Ice hockey is a sport whose athletes are at high risk for traumatic head injuries due to the violence of potential impacts with other athletes, ice, or glass during games. In order to develop the best protective strategies for the players, it is necessary to have a deep understanding of accident mechanisms during ice hockey games. Accident reconstructions using the finite element (FE) method are a way to perform a systematic analysis of impact cases, but require input data on the circumstances of the accidents. Thus, this project focused on finding a way to extract the position and velocity of the players involved from readily available videos of ice hockey accidents using motion tracking methods. This project included two parts: pose estimation and velocity estimation. The pose estimation aimed to align a human body model (HBM) with the players' poses and the key steps included estimating 2D joints from impact images, estimating the players' 3D poses, skeleton inferencing, and aligning the results with the baseline HBM via pelvic registration. The velocity estimation defined the initial conditions for simulating the collision and key steps included identifying the players' 2D joints across impact video frames, tracking of the players using a simplified pelvis projection on the rink plane, and estimating the players’ velocity using homography to identify their position on the ice hockey rink. Then, both parts were applied to accident cases from a video database of collisions that occurred during a hockey league season. The cases in which the pipeline was fully applied ultimately resulted in LS-DYNA positioning files for the Total Human Model for Safety (THUMS) model, and problematic cases were used to get an overview of the limits of the chosen methodology. Said limitations were mostly linked to the quality of the source videos, which is highly dependent on the source of the videos and possibly not controllable. Due to this, selection criteria are required, such as checking the blurriness and quality of the videos and the viewing angles to ensure as few occlusions as possible. Overall, this project resulted in a working semi-automatic pipeline for pose and velocity estimation in contact sports collisions, as well as a first set of personalized input information that should allow the reconstruction of ice hockey accidents using FE simulations. / Ishockey är en sport vars utövare löper stor risk att drabbas av traumatiska huvudskador på grund av de våldsamma potentiella kollisionerna med andra utövare, is eller glas under matcherna. För att kunna utveckla de bästa skyddsstrategierna för spelarna är det nödvändigt att ha en djup förståelse för olycksmekanismerna under ishockeymatcher. Olycksrekonstruktioner med hjälp av finita elementmetoden är ett sätt att utföra en systematisk analys av kollisionsfall, men kräver indata om omständigheterna kring olyckorna. Detta projekt fokuserade därför på att hitta ett sätt att extrahera de inblandade spelarnas position och hastighet från lättillgängliga videor av ishockeyolyckor med hjälp av rörelsespårningsmetoder. Projektet bestod av två delar: poseuppskattning och hastighetsuppskattning. Poseuppskattningen syftade till att anpassa en humanmodell till spelarnas poser och de viktigaste stegen omfattade uppskattning av 2D-leder från kollisionsbilder, uppskattning av spelarnas 3D-poser, skelettinferens och anpassning av resultaten till baslinjen HBM via bäckenregistrering. Hastighets-uppskattningen definierade de initiala villkoren för simulering av kollisionen och viktiga steg inkluderade identifiering av spelarnas 2D-led i videobilder av kollisionen, spårning av spelarna med hjälp av en förenklad bäckenprojektion på rinkplanet och uppskattning av spelarnas hastighet med hjälp av homografi för att identifiera deras position på ishockeyrinken. Därefter tillämpades båda delarna på olycksfall från en videodatabas med kollisioner som inträffade under en säsong i en hockeyliga. De fall där pipelinen tillämpades fullt ut resulterade slutligen i LS-DYNA-positioneringsfiler, och problematiska fall användes för att få en överblick över gränserna för den valda metoden. Begränsningarna var främst kopplade till kvaliteten på källvideorna, som är starkt beroende av källan till videorna och eventuellt inte kan kontrolleras. På grund av detta krävs urvalskriterier, t.ex. kontroll av videornas oskärpa och kvalitet samt betraktningsvinklar för att säkerställa så få ocklusioner som möjligt. Sammantaget resulterade detta projekt i en fungerande halvautomatisk pipeline för pose- och hastighetsuppskattning vid kollisioner i kontaktsporter, samt en första uppsättning personlig indatainformation som bör möjliggöra rekonstruktion av ishockeyolyckor med hjälp av simulering med finita element.

<p dir="ltr">Decades of semiconductor technology scaling and breakthroughs in communication technology have miniaturized computing, embedding it everywhere, enabling the development of smart things connected to the internet, forming the Internet of Things. Further miniaturization of devices has led to an exponential increase in the number of devices in and around the body in the last decade, forming a subset of IoT which is increasingly becoming popular as the Internet of Bodies (IoB). The gradual shift from the current form of human-electronics coexistence to human-electronics cooperation, is the vision of Internet of Bodies (IoB). This vision of a connected future with devices in and around our body talking to each other to assist their day-to-day functions demands energy efficient means of communication. Electro-Quasistatic Human Body Communication (EQS-HBC) has been proposed as an exciting alternative to traditional Radio Frequency based methodologies for communicating data around the body. In this dissertation, we expand the boundaries of wearable and implantable IoB nodes using Electro-Quasistatic Human Body Communication and Powering by developing advanced channel models and demonstrating novel applications.</p><p dir="ltr">Leveraging the advanced channel models developed for wearable EQS-HBC, we demonstrate wearable applications like ToSCom which extend the use cases of touchscreens to beyond touch detection and location to enable high-speed communication strictly through touch. We further demonstrate an application of EQS Resonant Human Body Powering to demonstrate Step-to-Charge, allowing mW-scale wireless power transfer to wearable devices. With increasing connected implanted healthcare devices becoming a part of the IoB space, we benchmark RF-based technologies for In-Body to Out-of-Body (IBOB) communication using novel in-vivo experiments. We then explore EQS-HBC in the realm of IBOB communication using advanced channel modeling, revealing its potential for low-power and physically secure data transfer from implantable devices to wearable nodes on the body, demonstrating its potential in extending the battery life span of implantable nodes. Finally, an overview of the potential of IoB devices is analyzed with the use of EQS-HBC where we propose a human-inspired distributed network of IoB nodes which brings us a step closer to the potential for perpetually operable devices in and around the body.</p>