Evaluating positioning systems has become a matter of heated debate during the last years. There is no clear standard on how these technologies should be evaluated, and no predominant solution for defining the ground truth in order to compare the position estimates. In this paper, we propose a simple and inexpensive solution for tackling both of these problems in real life use cases. With the proposed methodology, it is possible to measure both static and moving targets, by creating a predefined path with checkpoints. Then, a tester, walking over them, while moving or standing still, indicates when the device was over the aforementioned checkpoints. It is also specified how to evaluate the estimates by comparing them with interpolated points of the ground truth trajectory. Two methods are proposed for performing such interpolation. Finally, in order to evaluate the performance of the positioning system as well as the perceived utility of the position estimates from the end user's point of view, a series of statistical parameters is discussed. Additionally, in the context of perceived utility by the end user, a parameter that measures the occurrence of abrupt changes in the position estimates is proposed.

Quantitative information on how well a horse clears a jump has great potential to support the rider in improving the horse’s jumping performance. This study investigated the validation of a GPS-based inertial measurement unit, namely Alogo Move Pro, compared with a traditional optical motion capture system. Accuracy and precision of the three jumping characteristics of maximum height (Zmax), stride/jump length (lhorz), and mean horizontal speed (vhorz) were compared. Eleven horse–rider pairs repeated two identical jumps (an upright and an oxer fence) several times (n = 6 to 10) at different heights in a 20 × 60 m tent arena. The ground was a fiber sand surface. The 24 OMC (Oqus 7+, Qualisys) cameras were rigged on aluminum rails suspended 3 m above the ground. The Alogo sensor was placed in a pocket on the protective plate of the saddle girth. Reflective markers placed on and around the Alogo sensor were used to define a rigid body for kinematic analysis. The Alogo sensor data were collected and processed using the Alogo proprietary software; stride-matched OMC data were collected using Qualisys Track Manager and post-processed in Python. Residual analysis and Bland–Altman plots were performed in Python. The Alogo sensor provided measures with relative accuracy in the range of 10.5–20.7% for stride segments and 5.5–29.2% for jump segments. Regarding relative precision, we obtained values in the range of 6.3–14.5% for stride segments and 2.8–18.2% for jump segments. These accuracy differences were deemed good under field study conditions where GPS signal strength might have been suboptimal.

Background and Objective: In the context of the rapidly aging global population, the older adult vulnerability poses a significant challenge for public health systems. Frailty, cognitive and nutritional status, depression, and grip strength are essential parameters for staging the vulnerability of older adults. The objective of this study is to identify a rapid but multidimensional geriatric assessment tool that can enhance the rehabilitation process for older adults, tailored to their specific needs. Materials and Methods: This pilot study examines the relationships between grip strength, nutritional status, frailty, depression, and cognition in a group of 80 older adults with a mean age of 69.6 years, 49 male and 31 female, using standardized geriatric scales and digital grip strength measurements. The study employed a digital dynamometer, a portable and reliable tool that facilitated quick and accurate grip strength measurements. Results: The analysis revealed significant correlations among the parameters. Greater grip strength was associated with better cognitive performance (r = 0.237, p = 0.034) and improved nutritional status (r = 0.267, p = 0.016), while it was inversely related to frailty (r = −0.313, p = 0.005). Nutritional status also played a key role, showing an inverse relationship with frailty (r = −0.333, p = 0.003) and depression levels (r = −0.248, p = 0.027). Furthermore, frailty and depression were strongly interconnected, with those experiencing higher frailty levels also displaying more severe depressive symptoms (r = 0.545, p < 0.001). Marital status was also relevant: married participants exhibited higher grip strength, lower frailty, and fewer depressive symptoms, suggesting that social support positively influences both physical and mental health in older adults. Conclusions: These findings not only emphasize the need for integrated care approaches that simultaneously address physical health, nutrition, and cognitive function, but also provide a foundation for the development of a rapid and multidimensional assessment protocol, which consists of using a digital dynamometer and four geriatric scales. Such a tool could play a crucial role in the early detection of frailty syndrome and guide the implementation of multidisciplinary, tailored therapeutic strategies aimed at preserving the autonomy and improving the quality of life of older adults.

The Foreign Exchange market is a significant market for speculators, characterized by substantial transaction volumes and high volatility. Accurately predicting the directional movement of currency pairs is essential for formulating a sound financial investment strategy. This paper conducts a comparative analysis of various machine learning models for predicting the daily directional movement of the EUR/USD currency pair in the Foreign Exchange market. The analysis includes both decorrelated and non-decorrelated feature sets using Principal Component Analysis. Additionally, this study explores meta-estimators, which involve stacking multiple estimators as input for another estimator, aiming to achieve improved predictive performance. Ultimately, our approach yielded a prediction accuracy of 58.52% for one-day ahead forecasts, coupled with an annual return of 32.48% for the year 2022.

Accurate localization is crucial for numerous applications. While several methods exist for outdoor localization, typically relying on GPS signals, these approaches become unreliable in environments subject to a weak GPS signal or GPS outage. Many researchers have attempted to address this limitation, primarily focusing on real-time solutions. However, for applications that do not require real-time localization, these methods remain suboptimal. This paper presents a novel Transformer-based bidirectional encoder approach to address, in postprocessing, the localization challenges during GPS weak signal phases or GPS outages. Our method predicts the velocity during periods of weak or lost GPS signals and calculates the position through bidirectional velocity integration. Additionally, it incorporates position interpolation to ensure smooth transitions between active GPS and GPS outage phases. Applied to a dataset tracking horse positions—which features velocities up to 10 times those of pedestrians and higher acceleration—our approach achieved an average trajectory error below 3 m, while maintaining stable relative distance errors regardless of the GPS outage duration.

Sensing the presence of people in indoor spaces allows smart systems to be aware of and responsive to the occupants, and paves the way for a wide range of applications. In this paper, we show how the reflection patterns of ultrasonic signals can be leveraged to detect the presence of still persons. We propose the use of supervised learning over segmented reflection patterns, and prove that this method is capable of detecting minute variations in the environment's response. The experimental evaluation of the proposed method in an office and a residential environment shows that it achieves a high presence sensing accuracy in the case of low signal-to-noise ratio (SNR), and a perfect accuracy in the case of high SNR, even in the case of non line-of-sight. Among the different tested classifiers, we found that the linear Support Vector Machine (SVM) achieves the best performance, yielding a presence detection accuracy of 84.3%-98.4% for low SNR, and 100% for high SNR, in the tested environments.