Postural optimization for an ergonomic human-robot interaction

Busch, Baptiste; Maeda, Guilherme; Mollard, Yoan; Demangeat, Marie; Lopes, Manuel

doi:10.1109/iros.2017.8206107

Cited by 68 publications

(64 citation statements)

References 28 publications

(40 reference statements)

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“…In order to assess the postural risk, motion capture systems can be used to track workers' whole-body motion in real-time and simultaneously fill in standard ergonomic assessment worksheets. Busch et al [14] used optical marker-based motion capture to fill in the REBA ergonomic assessment worksheet automatically and online. They however noted that marker-based motion capture systems are ill-adapted to industrial settings due to occlusion issues.…”

Section: A Assessment Of Ergonomics In Industrymentioning

confidence: 99%

“…Aside from the motion tracking technique mentioned in the previous section, most methods proposed in the literature to automatically fill in ergonomic assessment worksheets rely on direct measurement of joints angles and segment positions [14] [15]. Such methods enable to identify postures, but they cannot identify gestures or actions (i.e., time-series of specific postures) which are present in some ergonomic assessment worksheets (e.g., walking in EAWS worksheet).…”

Section: B Automatic Recognition Of Human Activitiesmentioning

confidence: 99%

See 1 more Smart Citation

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

Malaisé

Maurice

Colas

et al. 2019

IEEE Robot. Autom. Lett.

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In 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.

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Section: A Assessment Of Ergonomics In Industrymentioning

confidence: 99%

Section: B Automatic Recognition Of Human Activitiesmentioning

confidence: 99%

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

Malaisé

Maurice

Colas

et al. 2019

IEEE Robot. Autom. Lett.

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“…To our knowledge, a few examples are available in the field of human robot interaction (HRI). For example, Busch et al [23] used a simplified human model to calculate the user's body configuration in HRI. They derived a continuous cost function based on the Rapid Entire Body Assessment score (REBA) and used it to choose the robot position optimizing the human joint angles (and thus the ergonomic comfort).…”

Section: Introductionmentioning

confidence: 99%

Caring About the Human Operator: Haptic Shared Control for Enhanced User Comfort in Robotic Telemanipulation

Rahal

Matarese

Gabiccini

et al. 2020

IEEE Trans. Haptics

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Haptic shared control enables a human operator and an autonomous controller to share the control of a robotic system using haptic active constraints. It has been used in robotic teleoperation for different purposes, such as navigating along paths minimizing the torques requested to the manipulator or avoiding possibly dangerous areas of the workspace. However, few works have focused on using these ideas to account for the user's comfort. In this work, we present an innovative haptic-enabled shared control approach aimed at minimizing the user's workload during a teleoperated manipulation task. Using an inverse kinematic model of the human arm and the Rapid Upper Limb Assessment (RULA) metric, the proposed approach estimates the current user's comfort online. From this measure and an a priori knowledge of the task, we then generate dynamic active constraints guiding the users towards a successful completion of the task, along directions that improve their posture and increase their comfort. Studies with human subjects show the effectiveness of the proposed approach, yielding a 30% perceived reduction of the workload with respect to using standard guided humanin-the-loop teleoperation.

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“…One of the first attempts to bring HRC to the level of subject-specific, assistive collaboration was made by our recent works, which enabled robot adaptation to the variability of the task as well as human dynamic states [4], [10]. This concept was then exploited in similar settings [11]- [13], with the aim to contribute to the reduction of work-related musculoskeletal disorders (WMSD), the single largest category of work-related injuries and responsible for almost the 30% of all worker's compensation costs [14]. In recent times, researchers in this field are aiming at approaches for the realtime assessment of human ergonomics in the workplace.…”

Section: Introductionmentioning

confidence: 99%

“…An activity recognition algorithm was presented in [12] to infer the actions and postures that are considered in well-established ergonomic worksheets and thus to develop an automatic ergonomics assessment system. Finally, the human body posture was optimised in [13] using postural assessment techniques, and a personalized human kinematic model, by following the guidance of a robot and a visual feedback interface.…”

Section: Introductionmentioning

confidence: 99%

Towards Ergonomic Control of Collaborative Effort in Multi-human Mobile-robot Teams

Kim

Lorenzini

Balatti

et al. 2019

2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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In this paper, we propose a control framework for a multi-human and mobile-robot collaborative team, that takes into account the co-workers' ergonomic requirements as well as the demand for high flexibility in the manufacturing industries. The new MObile Collaborative robotic Assistant (MOCA), which is composed of a lightweight manipulator arm, an underactuated hand, and a mobile platform driven by four omni-directional wheels enabling mobility in the workspace, is able to accomplish multiple tasks in a wide area with a high level of adaptability. In addition, an ergonomics module to anticipate and mitigate the human risk factors by means of a multi-object optimisation is integrated into the framework to ensure human safety and improvement of working conditions. The main advantage of this approach is that MOCA can assist multiple human operators, reducing their physical risks, with fast-adaptive capacities due to agile mobility and advanced interaction and manipulation. We validated the proposed method with an experiment simulating a simple manufacturing line which involves two subjects and the MOCA. The results demonstrate that the proposed framework is able to address multi-workers' ergonomics with a high level of flexibility in the workplace.

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Postural optimization for an ergonomic human-robot interaction

Cited by 68 publications

References 28 publications

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

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

Caring About the Human Operator: Haptic Shared Control for Enhanced User Comfort in Robotic Telemanipulation

Towards Ergonomic Control of Collaborative Effort in Multi-human Mobile-robot Teams

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