TriPOD: A Prototypal System for the Recognition of Capacitive Widget on Touchscreen Addressed for Montessori-Like Educational Applications

Fuccio, Raffaele Di; Siano, Giovanni; Marco, Antonio De

doi:10.1007/978-3-319-56538-5_68

Cited by 5 publications

(8 citation statements)

References 27 publications

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“…In related literature, we classify existing RPTs for smart toys into smart devices [1,4,5], AR-based platforms [6,7,8], mobile-based platforms [8,9,10,11], and modular toolkits [12]. In Table 1, we detail RPTs from the literature, including related products and licenses, and compare them with the IoT4Fun Toolkit.…”

Section: Related Workmentioning

confidence: 99%

“…Future versions of IoT4Fun can benefit from miniaturization, robustness, and reliability improvements. As a means to demonstrate adequacy with elicited requirements, in Section 3, we first compare IoT4Fun features with other RPTs from literature and industry [1,4,5,6,7,8,9,10,11,12]. In Section 4, we introduce IoT4Fun, a toolkit that combines embedded, modular, and plug-and-play approaches.…”

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confidence: 99%

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IoT4Fun Rapid Prototyping Toolkit for Smart Toys

Albuquerque

Kelner

Nogueira

et al. 2020

Proceedings of the Annual Hawaii International Conference on System Sciences

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Rapid prototyping tools turn the design of smart toys faster and easier for creative teams. Appropriate tools for smart toys should meet a list of requirements, which include distributed data collection and adaptability for assorted toy shapes and size. The IoT4Fun toolkit innovates by mixing the embedded, modular, and plugand-play approaches. It supports motion tracking data, wireless communication, and contactless identification. IoT4Fun demonstrates its effectiveness to design a variety of smart toy solutions by fitting into a hula-hoop toy until spherical, cubic, and wearable shapes. Solutions connect with either mobile applications or other toys and play rules range from open-ended to closed behaviors. End-users exhaustively tested developed solutions, and technical assessment evaluates their integrity after playtesting sessions. Results show comparative data on battery consumption and vulnerabilities threats for data security and privacy of each design. Future versions of IoT4Fun can benefit from miniaturization, robustness, and reliability improvements. As a means to demonstrate adequacy with elicited requirements, in Section 3, we first compare IoT4Fun features with other RPTs from literature and industry [1,4,5,6,7,8,9,10,11,12]. In Section 4, we introduce IoT4Fun, a toolkit that combines embedded, modular, and plug-and-play approaches. The first approach aims to embed the same RPT into different physical toys. Modularity permits better distribution of the hardware components and allows creators to decide which modules are essential to their solutions. The plug-andplay approach offers a rapid and easy-to-use experience for the creators to manage the modules. IoT4Fun uses Printed-Circuit Board (PCB) manufacture to favor miniaturization and robustness. It collects real-time motion tracking information, supports wireless communication with devices, and contactless identification of objects or users. Besides, it offers visual, auditory, and tactile feedback and permits programming all play behaviors using Arduino IDE. In Section 5, we detail how a group of 27 graduate students embedded IoT4Fun into five smart toy prototypes. Students selected RPT modules that were suitable for their designs. Prototypes present a variety of shapes and sizes (e.g., a hula-hoop toy, a plush toy, a hand-sized cube, a large box, and a glove). Solutions either connect with mobile applications or with tagged objects, and programmed behaviors range from openended play to closed-rules. A total of 40 end-users (23 males/17 females) exhaustively tested the prototypes in playtesting sessions. In Section 6, we check for the

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Section: Related Workmentioning

confidence: 99%

mentioning

confidence: 99%

IoT4Fun Rapid Prototyping Toolkit for Smart Toys

Albuquerque

Kelner

Nogueira

et al. 2020

Proceedings of the Annual Hawaii International Conference on System Sciences

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“…TouchTokens [20] used three touches to form a triangular pattern, and selected a group of 12 patterns by heuristic experiments. TriPOD [29] generated 24 triangular patterns by project points in a 3D space according to size and distance conditions. To further expand the number of capacitive patterns, ToMMI [28] and TUIC-2D [30] use three fiducial points plus one coding point to generate patterns.…”

Section: Capacitive Patternsmentioning

confidence: 99%

“…4) apply to active tokens and passive tokens. Drawing on the pattern designs of previous studies [12], [19], [29], we use the spatial information of three markers to construct a pattern. The triangular pattern design is based on the following considerations: the shape of a triangle can express ID, position, and direction, and we can provide unique IDs by selecting different triangles.…”

Section: A Pattern Designmentioning

confidence: 99%

“…Many parameters can be used to describe a triangle, such as side lengths, area, angles, vertex coordinates, local coordinates of vertices, etc. Inspired by previous work [29], we use the counterclockwise arrangement of side lengths to construct the feature vector ( Figure 1b). The advantages of the feature vector are that each feature vector can be uniquely mapped to a triangle, and other information such as angles and area can be easily calculated by side lengths.…”

Section: A Pattern Designmentioning

confidence: 99%

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PTPG: A Poisson Triangular Pattern Generator for Tokens on Tangible Surfaces

et al. 2020

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We present PTPG (Poisson triangular pattern generator), a method that generates and optimizes triangular patterns for tokens on tangible surfaces. PTPG uses spatial information of markers to form triangular patterns and processes a template-matching algorithm to identify patterns. We introduce Poisson disk sampling to search patterns in the feature vector space and set sampling constraints to obtain patterns that meet the requirements of size, shape, and pattern distance. The patterns optimized by a shape constraint address the marker-loss problem caused by a common design of capacitive tokens, and therefore improve the recognition rate. We manufacture ten sets of prototype tokens based on the patterns generated by Poisson sampling and test these tokens on different devices. Experimental results show that our method provides more unique patterns than other heuristic methods, and the patterns are applicable to different tangible surfaces with high recognition rates. INDEX TERMS Poisson disk sampling, pattern recognition, tangible user interfaces, tangible surface.

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