Utilizing an Accelerometric Bracelet for Ubiquitous Gesture-Based Interaction

Hein, Albert; Hoffmeyer, André; Kirste, Thomas

doi:10.1007/978-3-642-02710-9_57

Cited by 6 publications

(2 citation statements)

References 4 publications

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“…These gloves also incorporate many sensors delivering information on finger movement and position, which makes them quite bulky. Alternatively, an accelerometer equipped bracelet for gesture recognition was proposed by Hein et al [5] and Amft et al [1], who used accelerometers included in a watch to control certain functions of the watch by gestures. In our work, we combine the advantages of data gloves with the convenience of small hand-held devices by designing and implementing a very slim and unobstrusive interfaces based on a data glove, as depicted in Figure 1.…”

Section: Sensors and Devicesmentioning

confidence: 99%

Airwriting recognition using wearable motion sensors

Amma

Gehrig

Schultz

2010

Proceedings of the 1st Augmented Human International Conference

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In this work we present a wearable input device which enables the user to input text into a computer. The text is written into the air via character gestures, like using an imaginary blackboard. To allow hands-free operation, we designed and implemented a data glove, equipped with three gyroscopes and three accelerometers to measure hand motion. Data is sent wirelessly to the computer via Bluetooth. We use HMMs for character recognition and concatenated character models for word recognition. As features we apply normalized raw sensor signals. Experiments on single character and word recognition are performed to evaluate the end-to-end system. On a character database with 10 writers, we achieve an average writer-dependent character recognition rate of 94.8% and a writer-independent character recognition rate of 81.9%. Based on a small vocabulary of 652 words, we achieve a single-writer word recognition rate of 97.5%, a performance we deem is advisable for many applications. The final system is integrated into an online word recognition demonstration system to showcase its applicability.

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Section: Sensors and Devicesmentioning

confidence: 99%

Airwriting recognition using wearable motion sensors

Amma

Gehrig

Schultz

2010

Proceedings of the 1st Augmented Human International Conference

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“…GestureWrist [11] and GRacelet [6] are two examples for ubiquitous wearables equipped with various sensors. These wearables have in common that they are used to track handpose for free-hand gestures.…”

Section: Related Workmentioning

confidence: 99%

TouchPosing

Daiber

Gehring

Löchtefeld

et al. 2012

Proceedings of the 11th International Conference on Mobile and Ubiquitous Multimedia

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Multi-touch interaction offers opportunities to interact with complex data. Especially the exploration of geographical data, which until today mostly relies on mice and keyboard input, could benefit from this interaction paradigm. However, the gestures that are required to interact with complex systems like Geographic Information Systems (GIS) increase in difficulty with every additional functionality. This paper describes a novel interaction approach that allows nonexpert users to easily explore geographic data using a combination of multi-touch gestures and handpostures. The use of the additional input modality -handpose -is supposed to avoid more complex multi-touch gestures. Furthermore the screen of a wearable device serves as another output modality that on one hand avoids occlusion and on the other hand serves as a magic lens.

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