SLIIT Conference and Symposium Proceedings

Now showing 1 - 3 of 3

Open Access
Fitness Warrior: Fitness and Nutrition Tracker with Personalized Goal Generation
(SLIIT City UNI, 2025-07-08) De Mel, Y.D; Nallapperuma, P.M
Fitness Warrior is a comprehensive mobile fitness tracking application developed using React Native and Firebase that addresses critical limitations in existing solutions through the innovative integration of machine learning, gamification, and social features. Traditional fitness applications suffer from inaccurate step detection (with error rates exceeding 20% error rates), inefficient nutrition tracking interfaces, poor user retention (with 73% abandonment within three months), and a lack of adaptive personalization. This project uniquely implements ondevice machine learning via TensorFlow.js for privacypreserving step detection, combines TF-IDF vectorization with cosine similarity for efficient food searching, and incorporates principles of Self-Determination Theory through a cohesive social motivation framework. Development followed the Agile Scrum methodology, implementing a CNN-based model processing sensor data at 50Hz sampling rate, creating a database of 2,395 food items with optimized search algorithms, and designing gamified social features. The application achieves 95.2% real-world step counting accuracy compared to manual counting, significantly outperforming conventional threshold-based approaches (48.3% accuracy), while the calorie tracker delivers 92.7% relevant results in top-5 suggestions with 126ms search latency. Evaluation with 21 users demonstrated exceptional impact: 95.3% reported increased daily steps, 90.4% experienced greater calorie intake awareness, and 71.4% found social features strongly motivating. The application received outstanding approval with 90.5% of testers rating overall satisfaction at 8 or higher on a 10-point scale. This research successfully demonstrates how integrated, machine learning-enhanced fitness applications can meaningfully impact user health behaviours while overcoming significant limitations in existing solutions.
Open Access
Agent-Based Gamified Learning Environments for Data Science Education
(SLIIT Business School, 2023-12-14) Jayalath, N; Rajapakse, C
Because of the rapid advancement of technology and the increasing importance of the inferences that can be drawn from the big data available in organizations, modern organizations require managers and data Analysts who are capable of data-driven decision-making. But data science students need a natural environment when it comes to learning data-driven decisionmaking, especially when it comes to predictive and prescriptive analytics. Due to costs and other associated risks in a natural organisation setting, it is hard for educational institutions to teach these aspects of decision-making for data science students. Even Though gamification has been implemented in the data analysis domain in various forms, the field still requires a suitable environment to learn predictive analytics interactively for the students. Even though Researchers have identified that Gamified learning environments can improve Predictive analytics learning can be improved by 15.8%, still there is the lack of proper implementation of a suitable gamified learning environment. This research focused on identifying drawbacks of existing learning environments and whether Agent-Based Modeling can be used in modelling a suitable gamified learning environment. Therefore, an agent-based prototype model of a parameterized environment that enables data-driven decision-making in a simulated environment was modeled using Agentbased modeling, which depicts real-life donor interactions. Results suggest that fill in blanks This Agent-based model can be used as a learning environment for data analysis. Upon further modification, A game that applies this Agent-based model can be developed.
Embargo
Technology in retail marketing and the way forward with Gamification: An exploratory study
(SLIIT Business School, 2019-12-10) Alles, T.; Jayasooriya, S.
The retail landscape is evolving rapidly as firms embrace innovative technologies in an attempt to stay ahead of the aggressive competition prevalent within the industry. The focus of this paper deems to be to explore technologies used by retail firms in the execution of their marketing efforts as well as the drivers and challenges in adopting Gamification for such efforts. A qualitative inductive research approach was taken whereby critical analysis of literature was followed through with in-depth interviews with marketing professionals in the moderntrade retail industry. The interviewees were selected through judgmental purposive sampling technique and the thematic analysis was conducted in generating insights. Findings show that the retail firms currently employ several technologies in line with those discussed in existing literature such as loyalty card systems, digital signage, VR technologies, online Gamification amidst others in carrying out their marketing efforts. Mobile Instant Messaging & Autonomous shopping carts are to be employed in the near future while firms are receptive to experiment on Hologram technologies and In-store Gamification. Furthermore, key drivers that propel firms to implement novel technology like Gamification are; to generate customer insights, enhance customer experience and achieve marketing related KPI targets. Conversely, inadequate technology infrastructure, justifying focus on a niche crowd of tech-oriented customers and slow ROI pose as challenges in the process of Gamification adoption. The study offers theoretical contribution to the knowledge gap in this domain especially in the Sri Lankan context and while it is limited to modern-trade retailers future research can be extended to other formats of retail.

SLIIT Conference and Symposium Proceedings

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