People often operate their smartphones with only one hand, using just their thumb for touch input. With today's larger smartphones, this leads to a reachability issue: Users can no longer comfortably touch everywhere on the screen without changing their grip. We investigate using the head tracking in modern smartphones to address this reachability issue. We developed three interaction techniques, pure head (PH), head + touch (HT), and head area + touch (HA), to select targets beyond the reach of one's thumb. In two user studies, we found that selecting targets using HT and HA had higher success rates than the default direct touch (DT) while standing (by about 9%) and walking (by about 12%), while being moderately slower. HT and HA were also faster than one of the best techniques, BezelCursor (BC) (by about 20% while standing and 6% while walking), while having the same success rate.

Smartphones are used predominantly one-handed, using the thumb for input. Many smartphones, however, have grown beyond 5". Users cannot tap everywhere on these screens without destabilizing their grip. ForceRay (FR) lets users aim at an out-of-reach target by applying a force touch at a comfortable thumb location, casting a virtual ray towards the target. Varying pressure moves a cursor along the ray. When reaching the target, quickly lifting the thumb selects it. In a first study, FR was 195 ms slower and had a 3% higher selection error than the best existing technique, BezelCursor (BC), but FR caused significantly less device movement than all other techniques, letting users maintain a steady grip and removing their concerns about device drops. A second study showed that an hour of training speeds up both BC and FR, and that both are equally fast for targets at the screen border.

Picking values from long ordered lists, such as when setting a date or time, is a common task on smartphones. However, the system pickers and tables used for this require significant screen space for spinning and dragging, covering other information or pushing it off-screen. The Force Picker reduces this footprint by letting users increase and decrease values over a wide range using force touch for rate-based control. However, changing input direction this way is difficult. We propose three techniques to address this. With our best candidate, Thumb-Roll, the Force Picker lets untrained users achieve similar accuracy as a standard picker, albeit less quickly. Shrinking it to a single table row, 20% of the iOS picker height, slightly affects completion time, but not accuracy. Intriguingly, after 70 minutes of training, users were significantly faster with this minimized Thumb-Roll Picker compared to the standard picker, at the same accuracy and only 6% of the gesture footprint. We close with application examples.

When people hold their smartphone in landscape orientation, they use their thumbs for input on the frontal touchscreen, while their remaining fingers rest on the back of the device (BoD) to stabilize the grip. We present BackXPress, a new interaction technique that lets users create BoD pressure input with these remaining fingers to augment their interaction with the touchscreen on the front: Users can apply various pressure levels with each of these fingers to enter different temporary "quasi-modes" that are only active as long as that pressure is applied. Both thumbs can then interact with the frontal screen in that mode. We illustrate the practicality of BackXPress with several sample applications, and report our results from three user studies: Study 1 investigated which fingers can be used to exert BoD pressure and found index, middle, and ring finger from both hands to be practical. Study 2 revealed how pressure touches from these six fingers are distributed across the BoD. Study 3 examined user performance for applying BoD pressure (a) during single touches at the front and (b) for 20 seconds while touching multiple consecutive frontal targets. Participants achieved up to 92% pressure accuracy for three separate pressure levels above normal resting pressure, with the middle fingers providing the highest accuracy. BoD pressure did not affect frontal touch accuracy. We conclude with design guidelines for BoD pressure input.

Modern smartphones, like iPhone 7, feature touchscreens with co-located force sensing. This makes touch input more expressive, e.g., by enabling single-finger continuous zooming when coupling zoom levels to force intensity. Often, however, the user wants to select and confirm a particular force value, say, to lock a certain zoom level. The most common confirmation techniques are Dwell Time (DT) and Quick Release (QR). While DT has shown to be reliable, it slows the interaction, as the user must typically wait for 1 s before her selection is confirmed. Conversely, QR is fast but reported to be less reliable, although no reference reports how to actually detect and implement it. In this paper, we set out to challenge the low reliability of QR: We collected user data to (1) report how it can be implemented and (2) show that it is as reliable as DT (97.6% vs. 97.2% success). Since QR was also the faster technique and more preferred by users, we recommend it over DT for force confirmation on modern smartphones.

Devices like smartphones, smartwatches, and fitness trackers enable runners to control music, query fitness parameters such as heart rate and speed, or be guided by coaching apps. But while these devices are portable, interacting with them during running is difficult: they usually have small buttons or touchscreens which force the user to slow down to interact with them properly. On-body tapping is an interaction technique that allows users to trigger actions by tapping at different body locations eyes-free. This paper investigates on-body tapping as a potential input technique for runners. We conducted a user study to evaluate where and how accurately runners can tap on their body. We motion-captured participants while tapping locations on their body and running on a treadmill at different speeds. Results show that a uniform layout of five targets per arm and two targets on the abdomen achieved 96% accuracy rate. We present a set of design implications to inform the design of on-body interfaces for runners.

Using a smartphone touchscreen to control apps mirrored to a distant display is hard, since the user cannot see where she is touching while looking at the distant screen. Tactile landmarks at the back of the phone can mitigate this problem, especially in landscape mode [3]: By moving a finger across these landmarks, the user can haptically estimate the finger position in proportion to the touchscreen. Upon pinching the thumb resting above the touchscreen towards that finger at the back, the finger position is transferred to the front and registered as a touch. However, despite proprioception, this technique leads to a shift between back and front position, denoted as pinch error. We investigated this error using different target locations, device thicknesses, and tilt angles to derive target sizes that can be acquired at a 96% success rate.

Using a smartphone for touch input to control apps and games mirrored to a distant screen is difficult, as the user cannot see where she is touching while looking at the distant display. We present HaptiCase, an interaction technique that provides back-of-device tactile landmarks that the user senses with her fingers to estimate the location of her finger in relation to the touchscreen. By pinching the thumb resting above the touch- screen to a finger at the back, the finger position is transferred to the front as the thumb touches the screen. In a study, we compared touch performance of different landmark layouts with a regular landmark-free mobile device. Using a land- mark design of dots on a 3x5 grid significantly improves eyes-free tapping accuracy and allows targets to be as small as 17.5 mm---a 14% reduction in target size---to cover 99% of all touches. When users can look at the touchscreen, land- marks have no significant effect on performance. HaptiCase is low-cost, requires no electronics, and works with unmodified software.