Crowdsensing-Based Gamification for Collective Assistance for Post-Era of Coronavirus Epidemic in Community Living

The Role Of Gamification In Encouraging Social Interaction

Almost ten millions of people worldwide became hooked in an augmented reality battle for rewards, badges and points with the launch of Pokémon Go in 2016.The game involves catching pocket monsters (Pokémon) at specific places via your phone. You also need to visit 'Poké stops' -- check-in points at landmarks -- to collect items like the 'Poké balls' needed to capture those creatures. A tracker shows Pokémon at nearby stops and the weather at your position determines which kinds are available, enticing you to rush out and catch them. Your collection of creatures can also be used to battle other monsters in 'gyms' that earn rewards such as coins, which are used to purchase useful items (Chamary, 2018). The big benefits of playing Pokémon GO are huge and it simply include:

• 1.

  Engaging users to play physical activity

• 2.

  Exploring nature

• 3.

  Social Interaction

• 4.

  Brain Training

This type of game gamifies everyday life by providing users with an endless supply of opportunities to engage while you’re traveling between point A and point B with nothing better to do. Gamification is a great technique that can become an innovative part of any outreach tool for social or behavior change. Using games and gamification social networking techniques one can impact education, social learning, public health and how people communicate with each other. Actually, there is a large body of theoretical literature on how social networks and population structures may affect the spread of communicable disease like the current corona virus, however, there is no clear way on the design of optimal control strategies. Such research work often requires detailed data on populations and people positioning. By employing gamification techniques over the social media, this kind of data can be collected for developing better control strategies. This paper is an attempt in this direction to design a place learning through implementing a recommender game over the social media. The recommender game is in the form of intelligent advisor (Răzvan, 2005) with the following capabilities:

• 1.

  Intelligent advisor can extract knowledge from a customer, build and update corresponding customer profile during each interaction with a customer;

• 2.

  The customer profile built by the intelligent advisor can be further mined;

• 3.

  Intelligent advisor can use behavioral science techniques and create a customer dialog that embraces what/how people think, rather than forcing consumers to feed an optimization algorithm;

• 4.

  Intelligent advisor can continually learn from its interactions with consumers to improve its functionality;

• 5.

  Intelligent advisor can keep working healthily when facing data that are uncertain, noisy, sparse, or missing;

• 6.

  Intelligent advisor should be able to work in real-time to meet the requirements of an Internet application;

• 7.

  Intelligent advisor should be largely domain-independent, and be able to customize the same platform for other applications (e.g., selling computers, cars, financial services).

The Recommender Game Over Social Media

The recommender game idea is simple but effective and is based on two notions.