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Head injury is common in many different sports and elsewhere, and is often associated with differentdifficulties. One major problem is to identify and value the injury or the severity. Sometimes there is no sign of head injury, but a serious neck distortion has occurred, causing similar symptoms as head injuries e.g. concussion or mild TBI (traumatic brain injury). This study investigated whether direct and indirect measurements of head kinematics, combined with machine learning and 3D visualization can be used to identify head injury and value the injury. Injury statistics have found that many severe head injuries are caused by oblique impacts. An oblique impact will give rise to both linear and rotational kinematics. Since the human brain is very sensitive to rotational kinematics, many violent rotations of the head can results in large shear strains in the brain. This is when white matter and white matter connections are disrupted in the brain from acceleration and deceleration, or rotational acceleration kinematics which in turn will cause traumatic brain injuries as e.g. diffuse axonal injury (DAI). Lately there has been many studies in this field using different types of new technologies, but the most prevalent is the rise of wearable sensors that have become smaller, faster and more energy efficient where they have been integrated into mouthguards and inertial measurement units (IMUs) the size of a sim-card that measures and reports a body's specific force. It has been shown that a 6-axis IMU (3-axis rotational- and 3-axis acceleration measurements) may improve head injury prediction but more data is needed to confirm with existing head injury criterions and new criterions needs to be developed, that considers directional sensitivity. Today, IMUs are typically used in self-driving cars, aircrafts, spacecrafts, satellites etc. As of today, more and more studies have evaluated and utilized IMUs in new uncharted fields have shown promises, especially in sports, and in the neuroscience and medical field. This study proposed a method to 3D visualize head kinematics during the event of a possible head injury to indirectly identify and value the injury, by medical professionals, as well as, a direct method to identify and also value the severity of head injury with machine learning. An erroneous data collection process of reconstructed head impacts and non-head impacts have been recorded using an open-source 9-axis IMU sensor and a proprietary 6-axis IMU sensor. To value the head injury or the severity, existing head injury criterions as the Abbreviated Injury Scale (AIS), Head Injury Criterion (HIC), Head Impact Power (HIP), Severity Index (SI) and Generalized Acceleration Model for Brain Injury Threshold (GAMBIT) have been introduced. To detect head impact including the severity and non-head impact, a Random Forests (RF) classifier and Support Vector Machine (SVM) classifiers with linear- and radial basis function have been proposed, the prediction results have been promising.

Sport-related Traumatic Brain injuries (TBI) are a major problem in ice hockey. Reports show that it occurs 160 concussion per 1000 hours of play time and 4.6% of head injuries leads to a concussion in Sweden. A system that can monitor the players in real time and indicate an impact can contribute to better understanding the biomechanical etiology of a concussion. Purpose of this project is to test the ability of a wireless sensor network for monitoring the g-Forces that affect the head of the ice hockey players in real-time. We build a wireless sensor network system called g-Force Monitoring System (GFMS) by implementing a Web Socket connection between the sensor nodes and the server. The sensor measures and transmits the data over the Web Socket protocol to the server and the server registers and allows monitoring of the g-Force values in real-time. We achieved a 6 ms sampling rate by using the g-Force Monitoring System. The system was able to operate during one hour play time without any significant problem. The stored data shows that the GFMS has an ability to indicate impact and its duration over a predefined threshold. The user of the system can monitor the g-Force data in real time or can do analyzes on stored values. The GFMS can deliver valuable indications. If the system can come to existence and be implemented into the ice hockey helmets, by letting medical experts to look at and analyze the g-Force data, it can decrease the diagnosis and recovery time of a concussion. It can help to make the Ice hockey arena to a safer place without having to change the rhythm of the game.

The diploma thesis deals with the detection of falls and impacts, based on data obtained from inertial sensors, and by measuring the distance using a laser. The aim of this thesis is to extend the functionality of locators from Sewio. The thesis describes the procedure for designing algorithms for detection of falls and impacts. Then there is a procedure for development of hardware and software solution, for laser distance measurement by locator, together with presentation of achieved measurement results realized by locator after implementation of proposed solution. The work also emphasizes the minimization of energy consumption of individual solutions. In conclusion, there is a discussion of achieved results with evaluation of efficiency and usability of proposed solutions.

This dissertation focuses on the development of and applications for Nano-Composite Piezoresponsive Foam (NCPF). This self-sensing foam sensor technology was discovered through research in a sister technology, High Deflection Strain Gauges (HDSG), and was subsequently developed with some of the same base materials. Both technologies use nano and micro conductive additives to provide electrically responsive properties to materials which otherwise are insulative. NCPF sensors differ from HDSGs in that they provide a dual electrical response to dynamic and static loading, which is measured through an internally generated charge, or a change in resistance. This dissertation focuses on the development of the dynamic or piezoresponsive aspect of the NCPF sensors which tends to have more consistent electrical response over a larger number of cycles. The primary development goal was to produce a sensor that was accurate, while providing a consistent, repeatable response over multiple impacts. The hypothesized electric generation is attributed to a triboelectric interaction between the conductive additives and the polyurethane foam matrix. This hypothesis was validated by examining different conductive additives with varying loading levels and specific surface areas while accounting for other design considerations such as the electrode used to harvest the response. The results of this analysis support the triboelectric model and point to carbon or nickel-based additives for optimal performance. The NCPF response measured by digital signal acquisition devices is directly dependent upon its input impedance. Increased input capacitance has a negative effect on the signal, however, higher input resistance has a positive linear correlation to voltage. Other considerations that affect the electrical response include the temperature and humidity in which the sensor is used and result in a scaled electrical response.NCPF sensors are ideally suited for use in systems which benefit from impact energy attenuation while measuring the same. This work demonstrates how the NCPF sensors can be used to detect severity and location of impacts in systems with multiple sensors (football helmets), and those with one continuous sensor (carpets). When NCPF sensors were used in a football helmet the impact severity and location of impact was accurately identified. NCPF sensors provide the benefit of simplified design by replacing existing foam while providing a direct measure of the forces. Additional research was conducted on the changes in material properties, specifically how it affects the foam structures ability to absorb energy in quasi static loading scenarios. NCPF sensors are demonstrated as viable tool to measure many different biomechanical systems.

En zones de montagne, l'aléa de chutes de pierre concerne à la fois les personnes, les infrastructures et les voies de communication. Différentes mesures de protection peuvent être envisagées pour réduire les conséquences de tels aléas. Certaines de ces mesures sont basées, totalement ou en partie, sur l'utilisation de la forêt comme structure de protection. Des structures de protections faites d'arbres abattus et laissés sur place peuvent également être utilisées pour améliorer ou maintenir la fonction de protection de la forêt suite à des travaux d'entretien forestiers, notamment. L'objectif principal de ce travail est d'étudier la capacité de protection de ces systèmes pare-blocs composés d'arbres en travers ainsi que de fournir des éléments d'aide à leur dimensionnement. Un modèle numérique basé sur la Méthode des Eléments Discrets (MED) a été développé pour étudier l'impact d'un projectile sur une tige de bois vert. Ce modèle permet de prendre en compte à la fois l'interaction bloc-structure à l’échelle locale du contact et la réponse dynamique de la structure « arbre en travers ». Des études expérimentales en laboratoire à petite échelle ont été menées pour calibrer et valider le modèle numérique. Le modèle développé a enfin été utilisé pour analyser l'efficacité des systèmes pare-blocs composés d'arbres en travers. La diminution de l'énergie du bloc suite à l'impact, la réponse de la structure, et son endommagement ont été analysés pour différentes conditions d'impact. Ces résultats ont permis d'établir des recommandations pour la conception de ces dispositifs de protection. / In mountain areas, natural hazards such as snow avalanches, landslides or rockfall threaten people and infrastructures. For this reason, civil engineering has proposed solutions to reduce the risk associated with such hazards. Despite the developments in this field, the protective capacity of forest is largely recognized. For rockfall hazard in particular, forests protection function is relevant as rock impacts onto trees lead to a significant rock energy loss. After forests maintenance tasks or windstorms the protection capacity of forests decreases. For this, felled trees are often left on the ground, in oblique position to compensate the decrease in the forest protection capacity due the forests stands density reduction. The main goal of this PhD research is to study the rockfall protection structures made of felled trees. Moreover, these investigations aim to provide recommendations for the design of such devices. A numerical model based on the Discrete Element Method (DEM) has been developed to study the dynamic response of fresh wood structures to impact. Laboratory experiments have been carried out to calibrate and validate the numerical developments. The DEM model implemented has been finally used to simulate real scenarii of rock impacts on simplified felled tree structures. These simulations have allowed identifying the most favorable configurations leading to a maximal loss of the rock kinetic energy during the impact onto a felled tree structure. Some improvements on the design of these structures are proposed to improve their capacity to dissipate the rock energy.