Predictability, Effort and (Anti-)Resonance in Complex Object Control

International audienc

Assessment of physical exposure to musculoskeletal risks in collaborative robotics using dynamic simulation

Many industrial tasks cannot be executed by a robot alone. A way to help workers in order to decrease the risk of musculoskeletal disorders is to assist them with a collaborative robot. Yet assessing its usefulness to the worker remains costly because it usually requires a prototype. We propose a dynamic simulation framework to model the performing of a task jointly by a virtual manikin and a robot. It allows to measure physical quantities in order to perform an ergonomic assessment of the robot. Experiments are carried out on two different robots. The results show that the proposed simulation framework is helpful for designing collaborative robots. Further work includes enhancing the simulation realism and validation on a real robot

Online Human Activity Recognition for Ergonomics Assessment

International audienc

Online Human Activity Recognition for Ergonomics Assessment

International audienceWe address the problem of recognizing the current activity performed by a human worker, providing an information useful for automatic ergonomic evaluation of workstations for industrial applications.Traditional ergonomic assessment methods rely on pen-and-paper worksheet, such as the Er-gonomic Assessment Worksheet (EAWS). Nowadays, there exists no tool to automatically estimate the ergonomics score from sensors (external cameras or wearable sensors). As the ergonomic evaluation depends of the activity that is being performed, the first step towards a fully automatic ergonomic assessment is to automatically identify the different activities within an industrial task. To address this problem, we propose a method based on wearable sensors and supervised learning based on Hidden Markov Model (HMM). The activity recognition module works in two steps. First, the parameters of the model are learned offline from observation based on both sensors, then in a second stage, the model can be used to recognize the activity offline and online. We apply our method to recognize the current activity of a worker during a series of tasks typical of the manufacturing industry. We recorded 6 participants performing a sequence of tasks using wearable sensors.Two systems were used: the MVN Link suit from Xsens and the e-glove from Emphasis Telematics (See Fig. 1). The first consists of 17 wireless inertial sensors embedded in a lycra suit, and is used to track the whole-body motion. The second is a glove that includes pressure sensors on fingertips, and finger flexion sensors. The motion capture data are combined with the one from the glove and fed to our activity recognition model. The tasks were designed to involve elements of EAWS such as load handling, screwing and manipulating objects while in different static postures. The data are labeled following the EAWS categories such as " standing bent forward " , " overhead work " or " kneeling ". In terms of performances, the model is able to recognize the activities related to EAWS with 91% of precision by using a small subset of features such as the vertical position of the center of mass, the velocity of the center of mass and the angle of the L5S1 joint

Automatic selection of ergonomic indicators for the design of collaborative robots: a virtual-human in the loop approach

Conference of 2014 14th IEEE-RAS International Conference on Humanoid Robots, Humanoids 2014 ; Conference Date: 18 November 2014 Through 20 November 2014; Conference Code:112990International audienceThe growing number of musculoskeletal disorders in industry could be addressed by the use of collaborative robots, which allow the joint manipulation of objects by both a robot and a person. Designing these robots requires to assess the ergonomic benefit they offer. However there is a lack of adapted assessment methods in the literature. Many biomechanical quantities can represent the physical solicitations to which the worker is exposed, but their relevance strongly depends on the considered task. This paper presents a method to automatically select relevant ergonomic indicators for a given task to be performed with a collaborative robot. A virtual human simulation is used to estimate thirty indicators for varying human and robot features. A variance-based analysis is then conducted to extract the most discriminating indicators. The method is validated on several different tasks. The relevance of the proposed approach is confirmed by the obtained results

Predictability, Effort and (Anti-)Resonance in Complex Object Control

International audienc

Malacological survey and geographical distribution of vector snails for schistosomiasis within informal settlements of Kisumu City, western Kenya

<p>Abstract</p> <p>Background</p> <p>Although schistosomiasis is generally considered a rural phenomenon, infections have been reported within urban settings. Based on observations of high prevalence of <it>Schistosoma mansoni </it>infection in schools within the informal settlements of Kisumu City, a follow-up malacological survey incorporating 81 sites within 6 informal settlements of the City was conducted to determine the presence of intermediate host snails and ascertain whether active transmission was occurring within these areas.</p> <p>Methods</p> <p>Surveyed sites were mapped using a geographical information system. Cercaria shedding was determined from snails and species of snails identified based on shell morphology. Vegetation cover and presence of algal mass at the sites was recorded, and the physico-chemical characteristics of the water including pH and temperature were determined using a pH meter with a glass electrode and a temperature probe.</p> <p>Results</p> <p>Out of 1,059 snails collected, 407 (38.4%) were putatively identified as <it>Biomphalaria sudanica</it>, 425 (40.1%) as <it>Biomphalaria pfeifferi </it>and 227 (21.5%) as <it>Bulinus globosus</it>. The spatial distribution of snails was clustered, with few sites accounting for most of the snails. The highest snail abundance was recorded in Nyamasaria (543 snails) followed by Nyalenda B (313 snails). As expected, the mean snail abundance was higher along the lakeshore (18 ± 12 snails) compared to inland sites (dams, rivers and springs) (11 ± 32 snails) (F_{1, 79}= 38.8, P < 0.0001). Overall, 19 (1.8%) of the snails collected shed schistosome cercariae. Interestingly, the proportion of infected <it>Biomphalaria </it>snails was higher in the inland (2.7%) compared to the lakeshore sites (0.3%) (P = 0.0109). <it>B. sudanica </it>was more abundant in sites along the lakeshore whereas <it>B. pfeifferi </it>and <it>B. globosus </it>were more abundant in the inland sites. <it>Biomphalaria </it>and <it>Bulinus </it>snails were found at 16 and 11 out of the 56 inland sites, respectively.</p> <p>Conclusions</p> <p>The high abundance of <it>Biomphalaria </it>and <it>Bulinus </it>spp. as well as observation of field-caught snails shedding cercariae confirmed that besides Lake Victoria, the local risk for schistosomiasis transmission exists within the informal settlements of Kisumu City. Prospective control interventions in these areas need to incorporate focal snail control to complement chemotherapy in reducing transmission.</p

Online Human Activity Recognition for Ergonomics Assessment

International audienc

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

Ethical and Social Considerations for the Introduction of Human-Centered Technologies at Work

International audienceHuman-centered technologies such as collaborative robots, exoskeletons, and wearable sensors are rapidly spreading in industry and manufacturing because of their intrinsic potential at assisting workers and improving their working conditions. The deployment of these technologies, albeit inevitable, poses several ethical and societal issues. Guidelines for ethically aligned design of autonomous andintelligent systems do exist, however we argue that ethical recommendations must necessarily be complemented by ananalysis of the social impact of these technologies. In this paper, we report on our preliminary studies on the opinion of factoryworkers and of people outside this environment on human-centered technologies at work. In light of these studies, we discuss ethical and social considerations for deploying these technologies in a way that improves acceptance