Joint Optimization of Kinematics and Anthropometrics for Human Motion Denoising

Human Motion Enhancement and Restoration via Unconstrained Human Structure Learning

Human motion capture technology, which leverages sensors to track the movement trajectories of key skeleton points, has been progressively transitioning from industrial applications to broader civilian applications in recent years. It finds extensive use in fields such as game development, digital human modeling, and sport science. However, the affordability of these sensors often compromises the accuracy of motion data. Low-cost motion capture methods often lead to errors in the captured motion data. We introduce a novel approach for human motion reconstruction and enhancement using spatio-temporal attention-based graph convolutional networks (ST-ATGCNs), which efficiently learn the human skeleton structure and the motion logic without requiring prior human kinematic knowledge. This method enables unsupervised motion data restoration and significantly reduces the costs associated with obtaining precise motion capture data. Our experiments, conducted on two extensive motion datasets and with real motion capture sensors such as the SONY (Tokyo, Japan) mocopi, demonstrate the method's effectiveness in enhancing the quality of low-precision motion capture data. The experiments indicate the ST-ATGCN's potential to improve both the accessibility and accuracy of motion capture technology.

A Review of Depth-Based Human Motion Enhancement: Past and Present

In this article, we survey the current research trends of enhancement and denoising of depth-based motion capture data (D-Mocap) and also discuss possible future research issues. We first present the commonly used problem formulation for human motion enhancement. We then review related work and cover a broad set of methodologies including filtering based, learning based, and evolutionary based approaches. In addition, we present some important experiments-related issues, such as data creation or collection, reference data generation, and the metrics used for performance evaluation. It is our intent to provide a comprehensive tutorial and survey on the recent efforts on D-Mocap improvement, both methodologically and experimentally. By comparing the state-of-the-art methods, we also propose future research needs that could make D-Mocap more useful and relevant for real-world clinical applications.

Smart deployment of IoT-TelosB service care StreamRobot using software-defined reliability optimisation design

Intelligent service care robots have increasingly been developed in mission-critical sectors such as healthcare systems, transportation, manufacturing, and environmental applications. The major drawbacks include the open-source Internet of Things (IoT) platform vulnerabilities, node failures, computational latency, and small memory capacity in IoT sensing nodes. This article provides reliable predictive analytics with the optimisation of data transmission characteristics in StreamRobot. Software-defined reliable optimisation design is applied in the system architecture. For the IoT implementation, the edge system model formulation is presented with a focus on edge cluster log-normality distribution, reliability, and equilibrium stability considerations. A real-world scenario for accurate data streams generation from in-built TelosB sensing nodes is converged at a sink-analytic dashboard. Two-phase configurations, namely off-taker and on-demand, link-state protocols are mapped for deterministic data stream offloading. An orphan reconnection trigger mechanism is used for reliable node-to-sink resilient data transmissions. Data collection is achieved, using component-based programming in the experimental testbed. Measurement parameters are derived with TelosB IoT nodes. Reliability validations on remote monitoring and prediction processes are studied considering neural constrained software-defined networking (SDN) intelligence. An OpenFlow-SDN construct is deployed to offload traffic from the edge to the fog layer. At the core, fog detection-to-cloud predictive machine learning (FD-CPML) is used to predict real-time data streams. Prediction accuracy is validated with decision tree, logistic regression, and the proposed FD-CPML. The data streams latency gave 40.00%, 33.33%, and 26.67%, respectively. Similarly, linear predictive scalability behaviour on the network plane gave 30.12%, 33.73%, and 36.15% respectively. The results show satisfactory responses in terms of reliable communication and intelligent monitoring of node failures.