Abstract
Gesture control of mobile devices is an emerging user interaction modality. Large-scale deployment has been delayed by two main technical challenges: detecting gestures reliably and power consumption. There have also been user-experience-related challenges, such as indicating the start of a gesture, social acceptance, and feedback on the gesture detection status. This chapter evaluates a solution for the main challenges: an event-based movement interaction modality, tapping, that emphasizes minimal user effort in interacting with a mobile device. The technical feasibility of the interaction method is examined with a smartphone equipped with a sensor interaction cover, utilizing an enabling software framework. The reliability of detecting tapping is evaluated by analyzing a dataset collected with the smartphone prototype. Overall, the results suggest that detecting tapping is reliable enough for practical applications in mobile computing when the interaction is performed in a stationary situation.
Key Terms in this Chapter
True Positive %: True positive percentage is the relative number of correctly detected patterns, given by dividing all detected true positive patterns by all actually performed true patterns in a dataset.
False Positive %: False positive percentage is the relative number of falsely detected patterns, given by dividing the occurrence of all detected false positive patterns by all segments of data where a detected pattern in a dataset should not exist.
Smartphone: A smartphone is an advanced multifunctional mobile phone with a platform open to third-party software.
Accelerometer: 3-D accelerometer is a sensor capable of measuring object acceleration along three spatial axes.
Double Tap: Double tap is a form of movement interaction where the user performs two consecutive taps on a mobile device with a finger or palm, each producing a sharp spike waveform in an accelerometer signal measured with a high sampling rate. Gesture Interaction: Gesture interaction here refers to explicit movements made with a mobile device while holding it in a hand in order to perform any tasks with the device.
Pattern Recognition: Pattern recognition is the scientific discipline whose goal is the classification of objects into a number of categories or classes. Objects can be, for example, signal waveforms or any type of measurement that needs to be classified. These objects are here referred to using the generic term “patterns.”