As a resource-deficient country with a growing population, innovative solutions are needed for water sustainability in Singapore. According to PUB, up to 45% of water consumption is attributed to domestic use, with showering being the largest contributor within households. To address this, our solution focuses on promoting behavioral changes among the population to achieve significant water conservation. We propose to install water flow sensors and ESP32 microcontrollers onto shower hoses to collect data on water consumption. By utilizing supervised learning, this data will be translated into a visual representation using a RGB LED light, encouraging users to reduce their water usage. Furthermore, a personalized mobile application, HydroMind, will be developed to provide users with information on the amount of water saved, cost savings, and time required to meet the water-saving objectives. In short, Hydromind aims to empower each and every individual to play a proactive role in conserving water.
For green, the user may continue showering. For yellow, users should complete their shower within the next minute. For red, it is recommended that they stop showering.
The algorithm will be fed with daily water usage as training data. From the training stage, the system can make personalised recommendations for efficient showering. The cloud server utilises the computational power and resources provided by the cloud environment to perform advanced analysis and modelling on the data.
The purpose of the app is to allow our user to easily set their water saving goals with ease using our machine learning algorithm. There are three goals to choose from. Novice being the easiest, and elite being the hardest. After setting a goal, users can view their water usage for the week. This eliminates the need for the user to continuously update their goal before showering. Users can also view their total water usage and money saved to track their long-term progress.
The hardware installation process involves obtaining and setting up two key components: the sensor unit and the gateway. Both of these components are equipped with ESP32 microcontrollers, and the necessary code for each can be found in the files "hydromind-gateway.zip" and "hydromind-sensor.zip," respectively.
Currently, the HydroMind App is built using flutter and firebase. Detailed installed steps will be provided as the project progresses. For now, please refer to the presentation slides.
At this time, there are no specific documentation available for HydroMind App. Please refer to the source code and slides for more information as the project develops.
The HydroMind team does not have any licensing information. Please kindly note that all rights are reserved by the HydroMind Team.
Contributions to HydroMind are not open at this time. As the project progresses, the team will assess opportunities for collaborations and contributions. Stay tuned for updates