Real-time evaluation and feedback system for ergonomics on the shop floor.

Despite the greatly increased automation in manufacturing industries, manual operations still exist, and ergonomic risk factors that arise because of manual operations can lead to Work-Related Musculoskeletal Disorders (WMSDs). To mitigate the risk, manual operations should be assessed to identify if any risk, such as awkward posture, exist. Most assessments are carried out offline but this cannot alert and prevent operators from adopting awkward postures in time. Hence, due to the popularity of flexible manufacturing systems that require immediate response to changes, there is need for a real-time assessment. Therefore, the aim of this research is to develop a real-time knowledge-based ergonomic assessment system for use in the real-time evaluation of work postures on the shop floor and provision of feedback to workers, using 3D motion sensors. The developed intelligent system utilizes the knowledge from health and safety (H&S) guidelines, set of rules and an inference engine, to automatically capture and assess worker’s postures and provide real-time feedback to the worker through an easy-to-understand user interface. The system has been validated using many case studies which include the posture assessment of: 6 operators assembling engine valve, 4 seated researchers conducting desk-based reading and 15 operators during lifting, assembly and hammering of IKEA table. The system when tested proved to achieve: real-time assessment, easy-to-understand feedback, reliable measurements with Cronbach’s alpha of 0.978, p=0.045 and Kendall’s coefficient of concordance of 0.634, p = 0.000. The main contribution of this work lies in providing real-time feedback to workers. This contribution is in three sub-areas namely: i) Development of a real-time Kinect-based tool for H&S-compliant ergonomic assessment. ii) Development of a knowledge-based real-time feedback system for improved posture assessment. iii) Provision of real-time feedback to alert workers in time. The novelty of this research is in the development of a knowledge-based system for real-time ergonomic assessment and feedback to workers using 3D motion sensors.PhD in Manufacturin

Design and implementation of ergonomic risk assessment feedback system for improved work posture assessment

Ergonomic risk factors which include force, repetition and awkward postures, can result in Work-Related Musculoskeletal Disorders (WMSDs) among workers. Hence, systems that provide real-time feedback to the worker concerning his current ergonomic behaviours are desirable. This paper presents the design and implementation of a human-machine interface posture assessment feedback system whose conceptual model is developed through a model-driven development perspective using the Unified Modeling Language (UML) and interface flow diagrams. The resulting system provides a shop floor with a simple, cost-effective and automatic tool for real-time display of worker's postures. Testing the system on volunteer participants reveals that it is easy to use, achieves real-time posture assessment and provides easy-to-understand feedback to workers. This system may be useful for reducing the rate of occurrence of awkward postures, one of the contributing factors to risk of WMSDs among workers

Gesture Detection Towards Real-Time Ergonomic Analysis for Intelligent Automation Assistance

Manual handling involves transporting of load by hand through lifting or lowering and operators on the manufacturing shop floor are daily faced with constant lifting and lowering operations which leads to Work-Related Musculoskeletal Disorders. The trend in data collection on the Shop floor for ergonomic evaluation during manual handling activities has revealed a gap in gesture detection as gesture triggered data collection could facilitate more accurate ergonomic data capture and analysis. This paper presents an application developed to detect gestures towards triggering real-time human motion data capture on the shop floor for ergonomic evaluations and risk assessment using the Microsoft Kinect. The machine learning technology known as the discrete indicator—precisely the AdaBoost Trigger indicator was employed to train the gestures. Our results show that the Kinect can be trained to detect gestures towards real-time ergonomic analysis and possibly offering intelligent automation assistance during human posture detrimental tasks

Ergonomic Design of an Adaptive Automation Assembly System

Ergonomics is a key factor in the improvement of health and productivity in workplaces. Its use in improving the performance of a manufacturing process and its positive effects on productivity and human performance is drawing the attention of researchers and practitioners in the field of industrial engineering. This paper proposes an ergonomic design approach applied to an innovative prototype of an adaptive automation assembly system (A3S) equipped with Microsoft Kinect™ for real-time adjustment. The system acquires the anthropometric measurements of the operator by means of the 3-D sensing device and changes its layout, arranging the mobile elements accordingly. The aim of this study was to adapt the assembly workstation to the operator dimensions, improving the ergonomics of the workstation and reducing the risks of negative effects on workers’ health and safety. The case study of an assembly operation of a centrifugal electric pump is described to validate the proposed approach. The assembly operation was simulated at a traditional fixed workstation and at the A3S. The shoulder flexion angle during the assembly tasks at the A3S reduced between 18% and 47%. The ergonomic risk assessment confirmed the improvement of the ergonomic conditions and the ergonomic benefits of the A3S

Development of Kinectᵀᴿ applications for assembly simulation and ergonomic analysis

Marker-less motion capture technology has been harnessed for several years to track human movements for developing various applications. Recently, with the launch of Microsoft Kinect, researchers have been keenly interested in developing applications using this device. Since Kinect is very inexpensive (only $110 at the time of writing this thesis), it is a low-cost and a promising substitute for the comparatively expensive marker-based motion capture systems. Though it is principally designed for home entertainment, numerous applications can be developed with the capabilities of Kinect. The skeleton data of a human being tracked by a single Kinect device is enough to simulate the human movements, in some cases. However, it is highly desirable to develop a multiple Kinect system to enhance the tracking volume and to address an issue of occlusions. This thesis presents a novel approach for addressing the issue of interference of infrared light patterns while using multiple Kinect devices for human motion capture without lowering the frame rate. This research also presents a software solution to obtain skeleton data from multiple Kinect devices using Kinect for Windows SDK. It also discusses the development of an application involving auto scaling of a human model in digital human modeling software by Siemens Jack and human motion simulation using skeleton tracking data from Kinect to assist the industries with a flexible tool for ergonomic analysis. Further, the capability of this application for obtaining assembly simulations of fastening operations on an aircraft fuselage is also presented. --Abstract, page iii

Using RGB-D sensors and evolutionary algorithms for the optimization of workstation layouts

[EN] RGB-D sensors can collect postural data in an automatized way. However, the application of these devices in real work environments requires overcoming problems such as lack of accuracy or body parts' occlusion. This work presents the use of RGB-D sensors and genetic algorithms for the optimization of workstation layouts. RGB-D sensors are used to capture workers' movements when they reach objects on workbenches. Collected data are then used to optimize workstation layout by means of genetic algorithms considering multiple ergonomic criteria. Results show that typical drawbacks of using RGB-D sensors for body tracking are not a problem for this application, and that the combination with intelligent algorithms can automatize the layout design process. The procedure described can be used to automatically suggest new layouts when workers or processes of production change, to adapt layouts to specific workers based on their ways to do the tasks, or to obtain layouts simultaneously optimized for several production processes.This work was supported by the Programa estatal de investigacion, desarrollo e innovacion orientada a los retos de la sociedad of the Government of Spain under Grant TIN2013-42504-R.Diego-Mas, JA.; Poveda Bautista, R.; Garzon-Leal, D. (2017). Using RGB-D sensors and evolutionary algorithms for the optimization of workstation layouts. Applied Ergonomics. 65:530-540. doi:10.1016/j.apergo.2017.01.012S5305406

Digital Twin for Monitoring Ergonomics during Manufacturing Production

Within the era of smart factories, concerning the ergonomics related to production processes, the Digital Twin (DT) is the key to set up novel models for monitoring the performance of manual work activities, which are able to provide results in near real time and to support the decision-making process for improving the working conditions. This paper aims to propose a methodological framework that, by implementing a human DT, and supports the monitoring and the decision making regarding the ergonomics performances of manual production lines. A case study, carried out in a laboratory, is presented for demonstrating the applicability and the effectiveness of the proposed framework. The results show how it is possible to identify the operational issues of a manual workstation and how it is possible to propose and test improving solutions

Creating a Worker-Individual Physical Ability Profile Using a Low-Cost Depth Camera

Assembly workers suffer from long-term damage performing physically intensive tasks due to workstations that are not ergonomically designed for the individual’s needs. Current approaches towards ergonomic improvements of workstations only assess the workstations themselves without taking the individual worker and abilities into account. Therefore, physical limitations, such as age-related loss of range of motion, are not addressed. Work-induced long-term damages result in employee absences, especially of workers close to their pension. Regarding the demographic change, this issue will be even more prevalent in the future. The current approaches, like the functional capacity evaluation, allow movement analysis of individuals, but are too time-consuming to be performed on all workers of a production site. This paper presents a method to assess the individual ability of a worker using a low-cost depth camera with full body tracking to determine the angles between body segments. A set of ergonomic exercises is used to demonstrate relevant abilities for assembly and commissioning tasks. By capturing the motion sequence of these exercises, a physical ability profile can be created with little effort

Automatic assessment of the ergonomic risk for manual manufacturing and assembly activities through optical motion capture technology

Abstract Safeguard the operator health is nowadays a hot topic for most of the companies whose production process relies on manual manufacturing and assembly activities. European legislations, national regulations and international standards force the companies to assess the risk of musculoskeletal disorders of operators while they are performing manual tasks. Furthermore, international corporates typically require their partners to adopt and implement particular indices and procedures to assess the ergonomic risks specific of their industrial sector. The expertise and time required by the ergonomic assessment activity compels the companies to huge financial, human and technological investments. An original Motion Analysis System (MAS) is developed to facilitate the evaluation of most of the ergonomic indices traditionally adopted by manufacturing firms. The MAS exploits a network of marker-less depth cameras to track and record the operator movements and postures during the performed tasks. The big volume of data provided by this motion capture technology is employed by the MAS to automatically and quantitatively assesses the risk of musculoskeletal disorders over the entire task duration and for each body part. The developed hardware/software architecture is tested and validated with a real industrial case study of a car manufacturer which adopts the European Assembly Worksheet (EAWS) to assess the ergonomic risk of its assembly line operators. The results suggest how the MAS is a powerful architecture compared to other motion capture solutions. Indeed, this technology accurately assesses the operator movements and his joint absolute position in the assembly station 3D layout. Finally, the MAS automatically and quantitatively fill out the different EAWS sections, traditionally evaluated through time- and resource-consuming activities