Human-robot interaction in Industry 4.0 based on an Internet of Things real-time gesture control system

Abstract

In the context of fast-growing digitization of industrial environments, Industry 4.0 aims to improve key elements to achieve more efficient processes, flexibility in customizing products and reduction in energy consumption, among other objectives. This paper presents a system that exploits the Internet of Things (IoT), massive data computation, and human-robot collaboration to reach these goals. The described system combines technological and human-centered aspects to enhance human-robot interaction. In fact, the human factor cannot be left aside when technological advances affecting society are foreseen. The proposal has been tested on a gesture control system that enables a natural interaction with a robotic arm through the use of IoT-oriented inertial measurement unit devices. These devices capture the movements of both human’s arms. Experiments of a technical nature have been run to measure accuracy and latency. In addition, human-centered tests have been conducted with real users to determine the level of intuitiveness and acceptance of the proposed gesture control. The results obtained demonstrate that the proposal meets the demands in terms of real-time, success rate, flexibility and scalability, which are fundamental requirements in Industry 4.0. The usability results have enabled drawing useful conclusions on the use of such human-robot interaction systems.

Lightweight encryption for short-range wireless biometric authentication systems in Industry 4.0

“…Industry 4.0 [1] represents an innovative technological approach, where Cyber-Physical Systems [2], Artificial Intelligence [35,72], automatic decision systems [23], optimization solutions [18], exponential technologies [74], robotics [73] and circular business models are building the productive fabric [3,4].…”

Comparison of RGB-D and IMU-based gesture recognition for human-robot interaction in remanufacturing

“…Our recognition system, presented in [11] where further detail can be found, is composed of three main parts:…”

“…The component around which the experiments were built is our LoMoCA algorithm. It was developed to recognize movements using a small number of samples [11]. The algorithm will not be addressed in depth in this paper.…”

“…To fill this gap, this work addresses the experimental performance evaluation of both gesture-recognition approaches in a remanufacturing use case. Based on our wearablebased movement recognition algorithm that was recently presented [11]-the so-called Low-Frequency Movement Characterization Algorithm (LoMoCA)-a set of improvements have been carried out in order to process data from two systems in real-time: (i) the Microsoft Kinect v2 RGB-D device (hereinafter Kinect)-one of the most extended computer-vision commercial devices [12]-and (ii) our developed OperaBLE wristband, which embeds low-frequency inertial measurement units (IMUs) with a low-power microcontroller unit (MCU), and a Bluetooth low energy (BLE) communication interface [13]. By defining a set of gestures oriented to change the end-effector's position (left, right, backward, forward shifts) as well as action-oriented gestures (clean, tool-specific, flip, right tool, left tool, and screw), different accuracy tests are performed and discussed.…”

Augmented Humanity: A Systematic Mapping Review

“…However, recent scientific discoveries and inventions have demonstrated that technology is also beginning to modify human capabilities, pushing them beyond their natural limits [2][3][4]. With the advance of technology, the interaction between humans and machines has been "improved", "augmented" or even "redesigned" [5][6][7][8]. This has made it not only interesting and intriguing but also viable and arising as a serious concept of scientific research and development [9,10].…”