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Team members

Declan Ng Kai Rei (EPD), Brandon Yeo Yun Zhang (EPD), Chai Chi Yee Alysia (EPD), Kingsley Tay Zi Hao (ESD), Wong Yong Xiang (ESD), Tan Hui Shin (ESD)


Ying Xu, Mohan Rajesh Elara, Nuno Ribeiro

Writing Instructors:

Wong Yoke Chee Susan

Teaching Assistant:

Cheryl Low Rui Min

Introducing Team ReVitalise

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To preserve the value and hence, exceptional talent of our many sporting athletes during their peak performing years, our team has found a solution in forecasting an athlete's competition readiness through performance metrics tracking and machine-learning.

By using data to understand the performance of our athletes, we can predict how susceptible the athlete is to injury during competitions and trainings.


Our partnerships

Through the help from the VAS (Volleyball Association of Singapore), our solution was made achievable by identifying and collecting optimum performance metrics from the best Volleyball players Singapore has to offer.


"The biggest problem we have for our high-performance pathway is the lack of quantitative data to improve player's performance and prevent injuries."

- Chee Kok Leong, VAS President High Performance


"Many of our athletes were sidetracked by injury leading up to the SEA Games 2019 which resulted in the team not being able to perform well during the competition itself."

- Chloe Ang, VAS General Manager


Solution Timeline

Our solution is achieved in progressive stages.

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Firstly, we will enhance our performance metric tracking capabilities through sports wearables.

Secondly, we will use the data collected to create a personalised platform for athletes to improve their performance and technique.

Finally, by understanding the optimum performance of the athlete through the data, Coaches can use our product to determine their  athlete's competitive readiness.



From our interviews with VAS management. We concluded that our solution should use an athlete's jump related metrics as a means to evaluate their performance and potential injuries. In our solution, we evaluated two types of basic jumps used in Volleyball: The block jump and the approach jump. 

A jump from a stationary position is defined as a block jump as shown below.

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When a player quickly strides forward and jumps, this is defined as an approach jump. as shown below




Our solution

Our team would be using a sports wearable to collect important metrics pertaining to an athlete's jump. This data is processed by through machine-learning algorithms, producing qualitative results that is displayed through our mobile application.


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Wearable Product Flyer

The RejumpC is the data collection wearable in our solution.

product flyer


Product Demonstration Video

In this opening video, we will introduce the MVP of our sports wearable RejumpC and how it interacts with our two main stakeholders: Coaches and Volleyball athletes.


As shown in the video, the athlete would be required to don the wearable during training sessions and competitions. Throughout the duration of the events, important jump metrics will be constantly streamed into our database through an active WiFi connection.


Product integration

While RejumpC collects vital information and uploads it to a cloud, the data collected is processed through the use of machine learning models to produce constructive comments and ratings that summarise the athlete's performance and competition readiness. 

The function first quantifies jumps based on peak acceleration data. Next, the data points are used to derive kinematic variables for estimation of jump height. Last, these data points help to classify the jumps into block jumps and approach jumps.

data processing


After evaluating the data related to these jumps, the relevant output would be sent to our mobile applications


Application Demonstration Video

Product Poster





student Declan Ng Kai Rei Engineering Product Development
student Brandon Yeo Yun Zhang Engineering Product Development
student Chai Chi Yee Alysia Engineering Product Development
student Kingsley Tay Zi Hao Engineering Systems and Design
student Wong Yong Xiang Engineering Systems and Design
student Tan Hui Shin Engineering Systems and Design
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