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315 results on '"Anderson, Iain A."'

8. Glove-Based Hand Gesture Recognition for Diver Communication

Author

Antillon, Derek W. Orbaugh, Walker, Christopher R., Rosset, Samuel, and Anderson, Iain A.

Abstract

We have developed a smart dive glove that recognizes 13 static hand gestures used in diving communication. The smart glove employs five dielectric elastomer sensors to capture finger motion and implements a machine learning classifier in the onboard electronics to recognize gestures. Five basic classification algorithms are trained and assessed: the decision tree, support vector machine (SVM), logistic regression, Gaussian naïve Bayes, and multilayer perceptron. These basic classifiers were selected as they perform well in multiclass classification problems, can be trained using supervised learning, and are model-based algorithms that can be implemented on a microprocessor. The training dataset was collected from 24 participants providing for a range of different hand sizes. After training, the algorithms were evaluated in a dry environment using data collected from ten new participants to test how well they cope with new information. Furthermore, an underwater experiment was conducted to assess any impact of the underwater environment on each algorithm’s classification. The results show all classifiers performed well in a dry environment. The accuracies and F1-scores range between 0.95 and 0.98, where the logistic regressor and SVM have the highest scores for both the accuracy and F1-score (0.98). The underwater results showed that all algorithms work underwater; however, the performance drops when divers must focus on buoyancy control, breathing, and diver trim.

Published
2023
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9. Characterizations and Inkjet Printing of Carbon Black Electrodes for Dielectric Elastomer Actuators

Author

Yi, Jianan, Babick, Frank, Strobel, Carsten, Rosset, Samuel, Ciarella, Luca, Borin, Dmitry, Wilson, Katherine, Anderson, Iain, Richter, Andreas, and Henke, E.-F. Markus

Abstract

Dielectric elastomer actuators (DEAs) have been proposed as a promising technology for developing soft robotics and stretchable electronics due to their large actuation. Among available fabrication techniques, inkjet printing is a digital, mask-free, material-saving, and fast technology, making it versatile and appealing for fabricating DEA electrodes. However, there is still a lack of suitable materials for inkjet-printed electrodes. In this study, multiple carbon black (CB) inks were developed and tested as DEA electrodes inkjet-printed on acrylic membranes (VHB). Triethylene glycol monomethyl ether (TGME) and chlorobenzene (CLB) were selected to disperse CB. The inks’ stability, particle size, surface tension, viscosity, electrical resistance, and printability were characterized. The DEA with Ink-TGME/CLB (mixture solvent) electrodes obtained 80.63% area strain, a new benchmark for the DEA actuation with CB powder electrodes on VHB. The novelty of this work involves the disclosure of a new ink recipe (TGME/CLB/CB) for inkjet printing that can obtain stable drop formations with a small nozzle (17 × 17 μm), high resolution (∼25 μm, approaching the limit of drop-on-demand inkjet printing), and the largest area strain of DEAs under similar conditions, distinguishing this contribution from the previous works, which is important for the fabrication and miniaturization of DEA-based soft and stretchable electronics.

Published
2023
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34. Diver-Robot Communication Glove Using Sensor-Based Gesture Recognition

Author

Walker, Christopher R., Nad, Dula, Antillon, Derek W. Orbaugh, Kvasic, Igor, Rosset, Samuel, Miskovic, Nikola, and Anderson, Iain A.

Abstract

Despite the popularity of the buddy system, where a pair of divers look out for each other, scuba diving still poses significant risks with 15–30 deaths per 100 000 divers per year. In the 2015 edition of “Diving Medicine for Scuba Divers,” it is reported that 86% of diving fatalities involve a lone diver, yet divers still elect to dive alone for a variety of reasons including buddy unavailability. A solution to this may lie in a dedicated autonomous dive-buddy robot, which could also operate as an assistant. The challenge is how would the diver interact and communicate with the robot in underwater conditions often involving low visibility. In this article, we detail and demonstrate a technological solution that involves the direct motion capture of fingers and hand using a smart dive glove with integrated stretch sensors alongside an inertial measurement unit. The hardware and gesture recognition protocol were initially evaluated in a lab-based study where 10 users participated in a virtual reality environment setup and interacted with a virtual robot using gestures. In total, 140 out of 175 gestures were correctly recognized, resulting in diver-robot commands. A full sea demonstration of the underwater motion-capture system followed with the underwater dive glove used to send gesture-based acoustic commands to a dedicated dive buddy, a modified autonomous underwater vehicle. The diver was able to command the robot to move in six directions with a total delay of 6 s or less between the beginning of the gesture and the initiation of the action by the robot.

Published
2023
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