Traditional marker-based motion capture requires excessive and specialized equipment, hindering accessibility and wider adoption. In this work, we demonstrate such a system but rely on a very sparse set of low-cost consumer-grade sensors. Our system exploits a data-driven backend to infer the captured subject's joint positions from noisy marker estimates in real-time. In addition to reduced costs and portability, its inherent denoising nature allows for quicker captures by alleviating the need for precise marker placement and post-processing, making it suitable for interactive virtual reality applications.

Depth perception is considered an invaluable source of information for various vision tasks. However, depth maps acquired using consumer-level sensors still suffer from non-negligible noise. This fact has recently motivated researchers to exploit traditional filters, as well as the deep learning paradigm, in order to suppress the aforementioned non-uniform noise, while preserving geometric details. Despite the effort, deep depth denoising is still an open challenge mainly due to the lack of clean data that could be used as ground truth. In this paper, we propose a fully convolutional deep autoencoder that learns to denoise depth maps, surpassing the lack of ground truth data. Specifically, the proposed autoencoder exploits multiple views of the same scene from different points of view in order to learn to suppress noise in a self-supervised end-to-end manner using depth and color information during training, yet only depth during inference. To enforce self-supervision, we leverage a differentiable rendering technique to exploit photometric supervision, which is further regularized using geometric and surface priors. As the proposed approach relies on raw data acquisition, a large RGB-D corpus is collected using Intel RealSense sensors. Complementary to a quantitative evaluation, we demonstrate the effectiveness of the proposed self-supervised denoising approach on established 3D reconstruction applications. Code is avalable at https://github.com/VCL3D/DeepDepthDenoising.

Cardiac Rehabilitation (CR) can significantly improve mortality and morbidity rates from Cardiovascular Diseases (CVD). Nevertheless, traditional CR is diminished by low subsequent adherence rates. Thus, in this paper, an e-Health technological module for human motion analysis and user modelling is proposed, in order to address the requirements of unsupervised, tele-rehabilitation systems for CVD, by evaluating and personalizing prescribed physical CR programs. The proposed module consists of a) an exercise capturing and evaluation component, and b) a user modelling and decision support system for personalization of cardiac rehabilitation programs. In particular, the module monitors and analyses the body movements of the patient when exercising in real-time, while based on this analysis and the heart-rate measurements, it is capable of short-term and long-term CR session adaptation. The proposed module constitutes a significant tool for internet-enabled sensor-based home exercise platforms.

The latest developments in 3D capturing, processing, and rendering provide means to unlock novel 3D application pathways. The main elements of an integrated platform, which target tele-immersion and future 3D applications, are described in this paper, addressing the tasks of real-time capturing, robust 3D human shape/appearance reconstruction, and skeleton-based motion tracking. More specifically, initially, the details of a multiple RGB-depth (RGB-D) capturing system are given, along with a novel sensors' calibration method. A robust, fast reconstruction method from multiple RGB-D streams is then proposed, based on an enhanced variation of the volumetric Fourier transform-based method, parallelized on the Graphics Processing Unit, and accompanied with an appropriate texture-mapping algorithm. On top of that, given the lack of relevant objective evaluation methods, a novel framework is proposed for the quantitative evaluation of real-time 3D reconstruction systems. Finally, a generic, multiple depth stream-based method for accurate real-time human skeleton tracking is proposed. Detailed experimental results with multi-Kinect2 data sets verify the validity of our arguments and the effectiveness of the proposed system and methodologies.

We present a demonstration of a multi-modal 3D capturing platform coupled to a motion comparison system. This work is focused on the preservation of Traditional Sports and Games, namely the Gaelic sports from Ireland and Basque sports from France and Spain. Users can learn, compare and compete in the performance of sporting gestures and compare themselves to real athletes. Our online gesture database provides a way to preserve and display a wide range of sporting gestures. The capturing devices utilised are Kinect 2 sensors and wearable inertial sensors, where the number required varies based on the requested scenario. The fusion of these two capture modalities, coupled to our inverse kinematic algorithm, allow us to synthesize a fluid and reliable 3D model of the user gestures over time. Our novel comparison algorithms provide the user with a performance score and a set of comparison curves (i.e. joint angles and angular velocities), providing a precise and valuable feedback for coaches and players.

We present a novel, low-cost, interactive, exercise-based rehabilitation system. Our research involves the investigation and development of patient-centric, sensor-based rehabilitation games and surrounding technologies. HeartHealth is designed to provide a safe, personalised and fun exercise environment that could be deployed in any exercise based rehabilitation program. HeartHealth utilises a cloud-based patient information management system built on FIWARE Generic Enablers,and motion tracking coupled with our sophisticated motion comparison algorithms. Users can record customised exercises through a doctors interface and then play the rehabilitation game where they must perform a sequence of their exercises in order to complete the game scenario. Their exercises are monitored, recorded and compared by our Motion Evaluation software and real-time feedback is than given based on the users performance.