Many factors contribute to people's physical inactivity, but among the leading factors is a lack of motivation. Fitness trackers have been shown to encourage an increase in exercise, but they are frequently abandoned within a few short months. For this thesis I developed and asynchronous-play social gaming platform, FitPlay Games, to fill the gap in motivation left by current fitness trackers. By providing users with a variety of asynchronous cooperative and competitive gaming styles, this platform enable them to find a motivation technique that works best for their lifestyle and fitness prowess. The platform encourages prolonged use of fitness trackers, helping users to have more healthy lifestyles. Individual games are designed to allow both the novice and the maven to have a chance at winning, leveling the playing field, and increasing motivation to win. The effectiveness, usability, and enjoyability of the social games will be assessed, with an emphasis on understanding differences in play habits due to gender and lifestyle.

Physical activities like walking have proven health benefits. People are adopting fitness trackers to track physical activity, but they often stop using them after a relatively short time. Many apps and games exist in the app markets that use gamification to tackle this problem of motivation. In this thesis, we examined these existing gamified fitness tracker apps from app markets and looked at the usage of different game elements within these apps. We conducted a systematic review of existing fitness Tracker Apps from Google Play Store and Apple App Store and used a mixed-method approach to identify apps, categorize them by different game elements used and found gaps in the design space using basic statistics, group clustering algorithms, and network analysis using NodeXL. We also developed a mobile game that combines step tracker data, a compelling narrative, and a strategic resource management mechanic with social cooperative-collaborative gameplay to encourage users to keep using fitness trackers and exercise more. It utilizes game elements and mechanics that haven't been explored by previous research or games as validated by our results from the systematic review of gamified fitness tracker apps.

Human Activity Recognition (HAR) is a growing field of research concerned with classifying human activities from sensor data. Modern smartphones contain numerous sensors that could be used to identify the physical activities of the smartphone wearer, which could have applications in sectors such as healthcare, eldercare, and fitness. This project aims to use smartphone sensor data together with machine learning to perform HAR on the following human locomotion activities: standing, walking, running, ascending stairs, descending stairs, and biking. The classification was done using a random forest classifier. Furthermore, in the special case of walking, an algorithm that can count the number of steps in a given data sequence was developed. The step counting algorithm was not based on a previous implementation and could therefore be considered novel. The step counter achieved a testing accuracy of 99.1\% and the HAR classifier a testing accuracy of 100\%. It is speculated that the abnormally high accuracies can be attributed primarily to the lack of data diversity, as in both cases only two persons collected the data. / Mänsklig aktivitetsigenkänning är ett växande forskningsområde som handlar om att klassificera mänskliga aktiviteter från sensordata. Moderna mobiltelefoner innehåller många sensorer som kan användas för att identifiera de fysiska aktiviteterna som bäraren utför, vilket har tillämpningar inom sektorer som sjukvård, äldreomsorg och personlig hälsa. Detta projekt använder sensordata från mobiltelefoner tillsammans med maskininlärning för att utföra aktivitetsigenkänning på följande aktiviteter: stå, gå, springa, gå uppför trappor, gå nedför trappor och cykla. Klassificeringen gjordes med hjälp av en ``random forest''-klassificerare. Vidare utvecklades en algoritm som kan räkna antalet steg i en given datasekvens som samlats in när användaren går. Stegräkningsalgoritmen baserades inte på en tidigare implementering och kan därför betraktas som ny. Stegräknaren uppnådde en testnoggrannhet på 99,1\% och aktivitetsigenkänningen en testnoggrannhet på 100\%. De oväntat höga noggrannheterna antas främst bero på bristen av diversitet i datan, eftersom den endast samlades in av två personer i båda fallen. / Kandidatexjobb i elektroteknik 2022, KTH, Stockholm

In this paper, a technical overview will be provided for a developed mobile exergame, with a particular focus on its movement tracking. By utilizing spatial movement readout from the AR algorithm, we've managed to create an easy to use exergame that allows the user to track their horizontal movement. In contrast to a more conventional approach, our solution can work indoors, and can be applied to vertical motion tracking as well. The applied method led to an exergame tailor-made for its target group, but it didn't include a thorough examination of other alternatives to our AR usage. This means that our solution should be researched further to better understand its relevance in the field, while we've shown with a practical example how it can be utilized. / Heart-eXg