text: 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_37: We propose a vision of a grasp-based interaction system where users' intentions are inferred by the way they hold and interact with a device. In this paper we specifically discuss the Bar of Soap, a multi-function handheld prototype that uses grasp-based interactions to switch between modes. This prototype relies on the hypothesis that users share a set of stereotyped grasps associated with common multi-function modes. We show that using common machine learning techniques our device can reliably distinguish five separate classes based on the users' grasps. While this interaction is currently implemented in a multi-function handheld, we anticipate the existence of many scenarios where grasp recognition could provide a more intuitive or useful interface.
  @cite_26: We demonstrate that front-of-screen targeting on mobile phones can be predicted from back-of-device grip manipulations. Using simple, low-resolution capacitive touch sensors placed around a standard phone, we outline a machine learning approach to modelling the grip modulation and inferring front-of-screen touch targets. We experimentally demonstrate that grip is a remarkably good predictor of touch, and we can predict touch position 200ms before contact with an accuracy of 18mm.
  @cite_7: This paper reports on the design and use of tactile user interfaces embedded within or wrapped around the devices that they control. We discuss three different interaction prototypes that we built These interfaces were embedded onto two handheld devices of dramatically different form factors. We describe the design and implementation challenges, and user feedback and reactions to these prototypes. Implications for future design in the area of manipulative or haptic user interfaces are highlighted.
  @cite_36: This paper presents a novel user interface for handheld mobile devices by recognizing hand grip patterns. Particularly, we consider the scenario where the device is provided with an array of capacitive touch sensors underneath the exterior cover. In order to provide the users with intuitive and natural manipulation experience, we use pattern recognition techniques for identifying the users' band grips from the touch sensors. Preliminary user studies suggest that filtering out unintended user hand grip is one of the most important issues to be resolved. We discuss the details of the prototype implementation, as well as engineering challenges for practical deployment.
  @cite_19: As mobile and tangible devices are getting smaller and smaller it is desirable to extend the interaction area to their whole surface area. The HandSense prototype employs capacitive sensors for detecting when it is touched or held against a body part. HandSense is also able to detect in which hand the device is held, and how. The general properties of our approach were confirmed by a user study. HandSense was able to correctly classify over 80 percent of all touches, discriminating six different ways of touching the device (hold left right, pick up left right, pick up at top bottom). This information can be used to implement or enhance implicit and explicit interaction with mobile phones and other tangible user interfaces. For example, graphical user interfaces can be adjusted to the user's handedness.
  @cite_2: Multitouch tablets, such as iPad and Android tablets, support virtual keyboards for text entry. Our 64-user study shows that 98 of the users preferred different keyboard layouts and positions depending on how they were holding these devices. However, current tablets either do not allow keyboard adjustment or require users to manually adjust the keyboards. We present iGrasp, which automatically adapts the layout and position of virtual keyboards based on how and where users are grasping the devices without requiring explicit user input. Our prototype uses 46 capacitive sensors positioned along the sides of an iPad to sense users' grasps, and supports two types of grasp-based automatic adaptation: layout switching and continuous positioning. Our two 18-user studies show that participants were able to begin typing 42 earlier using iGrasp's adaptive keyboard compared to the manually adjustable keyboard. Participants also rated iGrasp much easier to use than the manually adjustable keyboard (4.2 vs 2.9 on five-point Likert scale.)
  @cite_34: The Graspables project is an exploration of how measuring the way people hold and manipulate objects can be used as a user interface. As computational ability continues to be implemented in more and more objects and devices, new interaction methods need to be developed. The Graspables System is embodied by a physical set of sensors combined with pattern recognition software that can determine how users hold a device. The Graspables System has been implemented in two prototypes, the Bar of Soap and the Ball of Soap. Applications developed for these prototypes demonstrate the effectiveness of grasp-recognition as an interface in multiple scenarios.
summary: Researchers have tried to identify which hand is holding the phone with the help of additional sensors or equipment. @cite_2 @cite_7 @cite_36 @cite_37 @cite_19 used capacitive sensors along the sides of the phone to detect grip while @cite_34 used additional accelerometers. @cite_26 on the other hand used the position of the fingers on the back of the phone to determine the finger interacting with the phone. This method also used additional capacitive sensors.