Abstract
Inertial sensors can potentially assist clinical decision making in gait-related disorders. Methods for objective spatio-temporal gait analysis usually assume the careful alignment of the sensors on the body, so that sensor data can be evaluated using the body coordinate system. Some studies infer sensor orientation by exploring the cyclic characteristics of walking. In addition to being unrealistic to assume that the sensor can be aligned perfectly with the body, the robustness of gait analysis with respect to differences in sensor orientation has not yet been investigated-potentially hindering use in clinical settings. To address this gap in the literature, we introduce an orientation-invariant gait analysis approach and propose a method to quantitatively assess robustness to changes in sensor orientation. We validate our results in a group of young adults, using an optical motion capture system as reference. Overall, good agreement between systems is achieved considering an extensive set of gait metrics. Gait speed is evaluated with a relative error of -3.1 +/- 9.2 cm/s, but precision improves when turning strides are excluded from the analysis, resulting in a relative error of -3.4 +/- 6.9 cm/s. We demonstrate the invariance of our approach by simulating rotations of the sensor on the foot.

Abstract
Intraoperative neuromonitoring (IONM) techniques are usually implemented during spine surgery to avoid nefarious abuse of the nervous system, which can cause postoperative problems. A lack of bibliometric analysis on the topic of IONM in spine surgery has been identified. Therefore, the aims of this study are to provide information about the main contributors to this field and their publication dynamics, as well as conceptual and cooperative networks. Results have shown that a steady publication increase has been occurring since 1991, with high levels of citations in the first decade, but irregular publication rates have been recorded more recently. Research production by country seems to be in line with what is observed in other surgical fields, but research funding for IONM in spine surgery seems to be lower, even with the clear interest of private funding agencies. The conceptual networks have shown the importance of motor-evoked potential, electromyography, and the effect of anesthesia, particularly in scoliosis surgery.

Abstract
The objective of this study is to present a new coupling method in order to measure the electrical properties of titanium alloy pedicle screws used in spinal surgery and to compare it with other common methods of measurement. An experimental setup was devised to test the electrical resistance of two specimens of pedicle screws using four methods for coupling the sensing leads, including the use of multimeter probes, alligator clips, wrapped wires and encapsulation with thermo-retractable sleeves. The electrical resistance of the pedicle screw under testing was measured at a current of 10 mA for each coupling method, and the results compared. Our findings show that although widely used in electrical analysis, the alligator clips do not perform as well as the other methods, such as simple wrapping of wires around the screw or the direct application of multimeter probes. The use of thermo-retractable sleeves provides the lowest resistance and inter-quartile range and is closer to the tabled values for the screw's titanium alloy. Additionally, only this method allows the measurement of identical resistivity values between different screw models manufactured with the same titanium alloy. We then concluded that the use of wrapped wires encapsulated with thermo-retractable sleeves allow more accurate measurements of the pedicle screw's electrical properties.

Abstract
This study presents and applies fractal Brownian motion assessment of the center of pressure (COP) excursion during feet ground contact on standard vertical jump impulse phase with long and short countermovement (CM) in relation with lower limb muscle stretch-shortening cycle (SSC) comparing it with no CM and SSC. Fifty-four tests were performed by a group of six healthy male students of sports and physical education degree without previous injury, specific training, or fitness ability. Three repetitions were performed by each subject of a squat jump (SJ) without CM and SSC, countermovement jump (CMJ) with long CM and SSC, as well as drop jump (DJ) with short CM and SSC after depth jump from a 40 cm step. During trial tests ground reaction force and force moments were acquired with force platform and impulse phases were segmented for COP coordinates computation. Fractal Brownian motion analysis of COP excursion during impulse phases conduced to detection of differences between critical time and displacement as well as short and long-term diffusion coefficient (Ds, Dl) and Hurst index scale exponent (Hs, Hl), with Ds, Dl presenting statistical significative correlations -0.491, -0.559 and Hs, Hl non statistical significative correlations 0.266 and -0.424 with MVJ height (ht) at 5% significance for explaining underlying mechanisms on CM and SSC at MVJ. Clinical Relevance- This work contributes with new method for the study expansion of the center of pressure excursion and stability during feet ground contact from orthostatic standing position to the impulse phase during standard maximum vertical jump as the most adequate method for assessment of lower limb muscle stretch-shortening cycle.

Abstract
eSports is a rapidly growing industry with increasing investment and large-scale international tournaments offering significant prizes. This has led to an increased focus on individual and team performance with factors such as communication, concentration, and team intelligence identified as important to success. Over a similar period of time, personal physiological monitoring technologies have become commonplace with clinical grade assessment available across a range of parameters that have evidenced utility. The use of physiological data to assess concentration is an area of growing interest in eSports. However, body-worn devices, typically used for physiological data collection, may constitute a distraction and/or discomfort for the subjects. To this end, in this work we devise a novel "invisible " sensing approach, exploring new materials, and proposing a proof-of-concept data collection system in the form of a keyboard armrest and mouse. These enable measurements as an extension of the interaction with the computer. In order to evaluate the proposed approach, measurements were performed using our system and a gold standard device, involving 7 healthy subjects. A particularly advantageous characteristic of our setup is the use of conductive nappa leather, as it preserves the standard look and feel of the keyboard and mouse. According to the results obtained, this approach shows 3-15% signal loss, with a mean difference in heart rate between the reference and experimental device of -1.778 & PLUSMN; 4.654 beats per minute (BPM); in terms of ECG waveform morphology, the best cases show a Pearson correlation coefficient above 0.99.

Abstract
Gait performance is an important marker of motor and cognitive decline in older adults. An instrumented gait analysis resorting to inertial sensors allows the complete evaluation of spatiotemporal gait parameters, offering an alternative to laboratory-based assessments. To estimate gait parameters, foot trajectories are typically obtained by integrating acceleration two times. However, to deal with cumulative integration errors, additional error handling strategies are required. In this study, we propose an alternative approach based on a deep recurrent neural network to estimate heel and toe trajectories. We propose a coordinate frame transformation for stride trajectories that eliminates the dependency from previous strides and external inputs. Predicted trajectories are used to estimate an extensive set of spatiotemporal gait parameters. We evaluate the results in a dataset comprising foot-worn inertial sensor data acquired from a group of young adults, using an optical motion capture system as a reference. Heel and toe trajectories are predicted with low errors, in line with reference trajectories. A good agreement is also achieved between the reference and estimated gait parameters, in particular when turning strides are excluded from the analysis. The performance of the method is shown to be robust to imperfect sensor-foot alignment conditions.