Taller and sleeker smartphone devices are becoming the new norm. More screen space and very responsive touchscreens have made for enjoyable experiences available to us at all times. However, after years of interacting with smaller, portable devices, we still try to use these large smartphones on the go, and do not want to change how, where, and when we interact with them. The older devices were easier to use with one hand, when mobile. Now, with bigger devices, users have trouble accessing all parts of the screen with one hand. We need to recognize the limitations in usability due to these large screens. We must start designing user interfaces that are more conducive to one hand usage, which is the preferred way of interacting with the phone. This paper introduces Adaptive App Design, a design methodology that promotes dynamic and adaptive interfaces for one handed usage. We present a novel method of recognizing which hand the user is interacting with and suggest how to design friendlier interfaces for them by presenting a set of design guidelines for this methodology.
@cite_33 @cite_16 @cite_9 @cite_5 are only few of many studies over several years stating that current interface designs on mobile devices are not suitable for one handed usage. Studies such as @cite_15 @cite_13 determine the functional area of the thumb, which quantifies the struggles that users face when operating these ill-designed interfaces.
@cite_33 @cite_9 @cite_5 @cite_15 @cite_16 @cite_13
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We present a predictive model for the functional area of the thumb on a touchscreen surface: the area of the interface reachable by the thumb of the hand that is holding the device. We derive a quadratic formula by analyzing the kinematics of the gripping hand. Model fit is high for the thumb-motion trajectories of 20 participants. The model predicts the functional area for a given 1) surface size, 2) hand size, and 3) position of the index finger on the back of the device. Designers can use this model to ensure that a user interface is suitable for interaction with the thumb. The model can also be used inversely - that is, to infer the grips assumed by a given user interface layout. Three studies of different mobile-device hand postures are presented. The first study measures the performance of postures in Fitts' law tasks using one and two hands, thumbs and index fingers, horizontal and vertical movements, and front- and back-of-device interaction. Results indicate that the index finger performs well on both the front and the back of the device, and that thumb performance on the front of the device is generally worse. Fitts' law models are created and serve as a basis for comparisons. The second study examines the orientation of shapes on the front and back of a mobile device. It shows that participants' expectations of visual feedback for finger movements on the back of a device reverse the direction of their finger movements to favor a ''transparent device'' orientation. The third study examines letter-like gestures made on the front and back of a device. It confirms the performance of the index finger on the front of the device, while showing limitations in the ability for the index finger on the back to perform complex gestures. Taken together, these results provide an empirical foundation upon which new mobile interaction designs can be based. A set of design implications and recommendations are given based directly on the findings presented. We present a mobile user interface which dynamically reformulates the layout based on the touch input pattern of users. By analyzing the touch input, it infers users' physical characteristics such as handedness, finger length, or usage habits, thereby calculates the optimal touch area for the user. The user interface is gradually adapted to each user by automatically rearranging graphic objects such as application icons to the most easy-to-touch positions. To compute the optimal touch area, we designed software architecture and implemented an Android application which analyzes touch input and determines the touch frequency in specific screen areas, the handedness and hand size of users. As proof of concept, this research prototype shows acceptable performance and accuracy. To decide which items should be placed in the optimal touch area, we plan to integrate our machine learning algorithm which prioritizes applications according to the context of users into the proposed system. We present PalmType, which uses palms as interactive keyboards for smart wearable displays, such as Google Glass. PalmType leverages users' innate ability to pinpoint specific areas of their palms and fingers without visual attention (i.e. proprioception), and provides visual feedback via the wearable displays. With wrist-worn sensors and wearable displays, PalmType enables typing without requiring users to hold any devices and does not require visual attention to their hands. We conducted design sessions with 6 participants to see how users map QWERTY layout to their hands based on their proprioception. To evaluate typing performance and preference, we conducted a 12-person user study using Google Glass and Vicon motion tracking system, which showed that PalmType with optimized QWERTY layout is 39 faster than current touchpad-based keyboards. In addition, PalmType is preferred by 92 of the participants. We demonstrate the feasibility of wearable PalmType by building a prototype that uses a wrist-worn array of 15 infrared sensors to detect users' finger position and taps, and provides visual feedback via Google Glass.