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

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

Human Movement and Ergonomics: an Industry-Oriented Dataset for Collaborative Robotics

International audienceImproving work conditions in industry is a major challenge that can be addressed with new emerging technologies such as collaborative robots. Machine learning techniques can improve the performance of those robots, by endowing them with a degree of awareness of the human state and ergonomics condition. The availability of appropriate datasets to learn models and test prediction and control algorithms however remains an issue. This paper presents a dataset of human motions in industry-like activities, fully labeled according to the ergonomics assessment worksheet EAWS, widely used in industries such as car manufacturing. Thirteen participants performed several series of activities, such as screwing and manipulating loads in different conditions, resulting in more than 5 hours of data. The dataset contains the participants' whole-body kinematics recorded both with wearable inertial sensors and marker-based optical motion capture, finger pressure force, video recordings, and annotations by 3 independent annotators of the performed action and the adopted posture following the EAWS postural grid. Sensor data are available in different formats to facilitate their reuse. The dataset is intended for use by researchers developing algorithms for classifying, predicting or evaluating human motion in industrial settings, as well as researchers developing collaborative robotics solutions that aim at improving the workers' ergonomics. The annotation of the whole dataset following an ergonomics standard makes it valuable for ergonomics-related applications, but we expect its use to be broader in the robotics, machine learning and human movement communities

Ovako work posture Analysis Method(OWAS) as an assessment Technique for Ergonomic Risk In the Norwegian Petroleum Industry.

This thesis focuses on occupational safety, looking at what characterizes ergonomic risk in the Norwegian petroleum industry from Major accidents that also threaten human life. It also looks at the different ergonomic assessment methods used for both offshore and shore workers which could lead to Musculoskeletal disorders. The objective is to see how effective these assessment methods are in reducing workplace injuries for personnel. The three assessment methods discussed in this thesis are the Rapid entire body assessment method(RULA), the Rapid upper limb assessment method(REBA), and the Ovako working posture assessment method(OWAS). These three methods have a common assessment approach of using a coding format and categorization table to identify postural risk and for decision making. The RULA and REBA methods are specific for particular body parts to be evaluated, however, the OWAS method puts into consideration postures that affect the back, legs, arms, and weight. It is for this reason that this thesis will be using the OWAS method as an example to illustrate how an ergonomic risk assessment is carried out. It could be seen that the OWAS method did not prove to be a very reliable assessment technique for ergonomics, hence a discussion is later made on how this method can be improved for better postural risk identification