Accelerometer-based wearables offer a cost-effective solution for managing match and training loads in invasion team sports. However, a multitude of acceleration-derived metrics, each employing different algorithms, has led to inconsistent and incomparable outcomes across studies and devices. This article reviews the mathematical procedures underlying whole-body mechanical load metrics, clarifies their conceptual differences, and proposes refinements to enhance standardization. Synthetic data were employed to investigate conceptual differences, while experimental accelerometric data (463 time series) from a set of elite handball training sessions (involving 16 players) were used to implement the corrected equations and analyze statistical relationships. Analysis of synthetic data revealed that derivative-based metrics, such as Jerk Modulus (typically referred to as Player Load) and corrected Accel’Rate (cAccel’Rate), tend to amplify noise compared to acceleration-based metrics, such as universal Dynamic Stress Load (uDSL) and Body Load. Experimental results indicated that when metrics were summed, their values were nearly identical. In time-series comparisons, Jerk Modulus and cAccel’Rate were predictably found to be nearly identical, while Body Load was the most distinct. Acceleration-based metrics are preferable to derivative-based ones. Sports scientists should lead the design and validation of such metrics, ensuring methodological rigor, transparency, and innovation while preventing commercial interests from introducing rebranded variables with undisclosed scaling factors and unclear calculations.

Spatiotemporal modulation (STM) is used in Ultrasonic Mid-Air Haptics to create compelling tactile sensations. The STM can create perceptually distinct sensations. We specified the sensations of a palm-size pattern by varying the focal point's speed and pattern sampling rate. Three sensations were specified, named as Dynamic, Vibratory and Uniform. A selective identification study was conducted to evaluate if the sensations were recognizable to the perception when presented individually and simultaneously (combined stimuli). The results support the STM's specification and the selective recognition of the sensations was possible for some combinations.

Persistent Homology (PH) analysis is a powerful tool for understanding many relevant topological features from a given dataset. PH allows finding clusters, noise, and relevant connections in the dataset. Therefore, it can provide a better view of the problem and a way of perceiving if a given dataset is equal to another, if a given sample is relevant, and how the samples occupy the feature space. However, PH involves reducing the problem to its simplicial complex space, which is computationally expensive and implementing PH in such Resource-Scarce Embedded Systems (RSES) is an essential add-on for them. However, due to its complexity, implementing PH in such tiny devices is considerably complicated due to the lack of memory and processing power. The following paper shows the implementation of 0-Dimensional Persistent Homology Analysis in a set of well-known RSES, using a technique that reduces the memory footprint and processing power needs of the 0-Dimensional PH algorithm. The results are positive and show that RSES can be equipped with this real-time data analysis tool.

As more and more devices are being deployed across networks to gather data and use them to perform intelligent tasks, it is vital to have a tool to perform real-time data analysis. Data are the backbone of Machine Learning models, the core of intelligent systems. Therefore, verifying whether the data being gathered are similar to those used for model building is essential. One fantastic tool for the performance of data analysis is the 0-Dimensional Persistent Diagrams, which can be computed in a Resource-Scarce Embedded System (RSES), a set of memory and processing-constrained devices that are used in many IoT applications because they are cost-effective and reliable. However, it is challenging to compare Persistent Diagrams, and Persistent Landscapes are used because they allow Persistent Diagrams to be passed to a space where the mean concept is well-defined. The following work shows how one can perform a Persistent Landscape analysis in an RSES. It also shows that the distance between two Persistent Landscapes makes it possible to verify whether two devices collect the same data. The main contribution of this work is the implementation of Persistent Landscape analysis in an RSES, which is not provided in the literature. Moreover, it shows that devices can now verify, in real-time, whether they can trust the data being collected to perform the intelligent task they were designed to, which is essential in any system to avoid bugs or errors.