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Cognitive Learning, Monitoring and Assistance of Industrial Workflows Using Egocentric Sensor Networks.

Bleser G, Damen D, Behera A, Hendeby G, Mura K, Miezal M, Gee A, Petersen N, Maçães G, Domingues H, Gorecky D, Almeida L, Mayol-Cuevas W, Calway A, Cohn AG, Hogg DC, Stricker D - PLoS ONE (2015)

Bottom Line: The above idea has been realized in a prototype, which combines components pushing the state of the art of hardware and software designed with interoperability in mind.The emphasis of this article is on the algorithms developed for the prototype: 1) fusion of inertial and visual sensor information from an on-body sensor network (BSN) to robustly track the user's pose in magnetically polluted environments; 2) learning-based computer vision algorithms to map the workspace, localize the sensor with respect to the workspace and capture objects, even as they are carried; 3) domain-independent and robust workflow recovery and monitoring algorithms based on spatiotemporal pairwise relations deduced from object and user movement with respect to the scene; and 4) context-sensitive augmented reality (AR) user feedback using a head-mounted display (HMD).A distinguishing key feature of the developed algorithms is that they all operate solely on data from the on-body sensor network and that no external instrumentation is needed.

View Article: PubMed Central - PubMed

Affiliation: Department Augmented Vision, German Research Center for Artificial Intelligence, Kaiserslautern, Germany; Department of Computer Science, Technical University of Kaiserslautern, Kaiserslautern, Germany.

ABSTRACT
Today, the workflows that are involved in industrial assembly and production activities are becoming increasingly complex. To efficiently and safely perform these workflows is demanding on the workers, in particular when it comes to infrequent or repetitive tasks. This burden on the workers can be eased by introducing smart assistance systems. This article presents a scalable concept and an integrated system demonstrator designed for this purpose. The basic idea is to learn workflows from observing multiple expert operators and then transfer the learnt workflow models to novice users. Being entirely learning-based, the proposed system can be applied to various tasks and domains. The above idea has been realized in a prototype, which combines components pushing the state of the art of hardware and software designed with interoperability in mind. The emphasis of this article is on the algorithms developed for the prototype: 1) fusion of inertial and visual sensor information from an on-body sensor network (BSN) to robustly track the user's pose in magnetically polluted environments; 2) learning-based computer vision algorithms to map the workspace, localize the sensor with respect to the workspace and capture objects, even as they are carried; 3) domain-independent and robust workflow recovery and monitoring algorithms based on spatiotemporal pairwise relations deduced from object and user movement with respect to the scene; and 4) context-sensitive augmented reality (AR) user feedback using a head-mounted display (HMD). A distinguishing key feature of the developed algorithms is that they all operate solely on data from the on-body sensor network and that no external instrumentation is needed. The feasibility of the chosen approach for the complete action-perception-feedback loop is demonstrated on three increasingly complex datasets representing manual industrial tasks. These limited size datasets indicate and highlight the potential of the chosen technology as a combined entity as well as point out limitations of the system.

No MeSH data available.


Object recognition results.As the number of objects increases from 1 to 30, the library size increases by more than 150×, while the average detection time increases by 3.3× (plier), 4.8× (claw) and 5.5× (charger). For the object ‘tape’ (right), the average detection time increases by 10×, particularly when objects with a circular shape are learnt (headphone, mug, apple, and scissors).
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pone.0127769.g022: Object recognition results.As the number of objects increases from 1 to 30, the library size increases by more than 150×, while the average detection time increases by 3.3× (plier), 4.8× (claw) and 5.5× (charger). For the object ‘tape’ (right), the average detection time increases by 10×, particularly when objects with a circular shape are learnt (headphone, mug, apple, and scissors).

Mentions: Further results are presented in [58]. In particular, [58] contains a comparison to state-of-the-art shape-based object detectors in an off-line mode on the standard ETHZ dataset [88], where the proposed approach achieves competitive performance. However, compared to state-of-the-art approaches, the proposed method is distinct in that it can learn and recognize multiple shape-based objects in real-time. Fig 22 plots the detection time as more objects are learnt. The detection time is the elapsed time until the object is correctly detected. The increase in detection time results from comparing to a larger number of descriptors in the hashtable as well as assessing ambiguous matches. From the figure, adding unambiguous objects does not affect the average detection time much. For the ambiguous object ‘tape’ (cf.Fig 22 right), the average detection time increases by 10 folds when 30 objects are being searched for. Alternative real-time shape-based object detectors [89, 90] scale linearly with the number of objects. In summary, the above mentioned results show a compatible recognition performance of the proposed approach while at the same time providing superior scalability when it comes to workflows involving a higher number of objects.


Cognitive Learning, Monitoring and Assistance of Industrial Workflows Using Egocentric Sensor Networks.

Bleser G, Damen D, Behera A, Hendeby G, Mura K, Miezal M, Gee A, Petersen N, Maçães G, Domingues H, Gorecky D, Almeida L, Mayol-Cuevas W, Calway A, Cohn AG, Hogg DC, Stricker D - PLoS ONE (2015)

Object recognition results.As the number of objects increases from 1 to 30, the library size increases by more than 150×, while the average detection time increases by 3.3× (plier), 4.8× (claw) and 5.5× (charger). For the object ‘tape’ (right), the average detection time increases by 10×, particularly when objects with a circular shape are learnt (headphone, mug, apple, and scissors).
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4488426&req=5

pone.0127769.g022: Object recognition results.As the number of objects increases from 1 to 30, the library size increases by more than 150×, while the average detection time increases by 3.3× (plier), 4.8× (claw) and 5.5× (charger). For the object ‘tape’ (right), the average detection time increases by 10×, particularly when objects with a circular shape are learnt (headphone, mug, apple, and scissors).
Mentions: Further results are presented in [58]. In particular, [58] contains a comparison to state-of-the-art shape-based object detectors in an off-line mode on the standard ETHZ dataset [88], where the proposed approach achieves competitive performance. However, compared to state-of-the-art approaches, the proposed method is distinct in that it can learn and recognize multiple shape-based objects in real-time. Fig 22 plots the detection time as more objects are learnt. The detection time is the elapsed time until the object is correctly detected. The increase in detection time results from comparing to a larger number of descriptors in the hashtable as well as assessing ambiguous matches. From the figure, adding unambiguous objects does not affect the average detection time much. For the ambiguous object ‘tape’ (cf.Fig 22 right), the average detection time increases by 10 folds when 30 objects are being searched for. Alternative real-time shape-based object detectors [89, 90] scale linearly with the number of objects. In summary, the above mentioned results show a compatible recognition performance of the proposed approach while at the same time providing superior scalability when it comes to workflows involving a higher number of objects.

Bottom Line: The above idea has been realized in a prototype, which combines components pushing the state of the art of hardware and software designed with interoperability in mind.The emphasis of this article is on the algorithms developed for the prototype: 1) fusion of inertial and visual sensor information from an on-body sensor network (BSN) to robustly track the user's pose in magnetically polluted environments; 2) learning-based computer vision algorithms to map the workspace, localize the sensor with respect to the workspace and capture objects, even as they are carried; 3) domain-independent and robust workflow recovery and monitoring algorithms based on spatiotemporal pairwise relations deduced from object and user movement with respect to the scene; and 4) context-sensitive augmented reality (AR) user feedback using a head-mounted display (HMD).A distinguishing key feature of the developed algorithms is that they all operate solely on data from the on-body sensor network and that no external instrumentation is needed.

View Article: PubMed Central - PubMed

Affiliation: Department Augmented Vision, German Research Center for Artificial Intelligence, Kaiserslautern, Germany; Department of Computer Science, Technical University of Kaiserslautern, Kaiserslautern, Germany.

ABSTRACT
Today, the workflows that are involved in industrial assembly and production activities are becoming increasingly complex. To efficiently and safely perform these workflows is demanding on the workers, in particular when it comes to infrequent or repetitive tasks. This burden on the workers can be eased by introducing smart assistance systems. This article presents a scalable concept and an integrated system demonstrator designed for this purpose. The basic idea is to learn workflows from observing multiple expert operators and then transfer the learnt workflow models to novice users. Being entirely learning-based, the proposed system can be applied to various tasks and domains. The above idea has been realized in a prototype, which combines components pushing the state of the art of hardware and software designed with interoperability in mind. The emphasis of this article is on the algorithms developed for the prototype: 1) fusion of inertial and visual sensor information from an on-body sensor network (BSN) to robustly track the user's pose in magnetically polluted environments; 2) learning-based computer vision algorithms to map the workspace, localize the sensor with respect to the workspace and capture objects, even as they are carried; 3) domain-independent and robust workflow recovery and monitoring algorithms based on spatiotemporal pairwise relations deduced from object and user movement with respect to the scene; and 4) context-sensitive augmented reality (AR) user feedback using a head-mounted display (HMD). A distinguishing key feature of the developed algorithms is that they all operate solely on data from the on-body sensor network and that no external instrumentation is needed. The feasibility of the chosen approach for the complete action-perception-feedback loop is demonstrated on three increasingly complex datasets representing manual industrial tasks. These limited size datasets indicate and highlight the potential of the chosen technology as a combined entity as well as point out limitations of the system.

No MeSH data available.