No abstract

In industry, ergonomists apply heuristic methods to determine workers’ exposure to ergonomic risks; however, current methods are limited to evaluating postures or measuring the duration and frequency of professional tasks. The work described here aims to deepen ergonomic analysis by using joint angles computed from inertial sensors to model the dynamics of professional movements and the collaboration between joints. This work is based on the hypothesis that with these models, it is possible to forecast workers’ posture and identify the joints contributing to the motion, which can later be used for ergonomic risk prevention. The modeling was based on the Gesture Operational Model, which uses autoregressive models to learn the dynamics of the joints by assuming associations between them. Euler angles were used for training to avoid forecasting errors such as bone stretching and invalid skeleton configurations, which commonly occur with models trained with joint positions. The statistical significance of the assumptions of each model was computed to determine the joints most involved in the movements. The forecasting performance of the models was evaluated, and the selection of joints was validated, by achieving a high gesture recognition performance. Finally, a sensitivity analysis was conducted to investigate the response of the system to disturbances and their effect on the posture.

A protocol for the representation of traditional crafts and the tools to implement this are proposed. The proposed protocol is a method for the systematic collection and organization of digital assets and knowledge, their representation into a formal model, and their utilization for research, education, and preservation. A set of digital tools accompanies this protocol that enables the online curation of craft representations. The proposed approach was elaborated and evaluated with craft practitioners in three case studies. Lessons learned are shared and an outlook for future work is provided.

Manual laborers from the industry sector are often subject to critical physical strain that lead to work-related musculoskeletal disorders. Lifting, poor posture and repetitive movements are among the causes of these disorders. In order to prevent them, several rules and methods have been established to identify ergonomic risks that the worker might be exposed during his/her activities. However, the ergonomic assessment though these methods is not a trivial task and a relevant degree of theoretical knowledge on the part of the analyst is necessary. Therefore in this paper, a web-based automatic ergonomic assessment module is proposed. The proposed module uses segment rotations acquired from inertial measurement units for the assessment and provides as feedback RULA scores, color visualisation and limb angles in a simple, intuitive and meaningful way. RULA is one of the most used observational methods for assessment of occupational risk factors for upper-extremity musculoskeletal disorders. By automatizing RULA an interesting perspective for extracting posture analytics for ergonomic assessment is opened, as well as the inclusion of new features that may complement it. For future work, the use of other features and sensors will be investigated for its implementation on the module.

To prevent work-related musculoskeletal disorders (WMSD) the ergonomists apply manual heuristic methods to determine when the worker is exposed to risk factors. However, these methods require an observer and the results can be subjective. This paper proposes a method to automatically evaluate the ergonomic risk factors when performing a set of postures from the ergonomic assessment worksheet (EAWS). Joint angle motion data have been recorded with a full-body motion capture system. These data modeled the motion patterns of four different risk factors, with the use of hidden Markov models (HMMs). Based on the EAWS, automated scores were assigned by the HMMs and were compared to the scores calculated manually. Because the method proposed here is intrusive and requires expensive equipment, kinematic data from a reduced set of two sensors was also evaluated.