Entraining music and power output on two different submaximal loads in bench press

Masteroppgave i kroppsøving - Nord universitet, 201

Development Of A Human Heart Rate And Skin Temeprature Monitoring System

Continuous heart rate monitors (CHRM) can give unique insights into a person\u27s physiological and psychological state; however, existing CHRMs are not suitable for continuous monitoring because they do not provide beat-to-beat accuracy and comfort needed for all-day use. Our group is currently developing a miniature earlobe mounted CHRM system which is both comfortable and provides beat-to-beat accuracy, based on a miniature heart rate sensing technology developed previously in our lab. The objective of this thesis is to design low power system firmware to perform key tasks: i) acquire heart rate and skin temperature data from wearable sensors; ii) signal process the raw heart rate data to calculate r-r interval; iii) optimize the heart rate sensor parameters for power and noise; iv) store the sensor data in local flash memory, and v) stream the data to an Android device. Each of the above functions was successfully implemented with low power consideration

Using Movies to Probe the Neurobiology of Anxiety

Over the past century, research has helped us build a fundamental understanding of the neurobiological underpinnings of anxiety. Specifically, anxiety engages a broad range of cortico-subcortical neural circuitry. Core to this is a ‘defensive response network’ which includes an amygdala-prefrontal circuit that is hypothesized to drive attentional amplification of threat-relevant stimuli in the environment. In order to help prepare the body for defensive behaviors to threat, anxiety also engages peripheral physiological systems. However, our theoretical frameworks of the neurobiology of anxiety are built mostly on the foundations of tightly-controlled experiments, such as task-based fMRI. Whether these findings generalize to more naturalistic settings is unknown. To address this shortcoming, movie-watching paradigms offer an effective tool at the intersection of tightly controlled and entirely naturalistic experiments. Particularly, using suspenseful movies presents a novel and effective means to induce and study anxiety. In this thesis, I demonstrate the potential of movie-watching paradigms in the study of how trait and state anxiety impact the ‘defensive response network’ in the brain, as well as peripheral physiology. The key findings reveal that trait anxiety is associated with differing amygdala-prefrontal responses to suspenseful movies; specific trait anxiety symptoms are linked to altered states of anxiety during suspenseful movies; and states of anxiety during movies impact brain-body communication. Notably, my results frequently diverged from those of conventional task-based experiments. Taken together, the insights gathered from this thesis underscore the utility of movie-watching paradigms for a more nuanced understanding of how anxiety impacts the brain and peripheral physiology. These outcomes provide compelling evidence that further integration of naturalistic methods will be beneficial in the study of the neurobiology of anxiety

Innovative care of individuals in the athletic training room: A case series

This work follows three unique cases of athletic injuries and/or injuries in patients. These cases were selected because of the innovative nature of the rehabilitation process conducted by the sports medicine team via the athletic training room or the unique nature of the injury/case. The first is a concussion sustained from a motor vehicle accident in a collegiate female thrower. This is unique because it’s a non-sport related concussion handled as an athletic trainer would handle a sports related concussion. It also demonstrates a new unique protocol for concussion management. The second case is a patient with two complete anterior cruciate ligament tears in contralateral knees sustained at different times with different surgical repair techniques. The patient is a collegiate, female lacrosse athlete. This case is unique because of the dual injuries as the different surgical procedures in the same patient. The third and final case is a collegiate baseball player who sustained an ulnar collateral ligament tear due to overuse. It was surgically repaired and rehabilitated with the Advanced Thrower’s Ten Protocol rather than the Thrower’s Ten Protocol which makes this case more unique

Modeling of IoT devices in Business Processes: A Systematic Mapping Study

[EN] The Internet of Things (IoT) enables to connect the physical world to digital business processes (BP). By using the IoT, a BP can, e.g.: 1) take into account real-world data to take more informed business decisions, and 2) automate and/or improve BP tasks. To achieve these benefits, the integration of IoT and BPs needs to be successful. The first step to this end is to support the modeling of IoT-enhanced BPs. Although numerous researchers have studied this subject, it is unclear what is the current state of the art in terms of current modeling solutions and gaps. In this work, we carry out a Systematic Mapping Study (SMS) to find out how current solutions are modelling IoT into business processes. After studying 600 papers, we identified and analyzed in depth a total of 36 different solutions. In addition, we report on some important issues that should be addressed in the near future, such as, for instance the lack of standardization.This research has been funded by Internal Funds KU Leuven (Interne Fondsen KU Leuven) and the financial support of the Spanish State Research Agency under the project TIN2017-84094-R and co-financed with ERDF.Torres Bosch, MV.; Serral, E.; Valderas, P.; Pelechano Ferragud, V.; Grefen, P. (2020). Modeling of IoT devices in Business Processes: A Systematic Mapping Study. IEEE. 221-230. https://doi.org/10.1109/CBI49978.2020.00031S22123

Camera-Based Heart Rate Extraction in Noisy Environments

Remote photoplethysmography (rPPG) is a non-invasive technique that benefits from video to measure vital signs such as the heart rate (HR). In rPPG estimation, noise can introduce artifacts that distort rPPG signal and jeopardize accurate HR measurement. Considering that most rPPG studies occurred in lab-controlled environments, the issue of noise in realistic conditions remains open. This thesis aims to examine the challenges of noise in rPPG estimation in realistic scenarios, specifically investigating the effect of noise arising from illumination variation and motion artifacts on the predicted rPPG HR. To mitigate the impact of noise, a modular rPPG measurement framework, comprising data preprocessing, region of interest, signal extraction, preparation, processing, and HR extraction is developed. The proposed pipeline is tested on the LGI-PPGI-Face-Video-Database public dataset, hosting four different candidates and real-life scenarios. In the RoI module, raw rPPG signals were extracted from the dataset using three machine learning-based face detectors, namely Haarcascade, Dlib, and MediaPipe, in parallel. Subsequently, the collected signals underwent preprocessing, independent component analysis, denoising, and frequency domain conversion for peak detection. Overall, the Dlib face detector leads to the most successful HR for the majority of scenarios. In 50% of all scenarios and candidates, the average predicted HR for Dlib is either in line or very close to the average reference HR. The extracted HRs from the Haarcascade and MediaPipe architectures make up 31.25% and 18.75% of plausible results, respectively. The analysis highlighted the importance of fixated facial landmarks in collecting quality raw data and reducing noise