Fast human motion prediction for human-robot collaboration with wearable interfaces

In this paper, we aim at improving human motion prediction during human-robot collaboration in industrial facilities by exploiting contributions from both physical and physiological signals. Improved human-machine collaboration could prove useful in several areas, while it is crucial for interacting robots to understand human movement as soon as possible to avoid accidents and injuries. In this perspective, we propose a novel human-robot interface capable to anticipate the user intention while performing reaching movements on a working bench in order to plan the action of a collaborative robot. The proposed interface can find many applications in the Industry 4.0 framework, where autonomous and collaborative robots will be an essential part of innovative facilities. A motion intention prediction and a motion direction prediction levels have been developed to improve detection speed and accuracy. A Gaussian Mixture Model (GMM) has been trained with IMU and EMG data following an evidence accumulation approach to predict reaching direction. Novel dynamic stopping criteria have been proposed to flexibly adjust the trade-off between early anticipation and accuracy according to the application. The output of the two predictors has been used as external inputs to a Finite State Machine (FSM) to control the behaviour of a physical robot according to user's action or inaction. Results show that our system outperforms previous methods, achieving a real-time classification accuracy of $94.3\pm2.9\%$ after $160.0msec\pm80.0msec$ from movement onset

Subject-Independent Frameworks for Robotic Devices: Applying Robot Learning to EMG Signals

The capability of having human and robots cooperating together has increased the interest in the control of robotic devices by means of physiological human signals. In order to achieve this goal it is crucial to be able to catch the human intention of movement and to translate it in a coherent robot action. Up to now, the classical approach when considering physiological signals, and in particular EMG signals, is to focus on the specific subject performing the task since the great complexity of these signals. This thesis aims to expand the state of the art by proposing a general subject-independent framework, able to extract the common constraints of human movement by looking at several demonstration by many different subjects. The variability introduced in the system by multiple demonstrations from many different subjects allows the construction of a robust model of human movement, able to face small variations and signal deterioration. Furthermore, the obtained framework could be used by any subject with no need for long training sessions. The signals undergo to an accurate preprocessing phase, in order to remove noise and artefacts. Following this procedure, we are able to extract significant information to be used in online processes. The human movement can be estimated by using well-established statistical methods in Robot Programming by Demonstration applications, in particular the input can be modelled by using a Gaussian Mixture Model (GMM). The performed movement can be continuously estimated with a Gaussian Mixture Regression (GMR) technique, or it can be identified among a set of possible movements with a Gaussian Mixture Classification (GMC) approach. We improved the results by incorporating some previous information in the model, in order to enriching the knowledge of the system. In particular we considered the hierarchical information provided by a quantitative taxonomy of hand grasps. Thus, we developed the first quantitative taxonomy of hand grasps considering both muscular and kinematic information from 40 subjects. The results proved the feasibility of a subject-independent framework, even by considering physiological signals, like EMG, from a wide number of participants. The proposed solution has been used in two different kinds of applications: (I) for the control of prosthesis devices, and (II) in an Industry 4.0 facility, in order to allow human and robot to work alongside or to cooperate. Indeed, a crucial aspect for making human and robots working together is their mutual knowledge and anticipation of other’s task, and physiological signals are capable to provide a signal even before the movement is started. In this thesis we proposed also an application of Robot Programming by Demonstration in a real industrial facility, in order to optimize the production of electric motor coils. The task was part of the European Robotic Challenge (EuRoC), and the goal was divided in phases of increasing complexity. This solution exploits Machine Learning algorithms, like GMM, and the robustness was assured by considering demonstration of the task from many subjects. We have been able to apply an advanced research topic to a real factory, achieving promising results

The Penn Hand-Clapping Motion Dataset

The Penn Hand-Clapping Motion Dataset is composed of inertial measurement unit (IMU) recordings from the hand motions of 15 naïve people. Each of these individuals participated in an experiment during which they were asked to pantomime various sequences of 10 different motions: back five, clap, double, down five, front five, lap pat, left five, right five, right snap, and up five. The examined motions comprise most typical actions from hand-clapping games like “Pat-a-cake” and “Slide.” This project was published in our IROS 2016 paper entitled “Using IMU Data to Demonstrate Hand-Clapping Games to a Robot.” After hearing of interest from other researchers, we are releasing the corresponding motion dataset, which was originally collected to help us investigate whether we could train highly accurate and rapid classifiers to label hand-clapping game motions performed by everyday people. This dataset, explained further in the included ReadMe file, may interest researchers who investigate human motion

The Penn Hand-Clapping Motion Dataset

The Penn Hand-Clapping Motion Dataset is composed of inertial measurement unit (IMU) recordings from the hand motions of 15 naïve people. Each of these individuals participated in an experiment during which they were asked to pantomime various sequences of 10 different motions: back five, clap, double, down five, front five, lap pat, left five, right five, right snap, and up five. The examined motions comprise most typical actions from hand-clapping games like “Pat-a-cake” and “Slide.” This project was published in our IROS 2016 paper entitled “Using IMU Data to Demonstrate Hand-Clapping Games to a Robot.” After hearing of interest from other researchers, we are releasing the corresponding motion dataset, which was originally collected to help us investigate whether we could train highly accurate and rapid classifiers to label hand-clapping game motions performed by everyday people. This dataset, explained further in the included ReadMe file, may interest researchers who investigate human motion