Adaptive Motion-Based Gesture Recognition Interface for Mobile Phones

In this paper, a biometric technique based on gesture recognition is proposed to improve the security of operations requiring authentication in mobile phones. Users are authenticated by making a gesture invented by them holding a mobile phone embedding an accelerometer on their hand. An analysis method based on sequence alignment is proposed and evaluated in different experiments. Firstly, a test of distinctiveness of gestures has been proposed obtaining an equal error rate (EER) of 4.98% with a database of 30 users and four repetitions. With the same database, a second experiment representing the unicity of accessing attempts has resulted in an EER value of 1.92%. Finally, a third experiment to evaluate the robustness of the technique has examined a database of 40 users with eight repetitions and real falsification attempts, performed by three impostors from the study of recordings of the carrying out of the original gestures, resulting in an EER of 2.5%.

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An Easily Customized Gesture Recognizer for Assisted Living Using Commodity Mobile Devices

Journal of Healthcare Engineering ◽

10.1155/2018/3180652 ◽

2018 ◽

Vol 2018 ◽

pp. 1-12 ◽

Cited By ~ 3

Author(s):

Antigoni Mezari ◽

Ilias Maglogiannis

Keyword(s):

Wireless Communication ◽

Gesture Recognition ◽

Mobile Phones ◽

Assisted Living ◽

Video Processing ◽

Main Approach ◽

Algorithmic Approach ◽

Symbolic Aggregate Approximation ◽

Important Field

Automatic gesture recognition is an important field in the area of human-computer interaction. Until recently, the main approach to gesture recognition was based mainly on real time video processing. The objective of this work is to propose the utilization of commodity smartwatches for such purpose. Smartwatches embed accelerometer sensors, and they are endowed with wireless communication capabilities (primarily Bluetooth), so as to connect with mobile phones on which gesture recognition algorithms may be executed. The algorithmic approach proposed in this paper accepts as the input readings from the smartwatch accelerometer sensors and processes them on the mobile phone. As a case study, the gesture recognition application was developed for Android devices and the Pebble smartwatch. This application allows the user to define the set of gestures and to train the system to recognize them. Three alternative methodologies were implemented and evaluated using a set of six 3-D natural gestures. All the reported results are quite satisfactory, while the method based on SAX (Symbolic Aggregate approXimation) was proven the most efficient.

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Watching Your Phone's Back

Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies ◽

10.1145/3463522 ◽

2021 ◽

Vol 5 (2) ◽

pp. 1-26

Author(s):

Lei Wang ◽

Xiang Zhang ◽

Yuanshuang Jiang ◽

Yong Zhang ◽

Chenren Xu ◽

...

Keyword(s):

Gesture Recognition ◽

Mobile Phones ◽

Sound Propagation ◽

User Interaction ◽

Recognition System ◽

Coarse Grained ◽

Back Surface ◽

Adversarial Learning ◽

Ultrasonic Signals ◽

Fine Grained

Gesture recognition on the back surface of mobile phone, not limited to the touch screen, is an enabling Human-Computer Interaction (HCI) mechanism which enriches the user interaction experiences. However, there are two main limitations in the existing Back-of-Device (BoD) gesture recognition systems. They can only handle coarse-grained gesture recognition such as tap detection and cannot avoid the air-borne propagation suffering from the interference in the air. In this paper, we propose StruGesture, a fine-grained gesture recognition system using the back of mobile phones with ultrasonic signals. The key technique is to use the structure-borne sounds (i.e., sound propagation via structure of the device) to recognize sliding gestures on the back of mobile phones. StruGesture can fully extract the structure-borne component from the hybrid Channel Impulse Response (CIR) based on Peak Selection Algorithm. We develop a deep adversarial learning architecture to learn the gesture-specific representation for robust and effective recognition. Extensive experiments are designed to evaluate the robustness over nine deployment scenarios. The results show that StruGesture outperforms the competitive state-of-the-art classifiers by achieving an average recognition accuracy of 99.5% over 10 gestures.

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Do mobile ‘phones have a detrimental impact on auditory function?

Yearbook of Otolaryngology-Head and Neck Surgery ◽

10.1016/s1041-892x(07)70025-2 ◽

2007 ◽

Vol 2007 ◽

pp. 26-27

Author(s):

M.M. Paparella

Keyword(s):

Mobile Phones ◽

Auditory Function ◽

Detrimental Impact

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MOBILE PHONES AND YOUR HEALTH

Pathology ◽

10.1080/00313020120070821 ◽

2001 ◽

Vol 33 (3) ◽

pp. 269-270

Author(s):

Clive G. Harper ◽

Victor K. Lee

Keyword(s):

Mobile Phones

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Correlating Personality and Actual Phone Usage

Journal of Individual Differences ◽

10.1027/1614-0001/a000139 ◽

2014 ◽

Vol 35 (3) ◽

pp. 158-165 ◽

Cited By ~ 38

Author(s):

Christian Montag ◽

Konrad Błaszkiewicz ◽

Bernd Lachmann ◽

Ionut Andone ◽

Rayna Sariyska ◽

...

Keyword(s):

Big Data ◽

Social Network ◽

Mobile Phone ◽

Mobile Phones ◽

Self Report ◽

Psychological Variables ◽

New Approach ◽

Report Data ◽

Self Report Data ◽

Voice Calls

In the present study we link self-report-data on personality to behavior recorded on the mobile phone. This new approach from Psychoinformatics collects data from humans in everyday life. It demonstrates the fruitful collaboration between psychology and computer science, combining Big Data with psychological variables. Given the large number of variables, which can be tracked on a smartphone, the present study focuses on the traditional features of mobile phones – namely incoming and outgoing calls and SMS. We observed N = 49 participants with respect to the telephone/SMS usage via our custom developed mobile phone app for 5 weeks. Extraversion was positively associated with nearly all related telephone call variables. In particular, Extraverts directly reach out to their social network via voice calls.

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