Grants Awarded

YEAR 2018

In 2018, Serious Games Asia LP was the solutionist with the The Logistic Institute Asia Pacific @ National University of Singapore (NUS-TLIAP). Together, we won the InnovPLUS Flame Award 2018. The winning solution was to address the challenges of training personal in responsive humanitarian logistics in the critical and early phases of disaster relief operations. We built a 72-hour game to demonstrate how a game authoring tool kit could help non game developers to reduce time  and resources in creating a game-based solution. Through the game, learners could strategically pre-position stockpiles of relief supplies for fastest response. Learners will learn quickly on how to reduce the risk of supply starvation and increase efficiency of the logistics of supply. Through the simple to use game authoring tool kit, trainers could code new mitigation strategies and gamify scenarios with an emphasis on learning points via role play. This is the 72-Hour Game Trailer:

Alongside with the 72-Hour Game, we developed and established our own game analytics engine – GamesTrax. GamesTrax’s building blocks are based on the descriptive and predictive analytics of learner’s decision-making behaviour. Our analytics tasks are derived from:

      1. Extraction of learners’ decision makings from the game log;
      2. Determine the evolution of the key variables through the game stages (phases);
      3. Feeding the decision makings and variables for analysis.

The 72-Hour game log provided us with a fixed structured data. With the understanding of the game logics and documentation, we would then extract various key entries from the game log. There are four types of actions we observed under the “actions” tag (the game recorded as “actions” though it contains multiple actions), with different “type” values:

    • Mini game Control Room: Records actions that the learner has taken (e.g. making choices with each agent)
    • Conversation: Records the conversation (generally initiated by the agents)
    • Inventory Received: Records the result obtained from the “mini game” or “conversation”; the analytics engine need to resolve and link them with the appropriate actions from the “mini game” or “conversation”
    • Menu Minigame: To take note of the “time” to identify the start time of the “mini game” or “conversation”

After the game log has been aligned with different stages played, the actions taken and results are then identified. The analytics engine further processes the actions in each section before labeling the actions. Based on the processed data, the analytics engine compute the changes of the variables throughout the game. Specifically, it records: budget, LSK, LSK_out, LSK_in, and transporation_capacity. The analytics engine then look for the “item” received from the “inventory received”. With the process described above, the analytics engine has converted a weakly structured data to be a structured data suitable for analytics and machine learning algorithms. By employing decision tree algorithm, the break points that mainly lead to the success or failure has been identified. We then proceeded onto the next stage of predictive analysis. We could then develop a deep learning model (LSTM) to predict the learner’s choices in the subsequent phases based on the learner’s choices and game metrics for the current and all previous stages. The deep learning model takes in the consideration of the evolutions of the features mentioned above across stages and the produced prediction could be interpreted by the game logic and adjust the game mechanics accordingly.

This resulted in the “break point” that could be converted into game triggers to increase game difficulty or decrease game difficulty as needed. This adaptive feature is now the intellectual property of Serious Games Asia.


Year 2019

In 2019, together with SingHealth and Playware Studio, Serious Games Asia won the Learning Technology Adoption Grant (LTAG). The grant aims to develop 10 Training & Assessment Games for SingHealth.

The main purpose is to transform our healthcare professionals into a modern skills-based workforce with clear objectives to (1) reduce training cost, (2) increase workforce productivity, (3) improve patient safety records and (4) establish a national standard for the deployment of immersive media training & development practices.

With the rising demand in the level of healthcare services provided for better patient care and also the importance of patient safety, SingHealth has been investing ample amount of funding, manpower and resources to ensure healthcare professionals are well-trained.  These training are not only labour-intensive but also resource intensive.  For instance, a typical part-task training using basic simulators would cost about $5,000 to $10,000 per day to train healthcare professionals such as doctors and nurses.  For more sophisticated training involving high fidelity simulators or even cadavers (for surgical), the cost of training per day could range from $30,000 to $50,000.  All these trainings would require many faculties to be present; some training would typically require 1 instructor to 3-4 trainees.  Given the huge financial requirement as well as manpower needs, we would not be able to carry repetitive training for the participants on regular basis.  Hence, we would need to source for additional or alternative training methodology to supplement the current training with clear objectives to (1) reduce training cost, (2) increase workforce productivity, (3) improve patient safety records and (4) establish a clear pathway to deploy such immersive media related training tools.


Year 2020

In 2020, Serious Games Asia partnered with Singapore General General Hospital and National University of Singapore to clinch the InnovPLUS 2020 Flame Awards. Our solution was to develop an intravenous cannulation training and assessment kit leveraging on game technology, microfluidic sensors and state-of-the art 3D printing to assesses the dexterity skill of the learner. This integrated approach serves as an innovative virtual medical training application to aid practitioners in training and mastering a task skill.

Objective 1:    Develop a “realistic training and assessment solution” for mastering the insertion of an intravenous (IV) cannulation into a patient. The in-situ assessment must be balanced by positive learner’s experience, which may be reflected by the engagement of the learner.

Objective 2:   Develop an affordable, highly accurate, mobile, multi-finger interaction, wearable force-feedback device in the form of a glove embedded with microfluidic pressure sensors. The glove will allow the learner to experience a force feedback through interacting and manipulating the cannula with the 3D printed arm that trigger realistic feedback from the patient avatar. This will help to augment the experience and learning outcome of the learner.

Objective 3:    To create a real-time feedback mechanism in the form of a virtual patient avatar. Concurrently, we will also create a dashboard using the player (learner) game play data for the learner and the trainer.

The innovation and expected impact in this solution is in the following areas:

    1. The use of microfluidic sensors in a glove that has the capability to detect changes in movements and finger pressure. This set of sensory gloves will be a prototype application for other future trial applications in the medical industry. The sensors could be woven into clothing such as socks, knee guards and other medical items such as bandages.
    2. The tracking of real world actions using sensory gloves (not just creating virtual reality immersive effects) translated into virtual environment for the medical industry will open up a new avenue for more real life simulation training.
    3. The development of a game-based assessment dashboard for a simulation game will establish the standards and protocols needed in the medical education domain. Such game-based assessments with massive un-curated data derived from behavioural outcome will allow us to establish models for skills-based competency assessment.

The outcome of this solution will have impact stretching beyond the healthcare domain. The pedagogical approach applies to other industries specifically targeting at lifelong training and development.


Serious Games Asia partnered with Singapore University for Technology and Design (SUTD) and FX Media Internet to clinched the second InnovPLUS 2020 Flame Awards. Our solution was address the issues in the training and education industry.

In the ‘new COVID-19 normal’ world, the delivery of knowledge, skills and attitudes in tertiary and adult education are commonly conducted via online learning management systems. However, these competency-based lessons are uninteresting and do not yet have a cheat-proof, structured assessment system that is needed for tertiary-level home-based learning at such critical times.

Singapore needs to have a better strategy in developing online lessons, leveraging on simulation game and virtual reality technology that could adapt quickly to the ever-changing environment with an updated, well-structured assessment modality. We have to go beyond standard online synchronous lectures and asynchronous video recordings, as they are exhausting and un-motivating for the learner. We are also limited in our mode of online assessment that currently includes MCQs and open-ended take home assignments.

An open simulation games and VR content platform is proposed for developing, hosting and deploying single and multiplayer games with a scalable game scenario builder and have the capability to leverage on the aggregated learner game log data for analyses that lay the foundation for machine learning of learner’s learning pattern using longitudinal datasets to develop adaptive and personalised learning algorithms.

The Serious Games-Training and Assessment Platform (TAP) is a single platform for deploying simulation-game training and game-based assessment solutions using technology such as virtual reality and artificial intelligence. The first layer of the technology consist of a game-authoring tool kit which allows non-technical personal to create and assemble a game-based assessment as desktop games or VR games. On top of the solution builder is the data analytic layer (GamesTrax) which will enable quick visualisation of the individual player’s results. With a targeted and comprehensive data pool, we would then be able to create innovative algorithm for personalised learning journeys for our  healthcare practitioners.

The Serious Games-Training and Assessment Platform will also be set up for establishing rigorous research and evidence-based methodology in other relevant technological advancement such as haptic gloves development leveraging on the virtual world in our simulation games. There will be many innovative digital training & development solutions as more stakeholders come on board to disrupt and improve this technology.

The Impact created are as follows:

    1. Being able to pool good educational games and VR content software together will allow teachers access to a large pool of resources which they may not have known or have access to. The cost savings from a shared economy will kick in. Cost savings in terms will duplicated purchases will also be eradicated. Curated content will also allow teachers to have confidence in the tools offered. Implementation cost could also be reduced in terms of shared hardware such as head mounted display, etc.
    2. All simulation games and VR content software are launched independently from LMS. This would not be able to combine the player (learner) profile and experience data with the game data. With a collection of games, the analysis of each learner would be much more in-depth and accurate. There is no necessity to on board the student (learner) multiple times when they use different software solutions.
    3. The consolidation of data into a single database will allow for machine learning algorithm to be built using pattern recognition technology reflected by the players’ (learners) activities. This will eventually lead to developing models for personalised learning pathways. In this project, we could already develop an assessment system that analyses the student data as they are using the simulation game and/or VR content software in real-time, allowing teachers to rapidly address difficulties individual students are encountering.