Tailored Fitness for Seniors: Enhancing Well-being with a Recommendation Engine for Physical Activities
The Omdena team created a recommendation system that suggests suitable activity programs based on the user’s previous activities and conditions. The system utilizes a curated list of programs offered by Good Boost and various leisure centers to provide personalized recommendations.
As people age, they may experience physical limitations that make it more challenging to engage in physical activities that are beneficial for their health. However, staying physically active is important for seniors to maintain their mobility, independence, and overall health. Machine learning can be used to analyze data on physical activities and their benefits for seniors. By examining patterns in this data, a machine learning model can learn to predict which physical activities would be most beneficial for a particular senior based on their individual characteristics.
The goal of recommending physical activities for seniors using machine learning is to improve their quality of life and overall health by encouraging them to engage in physical activities that are safe and beneficial for their unique needs. Tailoring physical activity recommendations to individual seniors, this approach can help increase their motivation to engage in physical activity and help them maintain their health and independence as they age.
The project outcomes
The Omdena team successfully developed a recommendation system that suggests suitable activity programs based on the user’s previous activities and conditions. The system recommends activities from a curated list of programs offered by Goodboost and various leisure centers. The project deliverables include an API endpoint that returns all available activities based on class availability, along with the code base and technical documentation. The project aims to encourage consistent participation of seniors and individuals of all ages in activities, fostering a sense of community among groups with similar interests, ages, and abilities.