Wearable devices for classification of inadequate posture at work using neural networks

Inadequate postures adopted by an operator at work are among the most important risk factors in Work-related Musculoskeletal Disorders (WMSDs). Although several studies have focused on inadequate posture, there is limited information on its identification in a work context. The aim of this study is to automatically differentiate between adequate and inadequate postures using two wearable devices (helmet and instrumented insole) with an inertial measurement unit (IMU) and force sensors. From the force sensors located inside the insole, the center of pressure (COP) is computed since it is considered an important parameter in the analysis of posture. In a first step, a set of 60 features is computed with a direct approach, and later reduced to eight via a hybrid feature selection. A neural network is then employed to classify the current posture of a worker, yielding a recognition rate of 90%. In a second step, an innovative graphic approach is proposed to extract three additional features for the classification. This approach represents the main contribution of this study. Combining both approaches improves the recognition rate to 95%. Our results suggest that neural network could be applied successfully for the classification of adequate and inadequate posture

Validation of minimal number of force sensitive resistors to predict risk of falling during a timed up and go test

Purpose Several studies use force sensitive resistors (FSR) to compute gait and balance parameters related to falls without investigating the number of sensor units required to produce useful information. We propose a model with minimal sensors for an instrumented insole by investigating and optimizing the location and variety of sensors required to efficiently detect people at risk of falling. Methods Datasets previously recorded on twelve Parkinson’s disease (PD) participants (67.7 ± 10.07 years), nine healthy elderly (66.8 ± 8.0 years) and ten young healthy adults (28.27 ± 3.74 years) were used in this study. We compared the datasets obtained from the use of four FSRs with those of three, two, one and no FSR; each set was combined with an inertial measurement unit (IMU). Results During the walking activity, the risk of falling scores from four FSRs and IMU (acceleration in y-axis only) were not significantly different compared with two FSRs and IMU (p > 0.05), whereas significant difference was found for three FSRs and IMU and one FSR and IMU (p  0.05). Conclusions We concluded that it is feasible to estimate the risk index after reducing the number of sensing units from four to two FSRs during walking test and from four to three FSRs during sit-to-stand and stand-to-sit tests. The FSRs should be placed at strategic positions to avoid information loss

Wearable Devices for Classification of Inadequate Posture at Work Using Neural Networks

Inadequate postures adopted by an operator at work are among the most important risk factors in Work-related Musculoskeletal Disorders (WMSDs). Although several studies have focused on inadequate posture, there is limited information on its identification in a work context. The aim of this study is to automatically differentiate between adequate and inadequate postures using two wearable devices (helmet and instrumented insole) with an inertial measurement unit (IMU) and force sensors. From the force sensors located inside the insole, the center of pressure (COP) is computed since it is considered an important parameter in the analysis of posture. In a first step, a set of 60 features is computed with a direct approach, and later reduced to eight via a hybrid feature selection. A neural network is then employed to classify the current posture of a worker, yielding a recognition rate of 90%. In a second step, an innovative graphic approach is proposed to extract three additional features for the classification. This approach represents the main contribution of this study. Combining both approaches improves the recognition rate to 95%. Our results suggest that neural network could be applied successfully for the classification of adequate and inadequate posture

Étude d’un système interactif sécuritaire dédié à l’interaction humain-robot appliqué à des mécanismes parallèles entraînés par des câbles

Depuis l'introduction des premiers robots interactifs en industrie, qui étaient à la base des systèmes collaboratifs supposés assister les humains dans les tâches pénibles et éprouvantes physiquement, le domaine de l’interaction humain-robot a fait des progrès considérables. Actuellement, les robots et les humains peuvent coexister conjointement dans un espace hybride afin de partager des tâches de production ou de partager le temps dans l’exécution d’une activité. Cependant, les nouveaux besoins industriels doivent conduire à des recherches pour adapter les chaînes de production et les rendre plus flexible et réactive à la modification des caractéristiques des produits. L’une des solutions consiste à adapter le manipulateur industriel présent dans les lignes de production à des fins d’interaction et de collaboration. Toutefois, la présence de l’humain dans l’espace de travail d’un manipulateur (cellule de travail hybride) représente un réel défi dans le domaine de l’interaction humain-robot puisque cela consiste à l’intégration d’une multitude de variétés de capteurs dits intelligents, surtout dans le cas de l’utilisation d’un mécanisme parallèle entraîné par des câbles. Pour cette raison, plusieurs problématiques ont été soulevées, pour lesquelles peu ou pas de recherches sont réalisées : cette nouvelle technologie est introduite sans entraînement de l’opérateur, l’évaluation de la sécurité a été très peu explorée lors de l’interaction et la performance de son utilisation demeure peu évaluée dans un contexte de réduction des troubles musculosquelettiques (TMS). Le projet de recherche vise l’étude et la conception d’un système interactif permettant d’améliorer la sécurité et l’intuitivité des personnes qui interagissent avec des mécanismes parallèles entraînés par des câbles. Deux modes d’interaction sont étudiés dans le système interactif, à savoir le partage des activités et l’interaction physique. En premier lieu, une méthode de génération de trajectoires avec évitement de collisions appliquée pour le mode de partage des activités est proposée. L’effecteur du manipulateur suit un chemin dans l’espace opérationnel à travers des points de passage. Ces derniers sont générés par un réseau de neurones rétropropagation (Back-propagation), et sont reliés par un polynôme quintique (de degré cinq). En outre, la géométrie déformable de l’obstacle et l’environnement dynamique sont pris en compte dans la méthode. En second lieu, une approche est abordée pour déterminer la distance minimale entre les câbles et identifier ceux qui sont en interférence. Le calcul de distance est exécuté en temps réel à travers un algorithme. En outre, les contraintes physiques des câbles ont été prises en compte dans la modélisation mathématique et formulées en un problème d’optimisation non linéaire. Ce dernier est résolu en utilisant l’approche de Karush-Kuhn-Tucker (KKT). Cette méthode de calcul de distance est intégrée à une loi de commande interactive permettant de gérer les câbles en interférence pendant l’interaction physique avec le mécanisme. Une force est calculée et introduite dans la boucle de la commande afin d’éviter le croisement et le relâchement des câbles en interférence. Par ce fait, la tâche est exécutée jusqu’aux limites des possibilités géométriques et cinématiques du mécanisme. Par ailleurs, cette stratégie est basée sur une commande en admittance pour permettre l’interaction physiquement avec un mécanisme parallèle entrainé par des câbles. Un algorithme permettant de sélectionner entre ces deux modes est proposé. Cette approche inclut un vêtement intelligent pour le changement de mode de manière intuitive simple et rapide. L’algorithme est exécuté en temps réel et basé sur une identification de gestes utilisant un polynôme d’interpolation trigonométrique. Les signaux analysés proviennent d’une semelle instrumentée qui est située au niveau du pied. Enfin, les différents algorithmes et stratégies sont validés en simulations et à travers des expérimentations sur un mécanisme parallèle entrainé par sept câbles. Ce projet de thèse apporte plusieurs contributions dans le domaine de l’interaction humain-robot notamment la capacité d’adaptation du système interactif pour des tâches industrielles. Since the introduction of the first interactive robots in industry, which was the collaborative robots (labelled as COBOT), the field of human robot interaction has made considerable progress. In its early version, those robots were used to increase muscle strength of the operator for moving heavy loads. Recently, robots and humans can share the same workspace, production activities or working time. However, new needs in industry require more flexibility and reactivity supporting fast changes in product characteristics. One solution consists in the adaptation of an industrial robot, that is already installed in the production line, for interaction and collaboration purposes such as kinetic learning assembly task, and adaptive third hand. However, the presence of the human in the manipulators’ workspace (hybrid work cell) represents a real challenge in the field of human-robot interaction. It consists in the integration of an intelligent sensor varieties, especially when the cables driven parallel mechanisms (CDPM) are used for an interaction task. For these reasons, several issues have been raised, for which few or no research has been done yet. This new technology is introduced without any operators training and the safety assessment has been very little explored during the interaction. Moreover, the performance of its use remains poorly evaluated in a context of reduction of musculoskeletal disorders (MSDs). The research project aims to study and design an interactive system in order to improve the safety and the intuitivity when the humans interact with cables driven parallel mechanisms. Two modes of cooperation are studied in the interactive system, namely the sharing of activities and the physical interaction. First, a trajectory generating method for an industrial manipulator in a shared workspace is proposed. A neural network using a supervised learning is applied to create the waypoints required for dynamic obstacles avoidance. These points are linked with a quintic polynomial function for smooth motion which is optimized using least-square to compute an optimal trajectory. Moreover, the evaluation of human motion forms has been taken into consideration in the proposed strategy. Second, a mathematical approach is presented to determine the minimum distance between cables and to identify which ones are interfering. To execute this approach in real time, an algorithm is also presented for calculating this distance. Furthermore, the physical constraints of the cables have been considered in mathematical modeling and formulated into a nonlinear optimization problem. The latter is solved using the Karush-Kuhn-Tucker (KKT) approach. This method of distance calculation is integrated with a new interactive control that eliminates the computation of the effect of a folding interfered cable. A control strategy is proposed, which allows to manage the cables in interference while the operator physically interacts with the mechanism. A repulsive force is generated and introduced to the controller to avoid the cables crossing and folding. Therefore, the task is executed within the limits of the kinematic possibilities. Moreover, this strategy is based on an admittance control for physically interacting with a CDPM. In order to allow a change of intuitive interaction mode, an algorithm for selecting between these two modes is proposed. This approach includes an instrumented insole placed into a shoe for intuitive mode change quick and easy. The algorithm is executed in real time and based on a gesture identification using a trigonometric interpolation polynomial. Finally, theses different strategies and algorithms are validated in simulations and through experiments on a parallel mechanism driven by seven cables. This thesis project brings several contributions in the field of human-robot interaction including the ability of the interactive system to adapt for industrial tasks