Prediction of the Ball Location on the 2D Plane in Football Using Optical Tracking Data

Tracking the ball location is essential for automated game analysis in complex ball-centered team sports such as football. However, it has always been a challenge for image processing-based techniques because the players and other factors often occlude the view of the ball. This study proposes an automated machine learning-based method for predicting the ball location from players' behavior on the pitch. The model has been built by processing spatial information of players acquired from optical tracking data. Optical tracking data include samples from 300 matches of the 2017-2018 season of the Turkish Football Federation's Super League. We use neural networks to predict the ball location in 2D axes. The average coefficient of determination of the ball tracking model on the test set both for the x-axis and the y-axis is accordingly 79% and 92%, where the mean absolute error is 7.56 meters for the x-axis and 5.01 meters for the y-axi

Base Station Power Optimization for Green Networks Using Reinforcement Learning

The next generation mobile networks have to provide high data rates, extremely low latency, and support high connection density. To meet these requirements, the number of base stations will have to increase and this increase will lead to an energy consumption issue. Therefore “green” approaches to the network operation will gain importance. Reducing the energy consumption of base stations is essential for going green and also it helps service providers to reduce operational expenses. However, achieving energy savings without degrading the quality of service is a huge challenge. In order to address this issue, we propose a machine learning based intelligent solution that also incorporates a network simulator. We develop a reinforcement-based learning model by using deep deterministic policy gradient algorithm. Our model update frequently the policy of network switches in a way that, packet be forwarded to base stations with an optimized power level. The policies taken by the network controller are evaluated with a network simulator to ensure the energy consumption reduction and quality of service balance. The reinforcement learning model allows us to constantly learn and adapt to the changing situations in the dynamic network environment, hence having a more robust and realistic intelligent network management policy set. Our results demonstrate that energy efficiency can be enhanced by 32% and 67% in dense and sparse scenarios, respectively

Spor Uygulamalarinda Yorumlanabilir Makine Öğrenmesi

Makine öğrenmesi ve insanlar birbirlerini tamamlayan becerilere sahipler. İnsanlar, bilgi ve deneyimlerinden soyutlama ve alanlar arasında bilginin aktarımı konusunda daha iyidir. Makine öğrenmesi, ham veri hesaplamada daha iyi ve hızlıdır. Projemin amacı, makine öğrenmesi algoritmalarını insan tarafından yorumlanabilir özelliklerle donatarak aradaki olası kopukluğu dolduracak bir çerçeve model geliştirmektir. Böylece, insanlar makine öğrenmesi teknikleriyle ilgili derinlemesine bilgi sahibi olmadan da büyük veriyi kullanarak daha iyi kararlar verebilmek ya da otomatik sistemin verdiği kararları yorumlayabilmek için bu sistemlerle etkin etkileşime girebileceklerdir. Bu şekilde her ikisinin de tek başına elde edemeyecekleri kadar iyi bir sonucu elde edebilmek uyum içinde çalışmalarının önü açılacaktır

Multi-Resident Human Behaviour Identification in Ambient Assisted Living Environments

Multimodal interactions in ambient assisted living environments require human behaviour to be recognized and monitored automatically. The complex nature of human behaviour makes it extremely difficult to infer and adapt to, especially in multi-resident environments. This proposed research aims to contribute to the multimodal interaction community by (i) providing publicly available, naturalistic, rich and annotated datasets for human behaviour modeling, (ii) introducing evaluation methods of several inference methods from a behaviour monitoring perspective, (iii) developing novel methods for recognizing individual behaviour in multi-resident smart environments without assuming any person identification, (iv) proposing methods for mitigating the scalability issues by using transfer, active, and semi-supervised learning techniques. The proposed studies will address both practical and methodological aspects of human behaviour recognition in smart interactive environments

WeCare: Wireless enhanced healthcare

In-home pervasive healthcare systems provide rich contextual information and alerting mechanisms against odd conditions with continuous monitoring. This minimizes the need for caregivers and help the chronically ill and elderly to survive an independent life. In this study, we present a webbased indoor monitoring system, namely WeCare, and showed the applicability of multi-modal sensor network technologies on healthcare monitoring

Using Wireless Sensor Network Technologies for Elder and Child Care: An Application Architecture Proposal

For the last decade wireless sensors, especially multi-modal sensor technologies have been developing for supporting healthcare services for elderly and children. The main motivation behind this is the fact that the world's expectations for an average lifetime is getting longer, thus increasing the costs of healthcare. The need for technological support on the subject has led wireless sensor technologies and sophisticated electronics to become within the reach of average users and research effort on the subject has attained much importance. The Radio Frequency Identification (RFID), which has already been used in retail and the wireless biological and environmental sensors have begun to be used in healthcare and smart remote monitoring applications. In this paper, we investigate the issues to be considered while designing smart remote monitoring applications and propose an appropriate architecture

Wireless sensor networks for healthcare: A survey

Becoming mature enough to be used for improving the quality of life, wireless sensor network technologies are considered as one of the key research areas in computer science and healthcare application industries. The pervasive healthcare systems provide rich contextual information and alerting mechanisms against odd conditions with continuous monitoring. This minimizes the need for caregivers and helps the chronically ill and elderly to survive an independent life, besides provides quality care for the babies and little children whose both parents have to work. Although having significant benefits, the area has still major challenges which are investigated in this paper. We provide several state of the art examples together with the design considerations like unobtrusiveness, scalability, energy efficiency, security and also provide a comprehensive analysis of the benefits and challenges of these systems

Quantifying the value of sprints in elite football using spatial cohesive networks

Football players are on the move during games and the sprint is one of the distinctive type of those movements. In this study, we focus on quantifying the value of the sprints using the spatial data of players and the collective movements of the teams during the game. We first propose a method to quantify the dispersion of the teams, namely, the weighted team spread. In order to find the weights of the team spread, we use individual players’ interaction behavior, using spatial cohesion matrices. Spatial features of the pitch such as the pitch value and the pass probability value are also used together with the weighted team spread to quantify the value of the sprints. These models are used to understand sprint character of the players according to their role and teams’ collective movements depending on their tactics. The proposed method applied on 306 Turkish first division games from 2018/2019. The sprint analysis results show that attackers have greater sprint averages than midfielders and defenders based on 5498 sprints from corresponding games. Full-backs and attacking midfielders are positions with the best sprint averages other than attacking players. Center backs and defensive midfielders are the weakest positions in sprinting. The results further show that the teams that are focused on having the possession of the ball have less average sprint value than teams playing counter-attack style

Multi-resident activity tracking and recognition in smart environments

During last decade, smart homes in which the activities of the residents are monitored automatically have been developed and demonstrated. However, smart homes with multiple residents still remains an open challenge. In order to tackle the multiple resident concurrent activity recognition problem in smart homes equipped with interaction-based sensors and with multiple residents, we propose two different approaches. In the first approach, we use a factorial hidden Markov model for modeling two separate chains corresponding to two residents. Secondly, we use nonlinear Bayesian tracking for decomposing the observation space into the number of residents. As opposed to the previous studies, we handle multiple residents at the same time without assuming any explicit identification mechanisms. We perform two experiments on real-world multi-resident Activity Recognition with Ambient Sensing data sets. In each experiment, we compare the proposed approach with a counterpart method. We also compare each approach with the manually separated observation performances. We show that both of the proposed methods consistently outperform their counterparts in both houses of the data sets and for both residents. We also discuss the advantages and disadvantages of each approach in terms of run time complexity, flexibility and generalizability

Obstruction-Aware Signal-Loss-Tolerant Indoor Positioning Using Bluetooth Low Energy

Indoor positioning is getting increased attention due to the availability of larger and more sophisticated indoor environments. Wireless technologies like Bluetooth Low Energy (BLE) may provide inexpensive solutions. In this paper, we propose obstruction-aware signal-loss-tolerant indoor positioning (OASLTIP), a cost-effective BLE-based indoor positioning algorithm. OASLTIP uses a combination of techniques together to provide optimum tracking performance by taking into account the obstructions in the environment, and also, it can handle a loss of signal. We use running average filtering to smooth the received signal data, multilateration to find the measured position of the tag, and particle filtering to track the tag for better performance. We also propose an optional receiver placement method and provide the option to use fingerprinting together with OASLTIP. Moreover, we give insights about BLE signal strengths in different conditions to help with understanding the effects of some environmental conditions on BLE signals. We performed extensive experiments for evaluation of the OASLTool we developed. Additionally, we evaluated the performance of the system both in a simulated environment and in real-world conditions. In a highly crowded and occluded office environment, our system achieved 2.29 m average error, with three receivers. When simulated in OASLTool, the same setup yielded an error of 2.58 m