Individualization of Intensity Thresholds on External Workload Demands in Women’s Basketball by K-Means Clustering: Differences Based on the Competitive Level

In previous studies found in the literature speed (SP), acceleration (ACC), deceleration (DEC), and impact (IMP) zones have been created according to arbitrary thresholds without considering the specific workload profile of the players (e.g., sex, competitive level, sport discipline). The use of statistical methods based on raw data could be considered as an alternative to be able to individualize these thresholds. The study purposes were to: (a) individualize SP, ACC, DEC, and IMP zones in two female professional basketball teams; (b) characterize the external workload profile of 5 vs. 5 during training sessions; and (c) compare the external workload according to the competitive level (first vs. second division). Two basketball teams were recorded during a 15-day preseason microcycle using inertial devices with ultra-wideband indoor tracking technology and microsensors. The zones of external workload variables (speed, acceleration, deceleration, impacts) were categorized through k-means clusters. Competitive level differences were analyzed with Mann–Whitney’s U test and with Cohen’s d effect size. Five zones were categorized in speed (<2.31, 2.31–5.33, 5.34–9.32, 9.33–13.12, 13.13–17.08 km/h), acceleration (<0.50, 0.50–1.60, 1.61–2.87, 2.88–4.25, 4.26–6.71 m/s2), deceleration (<0.37, 0.37–1.13, 1.14–2.07, 2.08–3.23, 3.24–4.77 m/s2), and impacts (<1, 1–2.99, 3–4.99, 5–6.99, 7–10 g). The women’s basketball players covered 60–51 m/min, performed 27–25 ACC-DEC/min, and experienced 134–120 IMP/min. Differences were found between the first and second division teams, with higher values in SP, ACC, DEC, and IMP in the first division team (p < 0.03; d = 0.21–0.56). In conclusion, k-means clustering can be considered as an optimal tool to categorize intensity zones in team sports. The individualization of external workload demands according to the competitive level is fundamental for designing training plans that optimize sports performance and reduce injury risk in sport.

Using Real-Time Tracking of Materials and Labor for Kit-Based Logistics Management in Construction

Improved productivity and the elimination of waste are key goals for lean methods in construction production control. One such lean method is a kit-based logistics management in which task-based materials are delivered just-in-time and aligned with assembly operations on-site. Digital platforms could enable a situational awareness of work and material flows, potentially increasing the benefit and applicability of kitting. The aim of the current research is to utilize a real-time indoor tracking of material and labor flows to evaluate an assembly kit–based management of construction projects. We propose a linked data framework to connect labor, material, and scheduling information to integrate heterogenous data. The contribution of the study is threefold: first, a feasible method is developed to enable real-time detection of work and material flows inside the building for logistics management purposes. Second, several key performance indicators for effective evaluation of kit-based production flow in construction are provided, which allows management to tackle root causes of problems and to enhance timely and productive logistic solutions. Thirdly, by applying the linked data method, the study introduces a novel approach to integrate heterogenous data from both indoor tracking and schedules.

An Ad Hoc Movement Monitoring Algorithm for Indoor Tracking During Examinations

Academic dishonesty includes cheating in examinations tests and any academic assignments, plagiarism, fabrication of information or citations, facilitation of acts of academic studies by others, unauthorized possession of examination materials, and tampering with the academic work of other researchers or scholars. These acts can be done before, during, and after the actual examination. This paper proposes a holistic algorithm for setting up an ad-hoc monitoring system to track candidates who leave the examination hall during the examination for either toilet breaks, medical breaks, or otherwise. Since most examinations are held indoors it is especially difficult to use existing GPS and GPRS based methods. We submit a proposal to deploy an indoor monitoring system based on the RSS of carefully placed receivers as they communicate with active tags held by the candidates. This paper outlines an algorithm that is used to place the receivers in such a way as to avoid any blind spots which may be utilized as cheating spots. The algorithm was deployed into a testbed system which was 100% foul proof. The proposed algorithm can be used to set up the deployment of the monitoring system in any environment where a clearly defined path is identified.

Indoor Tracking by Adding IMU and UWB using Unscented Kalman Filter

In recent days Internet of Things (IoT) applications becoming prominent, like smart home, connected health, smart farming, smart retail and smart manufacturing, will lead to a challenging task in providing low cost, high precision localization and tracking in indoor environments. Positioning in indoor is yet an open issue mostly because of not receiving the signals of GPS in the context of indoor. Inertial Measurement Unit (IMU) can give an exact indoor tracking, however, they regularly experience the cumulated error as the speed and position are gotten by incorporating the increasing acceleration constantly as for time. At the same time Ultra Wideband (UWB) localization and tracking will be influenced by the real time indoor conditions. It is difficult to utilize an independent localization and tracking system to accomplish high precision in indoor conditions. In this paper, we come up with an incorporated positioning system in indoor by joining IMU and the UWB over the Unscented Kalman Filter (UKF) and the Extended Kalman Filter (EKF) to enhance the precision. All these algorithms are analyzed and assessed dependent on their exhibition.