Abstract
In stress sensing, Window-derived Heart Rate Variability (W-HRV) methods are by far the most heavily used feature extraction methods. However, these W-HRV methods come with a variety of tradeoffs that motivate the development of alternative methods in stress sensing. We compare our method of using HeartBeat Morphology (HBM) features for stress sensing to the traditional W-HRV method for feature extraction. In order to adequately evaluate these methods we conduct a Trier Social Stress Test (TSST) to elicit stress in a group of 13 firefighters while recording their ECG, actigraphy, and psychological self-assessment measures. We utilize the data from this experiment to analyze both feature extraction methods in terms of computational complexity, detection resolution performance, and event localization performance. Our results show that each method has an ideal niche for its use in stress sensing. HBM features tend to be more effective in an online, stress detection context. W-HRV shows to be more suitable for offline post processing to determine the exact localization of the stress event.

Abstract
The electronic storage of medical patient data is becoming a daily experience in most of the practices and hospitals worldwide. However, much of the available data is in free text form, a convenient way of expressing concepts and events but especially challenging if one wants to perform automatic searches, summarization, or statistical analyses. Information Extraction can relieve some of these problems by offering a semantically informed interpretation and abstraction of the texts. MedInX, the Medical Information eXtraction system presented in this chapter is designed to process textual clinical discharge records in order to perform automatic and accurate mapping of free text reports onto a structured representation. MedInX components are based on Natural Language Processing principles and provide several mechanisms to read, process, and utilize external resources, such as terminologies and ontologies. MedInX current practical applications include automatic code assignment and an audit system capable of systematically analyze the content and completeness of the clinical reports. Recent evaluation efforts on a set of authentic patient discharge letters indicate that the system performs with 95% precision and recall. © 2013, IGI Global.

Abstract
Intraoperative evaluation of the efficacy of Deep Brain Stimulation includes evaluation of the effect on rigidity. A subjective semi-quantitative scale is used, dependent on the examiner perception and experience. A system was proposed previously, aiming to tackle this subjectivity, using quantitative data and providing real-time feedback of the computed rigidity reduction, hence supporting the physician decision. This system comprised of a gyroscope-based motion sensor in a textile band, placed in the patients hand, which communicated its measurements to a laptop. The latter computed a signal descriptor from the angular velocity of the hand during wrist flexion in DBS surgery. The first approach relied on using a general rigidity reduction model, regardless of the initial severity of the symptom. Thus, to enhance the performance of the previously presented system, we aimed to develop models for high and low baseline rigidity, according to the examiner assessment before any stimulation. This would allow a more patient-oriented approach. Additionally, usability was improved by having in situ processing in a smartphone, instead of a computer. Such system has shown to be reliable, presenting an accuracy of 82.0% and a mean error of 3.4%. Relatively to previous results, the performance was similar, further supporting the importance of considering the cogwheel rigidity to better infer about the reduction in rigidity. Overall, we present a simple, wearable, mobile system, suitable for intra-operatory conditions during DBS, supporting a physician in decision-making when setting stimulation parameters.

Abstract
Measuring human gait is important in medicine to obtain outcome parameter for therapy, for instance in Parkinson's disease. Recently, small inertial sensors became available which allow for the registration of limb-position outside of the limited space of gait laboratories. The computation of gait parameters based on such recordings has been the subject of many scientific papers. We want to add to this knowledge by presenting a 4-segment leg model which is based on inverse kinematic and Kalman filtering of data from inertial sensors. To evaluate the model, data from four leg segments (shanks and thighs) were recorded synchronously with accelerometers and gyroscopes and a 3D motion capture system while subjects (n = 12) walked at three different velocities on a treadmill. Angular position of leg segments was computed from accelerometers and gyroscopes by Kalman filtering and compared to data from the motion capture system. The four-segment leg model takes the stance foot as a pivotal point and computes the position of the remaining segments as a kinematic chain (inverse kinematics). Second, we evaluated the contribution of pelvic movements to the model and evaluated a five segment model (shanks, thighs and pelvis) against ground-truth data from the motion capture system and the path of the treadmill. Results: We found the precision of the Kalman filtered angular position is in the range of 2-6 degrees (RMS error). The 4-segment leg model computed stride length and length of gait path with a constant undershoot of 3% for slow and 7% for fast gait. The integration of a 5th segment (pelvis) into the model increased its precision. The advantages of this model and ideas for further improvements are discussed.

Abstract
Stress can impact multiple psychological and physiological human domains. In order to better understand the effect of stress on cognitive performance, and whether this effect is related to an autonomic response to stress, the Trier Social Stress Test (TSST) was used as a testing platform along with a 2-Choice Reaction Time Task. When considering the nature and importance of Air Traffic Controllers (ATCs) work and the fact that they are subjected to high levels of stress, this study was conducted with a sample of ATCs (n = 11). Linear Heart Rate Variability (HRV) features were extracted from ATCs electrocardiogram (ECG) acquired using a medical-grade wearable ECG device (Vital Jacket((R)) (1-Lead, Biodevices S.A, Matosinhos, Portugal)). Visual Analogue Scales (VAS) were also used to measure perceived stress. TSST produced statistically significant changes in some HRV parameters (Average of normal-to-normal intervals (AVNN), Standard Deviation of all NN (SDNN), root mean square of differences between successive rhythm-to-rhythm (RR) intervals (RMSSD), pNN20, and LF/HF) and subjective measures of stress, which recovered after the stress task. Although these short-term changes in HRV showed a tendency to normalize, an impairment on cognitive performance was evident. Despite that participant's reaction times were lower, the accuracy significantly decreased, presenting more errors after performing the acute stress event. Results can also point to the importance of the development of quantified occupational health (qOHealth) devices to allow for the monitoring of stress responses.

Abstract
Optical Tweezers (OTs) have been widely applied in Biology, due to their outstanding focusing abilities, which make them able to exert forces on micro-sized particles. The magnitude of such forces (pN) is strong enough to trap their targets. However, the most conventional OT setups are based on complex configurations, being associated with focusing difficulties with biologic samples. Optical Fiber Tweezers (OFTs), which consist in optical fibers with a lens in one of its extremities are valuable alternatives to Conventional Optical Tweezers (COTs). OFTs are flexible, simpler, low-cost and easy to handle. However, its trapping performance when manipulating biological and complex structures remains poorly characterized. In this study, we experimentally characterized the optical trapping of a biological cell found within a culture of rodent glial neuronal cells, using a polymeric lens fabricated through a photo-polymerization method on the top of a fiber. Its trapping performance was compared with two synthetic microspheres (PMMA, polystyrene) and two simple cells (a yeast and a Drosophila Melanogaster cell). Moreover, the experimental results were also compared with theoretical calculations made using a numerical model based on the Finite Differences Time Domain. It was found that, although the mammalian neuronal cell had larger dimensions, the magnitude of forces exerted on it was the lowest among all particles. Our results allowed us to quantify, for the first time, the complexity degree of manipulating such "demanding" cells in comparison with known targets. Thus, they can provide valuable insights about the influence of particle parameters such as size, refractive index, homogeneity degree and nature (biologic, synthetic). Furthermore, the theoretical results matched the experimental ones which validates the proposed model.