new digital initiatives with confidence
Creating the infrastructure and foundation to scale digital initiatives
Enable digital adoption in a quick, and agile manner
Understanding the data access and user experience requirements, Apexon kicked off a comprehensive digital engineering initiative from strategy and development through QE.
Build digital infrastructure and foundation for enterprises to scale
Working with Intel’s team, Apexon conducted field testing at multiple regional levels and championship trophy games to validate sensor data accuracy. The results were displayed during the live telecast.
Leverage data engineering to make strategic decisions and get digital right every time
Apexon installed a cloud application server to store data for future analysis, enable visualization for match or practice sessions, and user registration and mapping.
Intel is a proven innovator when it comes to creating next-generation technology infrastructure solutions. However, the new sports sensors introduced some challenges even a technology thought leader like Intel had not encountered before.
The company needed to find a way to provide seamless data access and user experience for the sports sensors to deliver on the many potential spots use cases.
Specifically, Intel needed help:
Conceptualizing and developing an application which would allow coaches and players to analyze and collaborate digitally via smartphones and IoT devices. The solution needed to monitor player performance continuously and provide feedback against predefined goals.
Developing a solution designed to be used during live events and broadcast on air for TV viewers.
Optimizing algorithms to improve sensor data accuracy.
Setting up the right physical infrastructure to simulate real-life sporting environments to test the sensors under different conditions.
Capturing and visualizing key data elements for intuitive use by players, coaches, and fans.
Accessing the right resources to execute various sports motions and capture real-time data about them.
Intel engaged Apexon as a digital strategy and execution partner based on its extensive expertise in the product engineering and management of the latest innovations in digital, IoT and wearables.
With a thorough understanding of Intel’s needs and expectations, the Apexon team kicked off a comprehensive digital engineering initiative. The engagement was divided into two parts: Development and Quality Engineering.
The scope of Apexon’s development efforts encompassed all functional flow and requirements, device drivers and algorithms, device interface, mobile application UI and UX, application logic, user dashboard, smart videos, performance analysis, user profiles and management, and reporting. The coin-sized device includes various sensors and a microcontroller with BLE capabilities.
The Apexon team developed drivers for the sensors to receive data and run algorithms on the received data to make a meaningful analysis. The data is wirelessly transmitted to a companion application on a smartphone. It is also stored in the sensor when it is not connected to a smart device.
Key development deliverables included:
Firmware and algorithms for the sport sensorThis enabled the ability to receive data from the device sensors, detect and record key activities such as shots based on a noise threshold, run algorithms to calculate parameters such as shot data, and send that data to the companion application on both Android and iOS.
Android and iOS companion applicationsThese applications receive, store and display the shot data from the sensor firmware, sync it with the cloud application server, enable visualization with a 3D avatar and control different user modes such as match and practice.
Cloud application serverThe application server stores data for future analysis, enables visualization for match or practice sessions, and enables user registration and mapping.
Discovery & PlanningApexon defined an end-to-end strategy that outlined the most important test scenarios and parameters to be validated.
Ground-Truth Validation Laboratory Setup Apexon set up a lab and a ground-truth test bench – essentially an indoor cricket pitch. The pitch was surrounded by motion capture and high-speed video cameras to simulate real-time scenarios for developing and validating the sensors.
Field Testing Working with Intel’s team, Apexon conducted field testing at multiple regional levels and championship trophy games to validate sensor data accuracy. The results were displayed during the live telecast.
Coverage, Execution & Reporting The optical motion capture system was tweaked to capture real-time Cricket bat swing data.
3D Motion Capture This enabled the capture of movement of the selected object from all angles to get a better idea of the 3D angles.
Data Analysis and Algorithm Optimization Data was captured for various parameters such as hand speed, hip speed, swing plane, hit angle, etc.
Statistical Models Created to analyze the data and alter the algorithms to improve the accuracy of the sensors.
