A sensing platform for physiological and contextual feedback to tennis athletes

In this paper we describe our work on creating a multi-modal sensing platform for providing feedback to tennis coaches and players. The platform includes a fixed installation around a tennis court consisting of a video camera network and a localisation system as well as wearable sensing technology deployed to individual athletes. We describe the various components of this platform and explain how we can capture synchronised multi-modal sensor data streams for games or training sessions. We then describe the content-based retrieval system we are building to facilitate the development of novel coaching tools. We provide some examples of the queries that the system can support, where these queries are chosen to be suitably expressive so as to reflect a coach's complex information needs regarding tennis-related performance factors

A multi-modal dance corpus for research into real-time interaction between humans in online virtual environments

We present a new, freely available, multimodal corpus for research into, amongst other areas, real-time realistic interaction between humans in online virtual environments. The specific corpus scenario focuses on an online dance class application scenario where students, with avatars driven by whatever 3D capture technology are locally available to them, can learn choerographies with teacher guidance in an online virtual ballet studio. As the data corpus is focused on this scenario, it consists of student/teacher dance choreographies concurrently captured at two different sites using a variety of media modalities, including synchronised audio rigs, multiple cameras, wearable inertial measurement devices and depth sensors. In the corpus, each of the several dancers perform a number of fixed choreographies, which are both graded according to a number of specific evaluation criteria. In addition, ground-truth dance choreography annotations are provided. Furthermore, for unsynchronised sensor modalities, the corpus also includes distinctive events for data stream synchronisation. Although the data corpus is tailored specifically for an online dance class application scenario, the data is free to download and used for any research and development purposes

TennisSense: a platform for extracting semantic information from multi-camera tennis data

In this paper, we introduce TennisSense, a technology platform for the digital capture, analysis and retrieval of tennis training and matches. Our algorithms for extracting useful metadata from the overhead court camera are described and evaluated. We track the tennis ball using motion images for ball candidate detection and then link ball candidates into locally linear tracks. From these tracks we can infer when serves and rallies take place. Using background subtraction and hysteresis-type blob tracking, we track the tennis players positions. The performance of both modules is evaluated using ground-truthed data. The extracted metadata provides valuable information for indexing and efficient browsing of hours of multi-camera tennis footage and we briefly illustrative how this data is used by our tennis-coach playback interface

Failure Analysis Of Rotating Equipment Using Vibration Studies And Signal Processing Techniques

This thesis focuses on failure analysis of rotating machines based on vibration analysis and signal processing techniques. The main objectives are: identifying machine’s condition, determining the faults specific response, creating methods to correct the faults, and investigating available statistical analysis methods for automatic fault detection and classification. In vibration analysis, the accelerometer data is analyzed in time and frequency domain which will determine the machine’s condition by identifying the characteristic frequencies of the faults. These fault frequencies are specific for each type of machine’s faults. Therefore, they are referred to as faults’ signatures. The most common faults of the rotating machines are unbalanced load torque, misaligned shaft, looseness, and bearing faults. The second objective is to find correction methods for rectifying the faulty situations. Therefore, correction methods for the unbalanced condition are comprehensively studied and a novel method for balancing an unbalanced rotor is developed which is based on image processing methods and results in lowering machine’s vibrations. Another objective of this research is to collect huge amount of vibration data and implement statistical data analysis methods to categorize different machine’s conditions. Therefore, principal components analysis, K-nearest neighbor, and singular value decomposition are implemented to identify different faults of the rotating machines automatically. The statistical methods have demonstrated high precision in classifying different faulty situations. Fault identification at early stages will enhance machine’s health and reduces the maintenance costs significantly. The statistical methods are easy to implement, and have disaffected the need for an expert maintenance engineer and will identify the machine’s fault automatically

LifeLogging: personal big data

We have recently observed a convergence of technologies to foster the emergence of lifelogging as a mainstream activity. Computer storage has become significantly cheaper, and advancements in sensing technology allows for the efficient sensing of personal activities, locations and the environment. This is best seen in the growing popularity of the quantified self movement, in which life activities are tracked using wearable sensors in the hope of better understanding human performance in a variety of tasks. This review aims to provide a comprehensive summary of lifelogging, to cover its research history, current technologies, and applications. Thus far, most of the lifelogging research has focused predominantly on visual lifelogging in order to capture life details of life activities, hence we maintain this focus in this review. However, we also reflect on the challenges lifelogging poses to an information retrieval scientist. This review is a suitable reference for those seeking a information retrieval scientist’s perspective on lifelogging and the quantified self