1,720,975 research outputs found
A Dataset of Human Motion and Muscular Activities in Manual Material Handling tasks for Biomechanical and Ergonomic Analyses
No full textManual Material Handling (MMH) activities represent a large portion of the workers’ tasks in the tertiary sector. The ability to monitor, model, and predict human behaviours are crucial to both the design of productive human-robot collaboration and an efficient physical exposure assessment system that can prevent Work-related Musculoskeletal Disorders (WMSDs), with the ultimate goal of improving workers’ quality of life. The combined use of wearable sensors and machine learning (ML) techniques can fulfil these purposes. Inertial Measurement Units (IMUs) and surface Electromyography (sEMG) allow collecting kinematic data and muscular activity information that can be used for biomechanical analyses, ergonomic risk assessment, and as input of ML algorithms aimed at joint torque/load estimation, and Human Activity Recognition (HAR). The latter needs a large amount of annotated training samples, and the use of publicly available datasets is the way forward. Nowadays, the majority of them concern Activities of Daily Life (ADLs) and, including only kinematic data, have limited applications. This paper presents a fully labelled dataset of working activities that include full-body kinematics from 17 IMUs and upper limbs sEMG data from 16 channels. Fourteen subjects participated in the experiment performed in laboratory settings for overall 18.6 hours of recordings. The activities are divided into two sets. The first includes lifting, lowering, and carrying objects, MMH activities suitable for ergonomic risk assessment, and HAR. The second includes isokinetic arm movements, mainly targeting load and joint torque estimation
Kinematic optimization for the design of a collaborative robot end-effector for tele-echography
Full text linkTele-examination based on robotic technologies is a promising solution to solve the current worsening shortage of physicians. Echocardiography is among the examinations that would benefit more from robotic solutions. However, most of the state-of-the-art solutions are based on the development of specific robotic arms, instead of exploiting COTS (commercial-off-the-shelf) arms to reduce costs and make such systems affordable. In this paper, we address this problem by studying the design of an end-effector for tele-echography to be mounted on two popular and low-cost collaborative robots, i.e., the Universal Robot UR5, and the Franka Emika Panda. In the case of the UR5 robot, we investigate the possibility of adding a seventh rotational degree of freedom. The design is obtained by kinematic optimization, in which a manipulability measure is an objective function. The optimization domain includes the position of the patient with regards to the robot base and the pose of the end-effector frame. Constraints include the full coverage of the examination area, the possibility to orient the probe correctly, have the base of the robot far enough from the patient’s head, and a suitable distance from singularities. The results show that adding a degree of freedom improves manipulability by 65% and that adding a custom-designed actuated joint is better than adopting a native seven-degrees-freedom robot
Kinematic Analysis of a Novel Pin-Wheel Joint
No full textIn this paper an innovative joint for the transmission of motion between parallel and incident axes is presented. It is made up of two frontal pin-wheels with cylindrical pins. The kinematics in case of parallel axes is discussed in detail. It is shown that, quite surprisingly, it may behave in many different ways, depending on the value of the center distance between the two axes. A systematic way to analyze the joint kinematics is provided
Wearable sensor network for biomechanical overload assessment in manual material handling
No full textThe assessment of risks due to biomechanical overload in manual material handling is nowadays mainly based on observational methods in which an expert rater visually inspects videos of the working activity. Currently available sensing wearable technologies for motion and muscular activity capture enables to advance the risk assessment by providing reliable, repeatable, and objective measures. However, existing solutions do not address either a full body assessment or the inclusion of measures for the evaluation of the effort. This article proposes a novel system for the assessment of biomechanical overload, capable of covering all areas of ISO 11228, that uses a sensor network composed of inertial measurement units (IMU) and electromyography (EMG) sensors. The proposed method is capable of gathering and processing data from three IMU-based motion capture systems and two EMG capture devices. Data are processed to provide both segmentation of the activity and ergonomic risk score according to the methods reported in the ISO 11228 and the TR 12295. The system has been tested on a challenging outdoor scenario such as lift-on/lift-off of containers on a cargo ship. A comparison of the traditional evaluation method and the proposed one shows the consistency of the proposed system, its time effectiveness, and its potential for deeper analyses that include intra-subject and inter-subjects variability as well as a quantitative biomechanical analysis
Recent In-Flight Results with the MicroHAPS Near-Space Platform for Space Technology Testing
No full textDuring the last six years the Space Systems Laboratory of the University of Pisa has been developing a small, low cost, quick-access platform (microHAPS) for scienti c experiments and technology demonstration in the stratosphere. The team at UniPi designed, manufactured, tested, and flew successfully more than 25 stratospheric missions of increasing complexity, using COTS sounding balloons as the lifting device. This was made possible by the increased availability of high performance, low power microcontrollers and sensors, so that the limited mass lifting capability of traditional sounding balloons is now much less relevant in determing the operational capabilities of such vehicles.
The mciroHAPS platform features an altitude regulation system that allows the platform to dwell above 25 km for several days, thanks to a control system based on deep reinforcement learning. Power is provided by exible solar cells, providing for the needs of the payload and for supplying a reaction wheel attitude stabilization and pointing system. Full-duplex telemetry and telecommand is implemented using a LoRa spread-spectrum transceiver.
With a cost orders of magnitude smaller than space platforms, mciroHAPS is a sort of Cubesat of the stratosphere, well suited to act as a test bench for microsatellite technologies. With 2 to 3 kg of payload, a large variety of experiments can be conducted in near-space conditions: at 30 km air pressure is around 20 mbar, temperature is -60 C, insolation is almost exactly Air-Mass-Zero, most of ionizing radiation is unshielded, and the field of view towards the surface of the Earth is hundreds of km in all directions.
While not full representative of the LEO environment, the higher stratosphere is nevertheless an excellent environment for testing of space technologies, allowing for significant TRL increase with extremely low cost, quick and repeatable access.
Among others, technology missions own so far by our team include the analysis of the EM background noise at 868 MHz and 2.4 GHz (for IoT applications of the ISM bands); detection of AIS signals form ships in the Mediterranean; analysis of nocturnal light pollution in urban areas; and I/V curve characterization of innovative solar cells. We report on the lessons learned and outline the next design features and the expected performance of the microHAPS system as a test bench for microsatellite technology
Identification of gait phases with neural networks for smooth transparent control of a lower limb exoskeleton
Full text linkLower limbs exoskeletons provide assistance during standing, squatting, and walking. Gait dynamics, in particular, implies a change in the configuration of the device in terms of contact points, actuation, and system dynamics in general. In order to provide a comfortable experience and maximize performance, the exoskeleton should be controlled smoothly and in a transparent way, which means respectively, minimizing the interaction forces with the user and jerky behavior due to transitions between different configurations. A previous study showed that a smooth control of the exoskeleton can be achieved using a gait phase segmentation based on joint kinematics. Such a segmentation system can be implemented as linear regression and should be personalized for the user after a calibration procedure. In this work, a nonlinear segmentation function based on neural networks is implemented and compared with linear regression. An on-line implementation is then proposed and tested with a subject
Kinematic Optimization for the Design of a UR5 Robot End-Effector for Cardiac Tele-Ultrasonography
No full textRobotic tele-examination is mainstream for solving the nowadays worsening shortage of physicians. However, many solutions are based on custom robotic arms, whereas using COTS arms could reduce costs and make such systems affordable. In this paper, we address the problem of the design of an end-effector for cardiac tele-ultrasonography, assuming the use of a popular and low-cost industrial robot such as the Universal Robot UR5. We use a kinematic optimization based on the manipulability measure taking into account the position of the robot base with respect to the patient, the number of degrees of freedom (DoFs), and the size of the end-effector. The constraints of the problem are the full inclusion of the examination area in the workspace and the possibility to orient the probe correctly. The results of this study show that, although the arm has 6 DoFs, an additive DoF of the end-effector improves the manipulability measure by more than 100%
A wearable device to assist the evaluation of workers health based on inertial and sEMG signals
No full textThis paper describes a wearable monitoring sys-tem for sEMG and coherent motion data signals that is primarily aimed at real-time tracking of workers' activity for the analysis and prevention of Work-related Musculoskeletal Disorders (WMSDs). The device supports the data recording, real-time streaming, and analysis of up to four 9-axis inertial units and up to 8 independent single-ended surface Electromyography (sEMG) signals. The device has been developed to acquire, elaborate and integrate information related to the arm motion and forces with a minimal encumbrance on the operator's arm. The paper discusses the design of the device and presents a validation of the measuring capabilities of the system
Transparency evaluation for the Kinematic Design of the Harnesses through Human-Exoskeleton Interaction Modeling
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