Capturing Basic Movements for Mobile Gaming Platforms Embedded with Motion Sensors

2012 ◽  
Vol 3 (4) ◽  
pp. 1-14
Author(s):  
Aravind Kailas ◽  
Chia-Chin Chong
Keyword(s):  
Recognition Algorithm ◽  
Smart Phones ◽  
Future Research ◽  
Motion Sensors ◽  
Pattern Recognition Algorithm ◽  
Basic Step ◽  
Mobile Gaming ◽  
Module Design ◽  
Dual Module ◽  
Dance Dance Revolution

A novel experimental setup using accelerometer and gyroscope sensors embedded on a single board along with a distance-based pattern recognition algorithm is presented for accurately identifying basic movements for possible application in gaming using a mobile platform. As an example, the authors considered some basic step sequences in the popular dance game (e.g., dance dance revolution), and could detect these movements with a reasonably high probability. They envision that the experimental results presented in this paper will motivate future research in the world of mobile gaming applications using advanced smart phones with a dual module design.

WLAN and Bluetooth Positioning in Smart Phones

2012 ◽  
pp. 44-68 ◽  
Author(s):  
Ruizhi Chen ◽  
Ling Pei ◽  
Jingbin Liu ◽  
Helena Leppäkoski
Keyword(s):  
Wireless Local Area Network ◽  
Local Area Network ◽  
Local Area ◽  
Recognition Algorithm ◽  
Smart Phones ◽  
Future Research ◽  
Area Network ◽  
Future Research Directions ◽  
Signals Of Opportunity ◽  
Short Section

Although the short range radio frequency technologies such as WLAN (Wireless Local Area Network) and Bluetooth were originally designed for the purpose of wireless communication, they have been widely adopted as common signals of opportunity for positioning in smart phones for both indoors and outdoors. The cell identifier and radio signal strength are the most common observables used for positioning. The applicable position methods include Cell-ID, fingerprinting, and trilateration. Fingerprinting is the most common approach, which can provide a positioning accuracy of even 2-5 meters indoors using either the pattern recognition algorithm or the probabilistic algorithm; however, the obtainable accuracy depends on the positioning environment. The objective of this chapter is to present the WLAN and Bluetooth positioning methodologies and explain the related positioning algorithms. The chapter covers an introduction of the topic, descriptions of the observables, the positioning algorithms, and the implementation issues of the positioning solutions. The chapter is concluded with a short section of future research directions followed by a brief conclusion.

scholarly journals Project SEARCH (Scanning EARs for Child Health): validating an ear biometric tool for patient identification in Zambia

2020 ◽  
Vol 4 ◽  
pp. 168
Author(s):  
Lauren Etter ◽  
Alinani Simukanga ◽  
Wenda Qin ◽  
Rachel Pieciak ◽  
Lawrence Mwananyanda ◽  
...  
Keyword(s):  
Recognition Algorithm ◽  
Patient Identification ◽  
Pattern Recognition Algorithm ◽  
Unique Identifier ◽  
Cross Sectional ◽  
Middle Income ◽  
Middle Income Countries ◽  
Image Capturing ◽  
Public Health Challenges ◽  
Validation Experiments

Patient identification in low- to middle-income countries is one of the most pressing public health challenges of our day. Given the ubiquity of mobile phones, their use for health-care coupled with a biometric identification method, present a unique opportunity to address this challenge. Our research proposes an Android-based solution of an ear biometric tool for reliable identification. Unlike many popular biometric approaches (e.g., fingerprints, irises, facial recognition), ears are noninvasive and easily accessible on individuals across a lifespan. Our ear biometric tool uses a combination of hardware and software to identify a person using an image of their ear. The hardware supports an image capturing process that reduces undesired variability. The software uses a pattern recognition algorithm to transform an image of the ear into a unique identifier. We created three cross-sectional datasets of ear images, each increasing in complexity, with the final dataset representing our target use-case population of Zambian infants (N=224, aged 6days-6months). Using these datasets, we conducted a series of validation experiments, which informed iterative improvements to the system. Results of the improved system, which yielded high recognition rates across the three datasets, demonstrate the feasibility of an Android ear biometric tool as a solution to the persisting patient identification challenge.

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