Hand and finger movements are underrepresented in biomechanical studies, primarily due to the challenge of tracking the hands and fingers. Several limitations are associated with marker-based motion capture, including interference with natural movement, and require the tedious, time-consuming application of numerous markers. Advancements in computer vision have led to the development of markerless motion capture systems yet validation of markerless systems for the upper extremities is limited, especially the hand and fingers. The purpose of this study was to develop and assess a markerless motion capture system capable of tracking hand and finger kinematics. A markerless system using four synchronized webcams was developed. Camera pairs were organized in different angles Centre90° (C/90°), Left45°/Right45° (L45°/R45°), and Centre/Left45° (C/L45°). Motion capture was performed with both marker-based and markerless systems. Twenty healthy participants performed five dynamic hand tasks with and without markers. Three-dimensional joint positions were defined using a musculoskeletal model in OpenSim. No significant differences were observed between C/90° and C/L45° markerless camera pairs and the marker-based system. The L45°/R45° camera pair differed significantly from other markerless pairs in several tasks but agreed with the marker-based system for the index finger during flexion. For most of the fingers, no significant differences were found across the different camera pairs. Correlations and error for the concurrent finger flexion task revealed high consistency among all the camera pairs, with R² above 0.90 and RMSD below 10°, the thumb showed greater variability. The R² and RMSD varied depending on the camera comparison and finger for each task. Markerless motion capture for the hands and fingers is possible with little difference to marker-based systems and is dependent on the camera orientation used. / Thesis / Master of Science in Kinesiology

The wrist is a body part that can be used during repetitive movements in many work environments. There is a need to measure these movements in order to notice harmful repetitive movements in advance. There are many different ways to measure these movements, such as with the use of a depth camera. The goal of this study is to determine if this can be done with high precision compared to other technologies. In order to determine this, an application was created that used several different technologies and libraries to track and pinpoint the hand’s and forearm’s location in each frame. With these locations, together with timestamps from the frames, the angular velocity of the wrist could be calculated. The recordings were made in several different test cases with factors such as background, clothes and lighting changing in each test. In order to compare the depth cameras values, a golden standard had to be set. The depth camera’s recorded values were compared to the golden standard’s recorded values by displaying the values on a graph and by calculating the root mean squared error as well as the mean absolute error. The results indicated that a depth camera can be used to measure wrist movements relatively accurately, even with more advanced movements relative to this study. The result also showed that the depth camera had problems in some test cases. / Handleden är en kroppsdel som kan användas under repetitiva rörelser i många arbetsmiljöer. Det finns ett behov av att mäta dessa rörelser för att upptäcka skadliga repetitiva rörelser i förväg. Det finns många olika sätt att mäta dessa rörelser, till exempel med hjälp av en djupkamera. Målet med denna studie är att avgöra om detta kan göras med hög precision jämfört med andra teknologier. För att avgöra detta skapades en applikation som använder flera olika teknologier och bibliotek för att spåra och lokalisera handens och underarmens position i varje bildruta. Med hjälp av dessa positioner, tillsammans med tidsstämplar från bildrutorna, kunde vinkelhastigheten för handleden beräknas. Inspelningarna gjordes i flera olika testfall där faktorer som bakgrund, kläder och belysning ändrades i varje test. För att kunna jämföra djupkamerans värden behövdes en referensstandard fastställas. Djupkamerans inspelade värden jämfördes med referensstandardens inspelade värden genom att visa värdena på en graf och beräkna rotmedelkvadratfelet samt medelabsolutfelet. Resultaten indikerade att en djupkamera kan användas för att mäta handledsrörelser relativt noggrant, även med mer avancerade rörelser i förhållande till denna studie. Resultatet visade även att djupkameran hade problem i vissa testfall.