Postural Optimization for an Ergonomic Human-Robot Interaction

International audienceIn human-robot collaboration the robot's behavior impacts the worker's safety, comfort and acceptance of the robotic system. In this paper we address the problem of how to improve the worker's posture during human-robot collaboration. Using postural assessment techniques, and a personalized human kinematic model, we optimize the model body posture to fulfill a task while avoiding uncomfortable or unsafe postures. We then derive a robotic behavior that leads the worker towards that improved posture. We validate our approach in an experiment involving a joint task with 39 human subjects and a Baxter torso-humanoid robot

Real time enhancement of operator's ergonomics in physical human - robot collaboration scenarios using a multi-stereo camera system

In collaborative tasks where humans work alongside machines, the robot's movements and behaviour can have a significant impact on the operator's safety, health, and comfort. To address this issue, we present a multi-stereo camera system that continuously monitors the operator's posture while they work with the robot. This system uses a novel distributed fusion approach to assess the operator's posture in real-time and to help avoid uncomfortable or unsafe positions. The system adjusts the robot's movements and informs the operator of any incorrect or potentially harmful postures, reducing the risk of accidents, strain, and musculoskeletal disorders. The analysis is personalized, taking into account the unique anthropometric characteristics of each operator, to ensure optimal ergonomics. The results of our experiments show that the proposed approach leads to improved human body postures and offers a promising solution for enhancing the ergonomics of operators in collaborative tasks

Adaptive Positionierung großer Arbeitsobjekte in der industriellen Montage zur Reduktion von physischen Belastungen

Das Institut für Arbeitswissenschaft entwickelt am Beispiel der Automobiltürmontage eine adaptive Arbeitsstation zur automatisierten Positionierung großer Arbeitsobjekte, um deren Auswirkung auf Belastung, Beanspruchung und Prozesseffizienz zu untersuchen. Für die Gestaltung des Adaptionsprozesses sind die Einflüsse von Häufigkeit, Genauigkeit und des Zeitpunktes der einzelnen Adaptionsschritte mit einzubeziehen. Zur reaktiven Regelung in Echtzeit bzw. präventiven Steuerung des Systems durch die Adaptionslogik muss eine geeignete Datengrundlage geschaffen werden. Die positive Wirkung einer Repositionierung auf die Expositionszeit in kritischen Flexions- bzw. Extensionswinkeln in Schulter und Nacken wurde in einer Proof of Concept Studie nachgewiesen

DULA and DEBA: Differentiable Ergonomic Risk Models for Postural Assessment and Optimization in Ergonomically Intelligent pHRI

Ergonomics and human comfort are essential concerns in physical human-robot interaction applications. Defining an accurate and easy-to-use ergonomic assessment model stands as an important step in providing feedback for postural correction to improve operator health and comfort. Common practical methods in the area suffer from inaccurate ergonomics models in performing postural optimization. In order to retain assessment quality, while improving computational considerations, we propose a novel framework for postural assessment and optimization for ergonomically intelligent physical human-robot interaction. We introduce DULA and DEBA, differentiable and continuous ergonomics models learned to replicate the popular and scientifically validated RULA and REBA assessments with more than 99% accuracy. We show that DULA and DEBA provide assessment comparable to RULA and REBA while providing computational benefits when being used in postural optimization. We evaluate our framework through human and simulation experiments. We highlight DULA and DEBA's strength in a demonstration of postural optimization for a simulated pHRI task.Comment: Submitted to IROS 2022. arXiv admin note: substantial text overlap with arXiv:2108.0597

Pengembangan Metode RULA Berbasis Image Processing dan Deep Learning untuk Penilaian Risiko Ergonomi Postur Kerja

Penelitian ini mengusulkan sistem perhitungan indeks risiko secara otomatis dengan memanfaatkan metode evaluasi Rapid Upper Limb Assessment (RULA). Sistem yang diusulkan mencakup perancangan hardware dan software yang menggabungkan sistem deteksi postur kerja tanpa memerlukan penambahan alat pada tubuh, melainkan menggunakan pemrosesan gambar dan memanfaatkan model deep learning MediaPipe. Software yang diusulkan secara spesifik mengestimasi postur kerja dari gambar yang diperoleh melalui webcam real-time atau gambar yang sudah ada. Selanjutnya, software menghitung sudut tubuh dan menghasilkan skor serta indeks risiko RULA. Pendekatan ini telah berhasil dievaluasi dalam skala laboratorium, dan penelitian ini memberikan gambaran komprehensif tentang sistem yang diusulkan, termasuk hasil validasi. Implementasi dan perbandingan hasil juga dilakukan dengan merujuk pada jurnal terkait. Sistem yang diusulkan memperoleh skor RULA 4, sedangkan jurnal pembanding mmeperoleh skor RULA 6. Sistem yang diusulkan menunjukkan bahwa sistem yang diusulkan secara efektif dapat memprediksi sudut kriteria RULA dengan signifikan, dan melakukan analisis RULA dengan akurasi yang sebanding atau bahkan lebih baik dibandingkan dengan analisis manual. Keefektifan ini tetap berlaku bahkan dalam kondisi yang tidak terduga yang biasanya dihadapi di lingkungan kerja. Kata kunci: penilaian resiko ergonomi, pemrosesan gambar, pembelajaran mendalam, rula, manufaktu

A brief review of robotics technologies to support social interventions for older users

In the last few decades, various studies demonstrated numerous robotics applications that can tackle the problem of the ageing population by supporting older people to live longer and independently at home. This article reviews the scientific literature and highlights how social robots can help the daily life of older people and be useful also as assessment tools for mild physical and mental conditions. It will underline the aspects of usability and acceptability of robotic solutions for older persons. Indeed, the design should maximise these to improve the users' attitude towards the actual use of the robots. The article discusses the advantages and concerns about the use of robotics technology in the social context with a vulnerable population. In this field, success is to assist social workers, not to replace them. We conclude recommending that care benefits should be balanced against ethical costs

Learning Intention Aware Online Adaptation of Movement Primitives

In order to operate close to non-experts, future robots require both an intuitive form of instruction accessible to laymen and the ability to react appropriately to a human co-worker. Instruction by imitation learning with probabilistic movement primitives (ProMPs) allows capturing tasks by learning robot trajectories from demonstrations, including the motion variability. However, appropriate responses to human co-workers during the execution of the learned movements are crucial for fluent task execution, perceived safety, and subjective comfort. To facilitate such appropriate responsive behaviors in human-robot interaction, the robot needs to be able to react to its human workspace co-inhabitant online during the execution of the ProMPs. Thus, we learn a goal-based intention prediction model from human motions. Using this probabilistic model, we introduce intention-aware online adaptation to ProMPs. We compare two different novel approaches: First, online spatial deformation, which avoids collisions by changing the shape of the ProMP trajectories dynamically during execution while staying close to the demonstrated motions and second, online temporal scaling, which adapts the velocity profile of a ProMP to avoid time-dependent collisions. We evaluate both approaches in experiments with non-expert users. The subjects reported a higher level of perceived safety and felt less disturbed during intention aware adaptation, in particular during spatial deformation, compared to non-adaptive behavior of the robot

Activity Recognition for Ergonomics Assessment of Industrial Tasks with Automatic Feature Selection

International audienceIn industry, ergonomic assessment is currently performed manually based on the identification of postures and actions by experts. We aim at proposing a system for automatic ergonomic assessment based on activity recognition. In this paper, we define a taxonomy of activities, composed of four levels, compatible with items evaluated in standard ergonomic worksheets. The proposed taxonomy is applied to learn activity recognition models based on Hidden Markov Models. We also identify dedicated sets of features to be used as input of the recognition models so as to maximize the recognition performance for each level of our taxonomy. We compare three feature selection methods to obtain these subsets. Data from 13 participants performing a series of tasks mimicking industrial tasks are collected to train and test the recognition module. Results show that the selected subsets allow us to successfully infer ergonomically relevant postures and actions