Health is one of the most important elements in every individual’s life. Even though there is much advancement in science, the quality of healthcare has never been up to the mark. This appears to be true especially in the field of Physiotherapy. Physiotherapy is the analysis of human joints and bodies and providing remedies for any pains or injuries that might have affected the physiology of a body. To give patients a top notch quality health analysis and treatment, either the number of doctors should increase, or there should be an alternative replacement for a doctor. Our Master Thesis is aimed at developing a prototype which can aid in providing healthcare of high standards to the millions.  Methods: Microsoft Kinect SDK 2.0 is used to develop the prototype. The study shows that Kinect can be used both as Marker-based and Marker less systems for tracking human motion. The degree angles formed from the motion of five joints namely shoulder, elbow, hip, knee and ankle were calculated. The device has infrared, depth and colour sensors in it. Depth data is used to identify the parts of the human body using pixel intensity information and the located parts are mapped onto RGB colour frame.  The image resulting from the Kinect skeleton mode was considered as the images resulting from the markerless system and used to calculate the angle of the same joints. In this project, data generated from the movement tracking algorithm for Posture Side and Deep Squat Side movements are collected and stored for further evaluation.  Results: Based on the data collected, our system automatically evaluates the quality of movement performed by the user. The system detected problems in static posture and Deep squat based on the feedback on our system by Physiotherapist.

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Technological instruments are used in sports to record and analyze data of performing athletes, in order to improve techniques and increase competitiveness. A method to allow for recording of data of a moving subject is by usage of a depth camera mounted on a drone that can track and follow a subject. The objective was to develop a drone system that was capable of autonomous operation based on follow-me mode. The original prototype lacked required hardware for reliable orientation in environments with varying air-pressure, and software for a follow-me mode-system. Height above ground was measured using a downward facing depth sensor and a 3D image of the subject was generated by a depth camera. The data was then used by the drone to navigate through the environment. Overall performance of the height adjustment would be sufficient to allow for autonomous operation according to test results. Testing of the follow-me mode showed that the current configuration was not capable of retaining a sufficiently consistent position relative to the subject, hence high video quality was not achieved. However, it gave a positive indication that autonomous operation based on a depth camera is possible. The concept has a high potential and if the system would be further developed it could allow athletes to record and analyze data in order to improve techniques and increase competitiveness. / Teknologiska instrument används inom sport för att samla in och analysera data om idrottare, detta för att kunna förbättra teknik och öka prestationsförmåga. En metod för att kunna samla in data från ett rörligt subjekt är genom användandet av en djupkamera monterad på en drönare som kan spåra och följa efter ett subjekt. Målet var att utveckla ett drönarsystem som är kapabel till autonom drift baserat på ett “följ mig-läge” (från engelskans follow-me mode). Den ursprungliga modellen saknade hårdvara som krävs för en pålitlig orientering i miljöer med varierande lufttryck samt mjukvara för följ mig-läge. Höjd över markytan mättes med en nedåtriktad djupsensor och en 3D-bild av ett subjekt genererades av en djupkamera. Datan användes sedan av drönaren för navigation i omgivningen. Övergripande prestandan för höjdjusteringen skulle vara tillräcklig för att möjliggöra autonom drift enligt testresultat. Testning av följ mig-läget visade att aktuell konfiguration ej är kapabel till att erhålla en tillräckligt konsekvent position relaterat till subjektet, därav var hög video kvalite ej uppnåelig. Däremot gavs en positiv indikation på att autonom drönarnavigering kan baseras på ett system med djupkamera. Konceptet har en hög potential och vid vidare utveckling skulle det kunna möjliggöra för idrottare att kunna samla in och analysera data för att kunna förbättra teknik och öka prestationsförmåga.

This master thesis presents a machine learning framework for real-time gesture and skeleton-based action recognition, integrated with the Unity game engine. The system aims to enhance human-computer interaction (HCI) in gaming and 3D related applications through natural movement recognition, by training a model on skeleton tracking data. The framework is trained to accurately categorize and identify gestures such as kicks and punches, enabling a more immersive gaming experience not existing in traditional controllers. After studying the evolution of HCI and how machine learning has transformed and reshaped the interaction paradigm, the prototype system is built through data collection, augmenting, and preprocessing, followed by training and evaluating a Long Short-Term Memory (LSTM) neural network model for gesture classification. The model is integrated into Unity via Unity Sentis using Open Neural Network Exchange (ONNX) format, enabling efficient real-time action recognition in 3D space. Each component of the pipeline is available and adaptable for future custom- ization and needs, skeleton tracking and Unity integration is built using the ZED 2i camera and ZED SDK. Experimental results demonstrate that the system presented can achieve over 90% accuracy in identifying predefined gestures. As a bridging solution tailored for Unity, this framework offers a practical solution to action recognition that could be found useful in future applications. This work contributes to advancing human-computer interaction and offers a foundation for further development in gesture-based Unity game design.