These three scenarios provided a broad spectrum of data on objective performance and subjec- tive experience in common wearable use scenarios. These records over a diverse set of scenarios verify that intra-hand input can support quick, accurate, expressive, and always-accessible inter- actions with wearables in diverse contexts, such as when mobile and in public. In the following subsections, I review these records and discuss them in terms of design factors.
6.2.1 Manipulation
Chapter3: The touch-sensitive nail prototype was able to perform a set of 29 inputs with an accuracy of up to 94.3% and a mean speed of 1.61s.
Chapter4: The touch-sensitive nail prototype with an optimized keyboard layout showed a text entry speed of up to 31.3 WPM and a 3.3 WER in a study on word repetition (assuming the expert level of speed).
Chapter5: The two prototypes (ear touch/chin and cheek touch) presented the speed and accuracy of (2.5s, 91.1%) and (2.4s, 78.5%), respectively, for the tasks (selecting one of the six targets along the ear and selecting one of the nine targets of a three-by-three grid). For the chin and cheek touch, regarding edge detection design (selecting one of three edges except for root edge), the mean accuracy increased to 96.9%.
These intra-hand inputs show quick performances. In terms of input speed, these actions can occur within three seconds (microinteraction [27]). Likewise, as for accuracy, these inputs provide results comparable to similar sensing configurations [14] (92.3
6.2.2 Accessibility
Chapter3: The study confirmed that the final set of 29 viable inputs was easily accessible without discomfort, pain, or fatigue.
Chapter4: The comparative empirical study on both sitting and mobile conditions confirmed that the nail-based touches are robust to physical disturbance while mobile.
The intra-hand inputs were highly accessible for diverse situational impairments, such as attention-demanding tasks, mobile use, and hands-occupied tasks in eyes-free and one-handed situations. In addition, intra-hand inputs are comfortable—the 144 touches were rated with a mean of 3.41/5 (mean of 4.2/5 for the final 29-input set) and are fairly comparable to the mean of 3.34/5 for 12 touches to the inner fingers in Huang et al. [49]).
6.2.3 Space
Chapter3: The ideation workshop generated various intra-hand input primitives (taps, flicks, and swipes on multiple nail regions) and derived a large set of 144 input actions from these input primitives. Finally, this led to the set of 29 viable nail inputs.
Chapter5: The elicitation study explored diverse face regions and touch input techniques, and among them, tap and drag inputs on three face regions (chin, cheek, and ears) were examined.
The most comparable study was conducted by Soliman et al. [80]. They explored 50 different gestures with eight finger action classes (including taps, tap-and-flap, slides, and drawing) with diverse variations (e.g., different tap locations). In particular, these actions included six swipe actions on the fingernails (horizontal swipes on index, middle, and ring with two directions).
Likewise, Kao et al. Kao15 presented a touch-sensitive nail prototype and a basic five-action set (four directional swipes and one tap). Compared to these works, this study explored more diverse intra-hand input actions within a fingernail by examining multiple actions (taps, swipes, and flicks), nail combinations (single and multiple nails), nail regions (tip, center, root, and both
tions of these input primitives can complement the design space of intra-hand input that have not yet been explored by prior works.
More practical comparisons should consider real-world complex applications, such as 3D modeling, sculpting, multi-objects controlling, and gaming. In most current VR devices, these are typically manipulated by hand controllers including two joysticks, six to eight buttons, and two touchpads with 3D motion tracking. To address this issue, our intra-hand input system can provide several buttons, swipes, pan, and flick actions with the vision system for 3D freehand motion. The thumb pad can be used for joystick, while other fingers can be used for buttons.
These wide input spaces will be useful for various frequently used shortcuts. For example, flicks can be used for frequently used system functions (e.g., copy, paste, and undo) and finger tapping (on single or multiple nails), while arm rotation can be used for diverse bar input controls (e.g., brush weight, volume, or size). In comparison with other AR devices, such as Hololens2 (freehand motions with a pinching gesture) and GoogleGlass (tap and swipe gestures on the glass temple), our system can provide much wider input spaces by adding more functionalities to the fingers and hand motions.
6.2.4 Social
Chapter 5: The elicitation study considering the public and social contexts provided five design strategies to achieve social acceptability and the follow-up study on two prototypes that instantiate these strategies validated the effectiveness of each strategy.
Regarding the social factor, while intra-hand input enables unobtrusive microinteraction, the main concern was on hand-to-face input because the face is one of the most prominent regions that can easily cause social problems when used as input surface. The results of the studies conducted here generally validate the effectiveness of the design strategies identified in the elicitation study and instantiated in the prototypes.