A Generic Software to Support Collective Decision in Food Chains and in Multi-Stakeholder Situations

International audienceMyChoice is a user-friendly web-based application supporting collective decision, developed by INRAE (French National Institute of Research for Agriculture, Food and the Environment). It is designed to analyse, compare and assess the acceptability of different alternatives-e.g. technologies, food processes, variants of a product, etc.-, based on explicative arguments stemming from various sources and stakeholders, regarding different criteria and aims. It is well-suited for accompanying news trends and developments in food chains, requiring the adhesion and cooperation of various stakeholders. Nevertheless, its design is generic and may also be applied to different fields. This paper presents the design concepts of the software, stemming from different disciplines-multicriteria decision, AI argumentation, database information systems, social psychology-, its features and expected future developments

Faire du corps une image (pour une iconographie épistémique de l'art posthumain)

L'art posthumain est l'histoire d'une rencontre. Il tente de faire dialoguer, sous le régime de l'art, une certaine conception de l'homme et du corps avec les nouvelles technologies. Cette thèse de doctorat interdisciplinaire interroge les enjeux sémiotiques, plastiques et sociologiques de cette rencontre. Analysant la pratique artistique de Stelarc, d'Orlan, d'Aziz et Cucher, d'Arthur Elsenaar, de Stahl Stenslie et de Natasha Vita-More, je mets en évidence que l'art posthumain ne se résume pas à un florilège de tentatives appliquant le paradigme cybernétique sur le corps humain. Il n'est pas que simple expérimentation désintéressée. Plutôt, chaque recours à la cybernétique dans l'art posthumain porte la marque d'un manque et d'un désir. La posthumanité n'est pas simple science-fiction ; si elle est fiction, c'est qu'elle est de même une réponse, se voulant scientifique, à un désir de transcendance.STRASBOURG-B.N.U.S. (674821001) / SudocSudocFranceF

A Generic Software to Support Collective Decision in Food Chains and in Multi-Stakeholder Situations

International audienceMyChoice is a user-friendly web-based application supporting collective decision, developed by INRAE (French National Institute of Research for Agriculture, Food and the Environment). It is designed to analyse, compare and assess the acceptability of different alternatives-e.g. technologies, food processes, variants of a product, etc.-, based on explicative arguments stemming from various sources and stakeholders, regarding different criteria and aims. It is well-suited for accompanying news trends and developments in food chains, requiring the adhesion and cooperation of various stakeholders. Nevertheless, its design is generic and may also be applied to different fields. This paper presents the design concepts of the software, stemming from different disciplines-multicriteria decision, AI argumentation, database information systems, social psychology-, its features and expected future developments

MyChoice

MyChoice is a web application supporting collective decision, designed by INRAE. The software enables project participants to:- Analyze, compare and assess stakeholders' attitudes towards different alternatives.- Review explanatory arguments stemming from various sources and reflecting different concerns.- Explain the criteria, aims and features pursued.- Highlight potential synergies or competing concerns.- Propose different modes of decision support (expert, consensual, prospective, etc.)

MyChoice

MyChoice is a web application supporting collective decision, designed by INRAE. The software enables project participants to:- Analyze, compare and assess stakeholders' attitudes towards different alternatives.- Review explanatory arguments stemming from various sources and reflecting different concerns.- Explain the criteria, aims and features pursued.- Highlight potential synergies or competing concerns.- Propose different modes of decision support (expert, consensual, prospective, etc.)

Model-based upper-limb gravity compensation strategies for active dynamic arm supports

International audienceNeuromuscular disorders (NMDs) may induce difficulties to perform daily life activities in autonomy. For people with NMDs affecting the upper-limb mobility, dynamic arm supports (DASs) turn out to be relevant assistive devices. In particular, active DASs benefit from an external power source to support severely impaired people. However, commercially available active devices are controlled with push buttons, which adds cognitive load and discomfort. To alleviate this issue, we propose a new force-based assistive control framework. In this preliminary work, we focus on the computation of a feedforward force to compensate upper-limb gravity. Four strategies based on a biomechanical model of the upper limb, tuned using anthropometric measurements, are proposed and evaluated. The first one is based on the potential energy of the upper-limb, the second one makes a compromise between the shoulder and elbow torques, the third one minimizes the sum of the squared user joint torques and the last one uses a probabilistic approach to minimize the expected torque norm in the presence of model uncertainties. These strategies have been evaluated quantitatively through an experiment including nine participants with an active DAS prototype. The activity of six muscles was measured and used to compute the mean effort index (MEI) which represents the global effort required to maintain the pose. A statistical analysis shows that the four strategies significantly lower the MEI (p-value < 0.001)

Model-based upper-limb gravity compensation strategies for active dynamic arm supports

International audienceNeuromuscular disorders (NMDs) may induce difficulties to perform daily life activities in autonomy. For people with NMDs affecting the upper-limb mobility, dynamic arm supports (DASs) turn out to be relevant assistive devices. In particular, active DASs benefit from an external power source to support severely impaired people. However, commercially available active devices are controlled with push buttons, which adds cognitive load and discomfort. To alleviate this issue, we propose a new force-based assistive control framework. In this preliminary work, we focus on the computation of a feedforward force to compensate upper-limb gravity. Four strategies based on a biomechanical model of the upper limb, tuned using anthropometric measurements, are proposed and evaluated. The first one is based on the potential energy of the upper-limb, the second one makes a compromise between the shoulder and elbow torques, the third one minimizes the sum of the squared user joint torques and the last one uses a probabilistic approach to minimize the expected torque norm in the presence of model uncertainties. These strategies have been evaluated quantitatively through an experiment including nine participants with an active DAS prototype. The activity of six muscles was measured and used to compute the mean effort index (MEI) which represents the global effort required to maintain the pose. A statistical analysis shows that the four strategies significantly lower the MEI (p-value < 0.001)

Model-based upper-limb gravity compensation strategies for active dynamic arm supports

International audienceNeuromuscular disorders (NMDs) may induce difficulties to perform daily life activities in autonomy. For people with NMDs affecting the upper-limb mobility, dynamic arm supports (DASs) turn out to be relevant assistive devices. In particular, active DASs benefit from an external power source to support severely impaired people. However, commercially available active devices are controlled with push buttons, which adds cognitive load and discomfort. To alleviate this issue, we propose a new force-based assistive control framework. In this preliminary work, we focus on the computation of a feedforward force to compensate upper-limb gravity. Four strategies based on a biomechanical model of the upper limb, tuned using anthropometric measurements, are proposed and evaluated. The first one is based on the potential energy of the upper-limb, the second one makes a compromise between the shoulder and elbow torques, the third one minimizes the sum of the squared user joint torques and the last one uses a probabilistic approach to minimize the expected torque norm in the presence of model uncertainties. These strategies have been evaluated quantitatively through an experiment including nine participants with an active DAS prototype. The activity of six muscles was measured and used to compute the mean effort index (MEI) which represents the global effort required to maintain the pose. A statistical analysis shows that the four strategies significantly lower the MEI (p-value < 0.001)

Model-based upper-limb gravity compensation strategies for active dynamic arm supports

International audienceNeuromuscular disorders (NMDs) may induce difficulties to perform daily life activities in autonomy. For people with NMDs affecting the upper-limb mobility, dynamic arm supports (DASs) turn out to be relevant assistive devices. In particular, active DASs benefit from an external power source to support severely impaired people. However, commercially available active devices are controlled with push buttons, which adds cognitive load and discomfort. To alleviate this issue, we propose a new force-based assistive control framework. In this preliminary work, we focus on the computation of a feedforward force to compensate upper-limb gravity. Four strategies based on a biomechanical model of the upper limb, tuned using anthropometric measurements, are proposed and evaluated. The first one is based on the potential energy of the upper-limb, the second one makes a compromise between the shoulder and elbow torques, the third one minimizes the sum of the squared user joint torques and the last one uses a probabilistic approach to minimize the expected torque norm in the presence of model uncertainties. These strategies have been evaluated quantitatively through an experiment including nine participants with an active DAS prototype. The activity of six muscles was measured and used to compute the mean effort index (MEI) which represents the global effort required to maintain the pose. A statistical analysis shows that the four strategies significantly lower the MEI (p-value < 0.001)