TapType: Ten-finger text entry on everyday surfaces via Bayesian inference

Streli, Paul; Jiang, Jiaxi; Fender, Andreas; Meier, Manuel; Romat, Hugo; Holz, Christian

doi:10.1145/3491102.3501878

Cited by 22 publications

(2 citation statements)

References 37 publications

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“…Many novel text entry methods and solutions for various application domains have recently been introduced. These include text entry in immersive virtual environments [32,33] and augmented reality systems [34], speech-based text entry [35], and typing on passive surfaces [36,37]. All these work uses a common phrase set, namely that developed by MacKenzie and Soukoreff, whilst related experiments sometimes only use part of the set.…”

Section: Discussionmentioning

confidence: 99%

Generating Representative Phrase Sets for Text Entry Experiments by GA-Based Text Corpora Sampling

Ljubić

Salkanović

2023

Mathematics

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In the field of human–computer interaction (HCI), text entry methods can be evaluated through controlled user experiments or predictive modeling techniques. While the modeling approach requires a language model, the empirical approach necessitates representative text phrases for the experimental stimuli. In this context, finding a phrase set with the best language representativeness belongs to the class of optimization problems in which a solution is sought in a large search space. We propose a genetic algorithm (GA)-based method for extracting a target phrase set from the available text corpus, optimizing its language representativeness. Kullback–Leibler divergence is utilized to evaluate candidates, considering the digram probability distributions of both the source corpus and the target sample. The proposed method is highly customizable, outperforms typical random sampling, and exhibits language independence. The representative phrase sets generated by the proposed solution facilitate a more valid comparison of the results from different text entry studies. The open source implementation enables the easy customization of the GA-based sampling method, promotes its immediate utilization, and facilitates the reproducibility of this study. In addition, we provide heuristic guidelines for preparing the text entry experiments, which consider the experiment’s intended design and the phrase set to be generated with the proposed solution.

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Section: Discussionmentioning

confidence: 99%

Generating Representative Phrase Sets for Text Entry Experiments by GA-Based Text Corpora Sampling

Ljubić

Salkanović

2023

Mathematics

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“…To detect pinch events, we threshold the running rate-of-change score 𝑐, which accumulates the absolute change in the acceleration signals a 𝑤 captured by the IMU at the wrist across time [38,54],…”

Section: Pinch Detectionmentioning

confidence: 99%

HOOV: Hand Out-Of-View Tracking for Proprioceptive Interaction using Inertial Sensing

Streli

Armani

Cheng

et al. 2023

Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems

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HOOV is a wireless sensing method that complements existing virtual and augmented reality headsets to support hand tracking outside the field of view of the headset's cameras. In this example app of our system, a participant is building a structure composed of blocks with different sizes. When the user places an object onto the structure, his hand is tracked by the headset's cameras (left). As the user reaches for a block just to his right (right), his hand leaves the field of view of the cameras. Now, HOOV continuously estimates the 6D position and orientation of the wrist using the 6-axis inertial measuring unit attached to the wrist during the time the hand stays outside the field of view.

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