Machine learning based activity recognition to identify wasteful activities in production

Lean Management focusses on the elimination of wasteful activities in production. Whilst numerous methods such as value stream analysis or spaghetti diagrams exist to identify transport, inventory, defects, overproduction or waiting, the waste of human motion is difficult to detect. Activity recognition attempts to categorize human activities using sensor data. Human activity recognition (HAR) is already used in the consumer domain to detect human activities such as walking, climbing stairs or running. This paper presents an approach to transfer the human activity recognition methods to production in order to detect wasteful motion in production processes and to evaluate workplaces. Using sensor data from ordinary smartphones, long-term short-term memory networks (LSTM) are used to classify human activities. Additional to the LSTM-network, the paper contributes a labeled data set for supervised learning. The paper demonstrates how activity recognition can be included in learning factory training starting from the generation of training data to the analysis of the results

Integrated lean and ergonomic assessment for the planning of human-centered factories

This research proposes an approach for evaluating human-centered work processes by combining Value Stream Mapping and ergonomic assessment of physical workload. Value Stream Mapping is a method used to create a visual representation of the flow of materials and information required to deliver a product, and aids in identifying potential bottlenecks and other constraints. However, it does not consider the effect of physical ergonomics on process execution and planning. To bridge this gap, the Ergonomic Assessment Worksheet screening tool is used to gain insight into unfavourable physical workload situations. The proposed approach follows five main steps: (i) scope definition, (ii) Value Stream Map, (iii) time study, (iv) ergonomic assessment, (v) combined analysis. A case study is conducted at a bicycle manufacturer to demonstrate the effectiveness of the approach in quantifying the impact of physical ergonomics on process performance. The method enables a systematic analysis of process chains to identify critical steps from both lean and ergonomic perspectives, emphasizing the importance of worker's physical well-being and the ability of ergonomic assessment methods to enhance Value Stream Maps.</p

Gait parameters of elderly subjects in single-task and dual-task with three different MIMU set-ups

The increasing average age of the population emphasizes the strong correlation between cognitive decline and gait disorders of elderly people. Wearable technologies such as magnetic inertial measurement units (MIMUs) have been ascertained as a suitable solution for gait analysis. However, the relationship between human motion and cognitive impairments should still be investigated, considering outcomes of different MIMU set-ups. Accordingly, the aim of the present study was to compare single-task and dual-task walking of an elderly population by using three different MIMU set-ups and correlated algorithms (trunk, shanks, and ankles). Gait sessions of sixteen healthy elderly subjects were registered and spatio-temporal parameters were selected as outcomes of interest. The analysis focused both on the comparison of walking conditions and on the evaluation of differences among MIMU set-ups. Results pointed out the significant effect of cognition on walking speed (p = 0.03) and temporal parameters (p ≤ 0.05), but not on the symmetry of gait. In addition, the comparison among MIMU configurations highlighted a significant difference in the detection of gait stance and swing phases (for shanks-ankles comparison p < 0.001 in both single and dual tasks, for trunk-ankles comparison p < 0.001 in single task and p < 0.01 in dual task). Overall, cognitive impact and MIMU set-ups revealed to be fundamental aspects in the analysis of gait spatio-temporal parameters in a healthy elderly population

real time assistance to manual assembly through depth camera and visual feedback

Abstract The current fourth industrial revolution significantly impacts on production processes. The personalized production paradigm enables customers to order unique products. The operators assemble an enormous component variety adapting their process from product to product with limited learning opportunities. Digital technologies are increasingly adopted in production processes to improve performance and quality. Considering this framework, this research proposes a hardware/software architecture to assist in real-time operators involved in manual assembly processes. A depth camera captures human motions in relation with the workstation environment whereas a visual feedback guides the operator through consecutive assembly tasks. An industrial case study validates the architecture

an automatic procedure based on virtual ergonomic analysis to promote human centric manufacturing

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Predictive Health Analysis in Industry 5.0: A Scientometric and Systematic Review of Motion Capture in Construction

In an era of rapid technological advancement, the rise of Industry 4.0 has prompted industries to pursue innovative improvements in their processes. As we advance towards Industry 5.0, which focuses more on collaboration between humans and intelligent systems, there is a growing requirement for better sensing technologies for healthcare and safety purposes. Consequently, Motion Capture (MoCap) systems have emerged as critical enablers in this technological evolution by providing unmatched precision and versatility in various workplaces, including construction. As the construction workplace requires physically demanding tasks, leading to work-related musculoskeletal disorders (WMSDs) and health issues, the study explores the increasing relevance of MoCap systems within the concept of Industry 4.0 and 5.0. Despite the growing significance, there needs to be more comprehensive research, a scientometric review that quantitatively assesses the role of MoCap systems in construction. Our study combines bibliometric, scientometric, and systematic review approaches to address this gap, analyzing articles sourced from the Scopus database. A total of 52 papers were carefully selected from a pool of 962 papers for a quantitative study using a scientometric approach and a qualitative, indepth examination. Results showed that MoCap systems are employed to improve worker health and safety and reduce occupational hazards.The in-depth study also finds the most tested construction tasks are masonry, lifting, training, and climbing, with a clear preference for markerless systems

ESTUDO BIBLIOMÉTRICO PARA IDENTIFICAÇÃO DE QUAIS TECNOLOGIAS DA INDÚSTRIA 4.0 SÃO MAIS APLICÁVEIS AO RISCO OCUPACIONAL POSTURA SENTADA

A sociedade está na Era denominada quarta revolução industrial ou Indústria 4.0 que representa uma nova forma de organizar e otimizar o trabalho. A Indústria 4.0 pode ser aplicada em todos os setores da sociedade como, saúde, produção, educação, e segurança do trabalho, por exemplo. O objetivo deste trabalho foi o de realizar um estudo bibliométrico com vistas a identificar na literatura científica quais tecnologias, pertencentes a Indústria 4.0, mais se destacam com aplicações destinadas ao risco ocupacional postura sentada. A metodologia aplicada inclui a busca por documentos na base de dados Scopus e a aplicação dos softwares VoSviewer e Bibliometrix. A Indústria 4.0 está composta por diversos tipos de tecnologias, mas os resultados apontam que a realidade virtual, a inteligência artificial e a internet das coisas são as tecnologias com mais documentos disponíveis para aplicações destinadas a postura sentada

A dynamic order acceptance and scheduling approach for additive manufacturing on-demand production

This is the final version. Available on open access from Springer Verlag via the DOI in this recordAdditive manufacturing (AM), also known as 3D printing, has been called a disruptive technology as it enables the direct production of physical objects from digital designs and allows private and industrial users to design and produce their own goods enhancing the idea of the rise of the “prosumer”. It has been predicted that, by 2030, a significant number of small and medium enterprises will share industry-specific AM production resources to achieve higher machine utilization. The decision-making on the order acceptance and scheduling (OAS) in AM production, particularly with powder bed fusion (PBF) systems, will play a crucial role in dealing with on-demand production orders. This paper introduces the dynamic OAS problem in on-demand production with PBF systems and aims to provide an approach for manufacturers to make decisions simultaneously on the acceptance and scheduling of dynamic incoming orders to maximize the average profit-per-unit-time during the whole makespan. This problem is strongly NP hard and extremely complicated where multiple interactional subproblems, including bin packing, batch processing, dynamic scheduling, and decision-making, need to be taken into account simultaneously. Therefore, a strategy-based metaheuristic decision-making approach is proposed to solve the problem and the performance of different strategy sets is investigated through a comprehensive experimental study. The experimental results indicated that it is practicable to obtain promising profitability with the proposed metaheuristic approach by applying a properly designed decision-making strategy.National High Technology Research and Development Program of Chin

Current and future Industry 4.0 capabilities for information and knowledge sharing

Humans are indispensable in the manufacturing industry as its complexity increases in an Industry 4.0 context, mainly due to changing customer demands. Managing the challenges of increased complexity can create a competitive advantage for SMEs. Technologies which enable the emerging phenomenon of Industry 4.0 have the possibility to simplify the sharing of information and knowledge among people at work, especially for Operator 4.0. However, few SMEs have actually implemented such technologies for this purpose. Therefore, this paper aims to create an understanding of the current state and challenges which need to be overcome, and further, to provide some insights on future possibilities by identifying the stages of Industry 4.0 development of SMEs with regards to their capabilities. This qualitative interview study focuses on how human-centered production processes are currently supporting assembly and office work. Two Swedish SMEs were studied, where almost all of their operators and management team on site were interviewed individually concerning their views on their current capabilities. The interviews were thematically analyzed with regards to the state-of-the-art research, and results show that the case companies are in some aspects digitized with regards to computerization and connectivity, but have some challenges in making the shared production-related information more visible and transparent, especially on shop-floors where most information and knowledge are shared through word of mouth or with pen and paper. Conclusively, the studied SMEs have started their digitalization journey, but further Industry 4.0 development needs to align with their respective conditions and needs. While Industry 4.0 enabling technologies that support information and knowledge sharing are emerging, organizational development can support the implementation of such technologies, which should be the focus for future research

Motion capture technology in industrial applications: A systematic review

The rapid technological advancements of Industry 4.0 have opened up new vectors for novel industrial processes that require advanced sensing solutions for their realization. Motion capture (MoCap) sensors, such as visual cameras and inertial measurement units (IMUs), are frequently adopted in industrial settings to support solutions in robotics, additive manufacturing, teleworking and human safety. This review synthesizes and evaluates studies investigating the use of MoCap technologies in industry-related research. A search was performed in the Embase, Scopus, Web of Science and Google Scholar. Only studies in English, from 2015 onwards, on primary and secondary industrial applications were considered. The quality of the articles was appraised with the AXIS tool. Studies were categorized based on type of used sensors, beneficiary industry sector, and type of application. Study characteristics, key methods and findings were also summarized. In total, 1682 records were identified, and 59 were included in this review. Twenty-one and 38 studies were assessed as being prone to medium and low risks of bias, respectively. Camera-based sensors and IMUs were used in 40% and 70% of the studies, respectively. Construction (30.5%), robotics (15.3%) and automotive (10.2%) were the most researched industry sectors, whilst health and safety (64.4%) and the improvement of industrial processes or products (17%) were the most targeted applications. Inertial sensors were the first choice for industrial MoCap applications. Camera-based MoCap systems performed better in robotic applications, but camera obstructions caused by workers and machinery was the most challenging issue. Advancements in machine learning algorithms have been shown to increase the capabilities of MoCap systems in applications such as activity and fatigue detection as well as tool condition monitoring and object recognition