Innovative Collaborative Method for Interaction between a Human Operator and Robotic Manipulator Using Pointing Gestures

The concept of “Industry 4.0” relies heavily on the utilization of collaborative robotic applications. As a result, the need for an effective, natural, and ergonomic interface arises, as more workers will be required to work with robots. Designing and implementing natural forms of human–robot interaction (HRI) is key to ensuring efficient and productive collaboration between humans and robots. This paper presents a gestural framework for controlling a collaborative robotic manipulator using pointing gestures. The core principle lies in the ability of the user to send the robot’s end effector to the location towards, which he points to by his hand. The main idea is derived from the concept of so-called “linear HRI”. The framework utilizes a collaborative robotic arm UR5e and the state-of-the-art human body tracking sensor Leap Motion. The user is not required to wear any equipment. The paper describes the overview of the framework’s core method and provides the necessary mathematical background. An experimental evaluation of the method is provided, and the main influencing factors are identified. A unique robotic collaborative workspace called Complex Collaborative HRI Workplace (COCOHRIP) was designed around the gestural framework to evaluate the method and provide the basis for the future development of HRI applications.

PROPOSTA DE SISTEMA DE CAPTAÇÃO DE MEDIDAS ANTROPOMÉTRICAS POR INFRAVERMELHO

Esta pesquisa tem como objetivo propor o desenvolvimento de um sistema de coleta de medidas antropométricas utilizando sensores de profundidade por luz estruturada infravermelha. Buscou-se propor um sistema de medições antropométricas de baixo custo, mais acessível, com medições rápidas e precisas, e com maior conforto para o usuário, quando comparado com os métodos convencionais de medição antropométrica. Desse modo, foi desenvolvido um modelo de medição aplicando um sistema de body tracking e skeleton tracking (rastreamento de articulações), fornecido por bibliotecas computacionais desenvolvidas para o sensor Kinect for Windows. Juntamente, com modelo de medição utilizando o body tracking, foi proposto um projeto conceitual para esse modelo, que contém os hardwares utilizados, um totem de autoatendimento modelado em CAD e um processo de medição. O sistema mede os comprimentos dos membros do usuário, os desvios, entre lado esquerdo e direito do corpo, de ombro, quadril e joelho, e os desvios posturais. Essa pesquisa se trata de um modelo e projeto conceitual. Portanto, não se pode afirmar que tudo que foi desenvolvido e modelado será executado do modo apresentado para trabalhos futuros.,

Construction of Vertebral Body Tracking Algorithm Based on Dynamic Imaging Parameter Measurement and Its Application in the Treatment of Lumbar Instability

Static imaging measurements could not truly reflect the dynamic panorama of the lumbar movement process, and the abnormal activities between the lumbar vertebrae and their dynamic balance could not be observed, resulting in difficulties in the mechanism analysis of lumbar instability and the efficacy evaluation of manipulation therapy. Therefore, this paper constructed a vertebral tracking algorithm based on dynamic imaging parameter measurement through imaging parameter measurement and calculation. According to the imaging data obtained by vertebral body tracking algorithm, the corresponding statistical methods were used to compare the functional scores before and after manipulation and the changes of imaging data, so as to evaluate the therapeutic effect of manipulation on lumbar instability. Through the clinical observation and imaging analysis of 15 patients with lumbar instability before and after manipulation treatment, it is verified that the vertebra tracking algorithm is effective in the vertebra tracking and plays a positive role in the treatment of lumbar instability.

AZURE KINECT BODY TRACKING UNDER REVIEW FOR THE SPECIFIC CASE OF UPPER LIMB EXERCISES

A tool for human pose estimation and quantification using consumer-level equipment is a long-pursued objective. Many studies have employed the Microsoft Kinect v2 depth camera but with recent release of the new Kinect Azure a revision is required. This work researches the specific case of estimating the range of motion in five upper limb exercises using four different pose estimation methods. These exercises were recorded with the Kinect Azure camera and assessed with the OptiTrack motion tracking system as baseline. The statistical analysis consisted of evaluation of intra-rater reliability with intra-class correlation, the Pearson correlation coefficient and Bland–Altman statistical procedure. The modified version of the OpenPose algorithm with the post-processing algorithm PoseFix had excellent reliability with most intra-class correlations being over 0.75. The Azure body tracking algorithm had intermediate results. The results obtained justify clinicians employing these methods, as quick and low-cost simple tools, to assess upper limb angles.

Advanced human-robot interaction system based on human communication recognition

This work describes the implementation of various human-robot interaction systems in a functioning mobile robot. This project is the result of integrating a tracking system for human faces and objects, face recognition, gesture recognition, body tracking, stereo-vision, speech synthesis, and voice recognition. The majority of these systems are custom designed for this particular project, these systems and how they were designed are explained in detail throughout this report. A unique vector-based approach is used for gesture recognition. There is minimal focus on the mechanics and electronics of the human-robot interaction system, but rather on the information processing of the robot. Using combinations of many information processing systems will allow robots to interact with human users more naturally, and will provide a natural conduit for future cooperative human-robot efforts. This project lays the groundwork for what will be a large collaborative effort aimed at creating possibly one of the most advanced human interactive robot in the world.

Advanced human-robot interaction system based on human communication recognition

This work describes the implementation of various human-robot interaction systems in a functioning mobile robot. This project is the result of integrating a tracking system for human faces and objects, face recognition, gesture recognition, body tracking, stereo-vision, speech synthesis, and voice recognition. The majority of these systems are custom designed for this particular project, these systems and how they were designed are explained in detail throughout this report. A unique vector-based approach is used for gesture recognition. There is minimal focus on the mechanics and electronics of the human-robot interaction system, but rather on the information processing of the robot. Using combinations of many information processing systems will allow robots to interact with human users more naturally, and will provide a natural conduit for future cooperative human-robot efforts. This project lays the groundwork for what will be a large collaborative effort aimed at creating possibly one of the most advanced human interactive robot in the world.