Posture Recognition with G-Sensors on Smart Phones

[[abstract]]With the popularity of smart phones in recent years, various sensors on smart phones can be utilized to detect the movement or intention of the smart phone users. In this research, we aim at using the signals collected from the G-sensor in the smart phone to recognize the posture of the user. Signals for sit, stand, walk and run are collected to train an offline neural network as the classifier. After the neural network learns the four postures, we then implement a neural network with the learned connection weights in a smart phone app. The app can record the postures of the user for the whole day and estimate the burned calories accordingly. This app can replace the pedometer to have a more accurate estimate of calorie consumption. Details of the app are presented in this paper. The accuracy of neural networks on posture recognition with G-sensor signals is also verified by five-fold cross-validation.[[conferencetype]]國際[[conferencedate]]20120926~20120928[[booktype]]電子版[[iscallforpapers]]Y[[conferencelocation]]Melbourne, Australi

Kurdish Optical Character Recognition

Currently, no offline tool is available for Optical Character Recognition (OCR) in Kurdish. Kurdish is spoken in different dialects and uses several scripts for writing. The Persian/Arabic script is widely used among these dialects. The Persian/Arabic script is written from Right to Left (RTL), it is cursive, and it uses unique diacritics. These features, particularly the last two, affect the segmentation stage in developing a Kurdish OCR. In this article, we introduce an enhanced character segmentation based method which addresses the mentioned characteristics. We applied the method to text-only images and tested the Kurdish OCR using documents of different fonts, font sizes, and image resolutions. The results of the experiments showed that the accuracy rate of character recognition of the proposed method was 90.82% on average

Using a serious game to assess spatial memory in children and adults

Short-term spatial memory has traditionally been assessed using visual stimuli, but not auditory stimuli. In this paper, we design and test a serious game with auditory stimuli for assessing short-term spatial memory. The interaction is achieved by gestures (by raising your arms). The auditory stimuli are emitted by smart devices placed at different locations. A total of 70 participants (32 children and 38 adults) took part in the study. The outcomes obtained with our game were compared with traditional methods. The results indicated that the outcomes in the game for the adults were significantly greater than those obtained by the children. This result is consistent with the assumption that the ability of humans increases continuously during maturation. Correlations were found between our game and traditional methods, suggesting its validity for assessing spatial memory. The results indicate that both groups easily learn how to perform the task and are good at recalling the locations of sounds emitted from different positions. With regard to satisfaction with our game, the mean scores of the children were higher for nearly all of the questions. The mean scores for all of the questions, except one, were greater than 4 on a scale from 1 to 5. These results show the satisfaction of the participants with our game. The results suggest that our game promotes engagement and allows the assessment of spatial memory in an ecological way

Detección de caracteres mediante accelerometría en dispositivos Android

El objetivo principal de este Trabajo Fin de Grado es la detección de caracteres, en concreto de las vocales del abecedario, a partir de señales de acelerometría en dispositivos Android. Para ello, se desarrollará un software en Android Studio que permitirá extraer muestras de datos generadas por un acelerómtero, para luego procesarlas y realizar una serie de operaciones matemáticas que lleven a la correcta identificación de la vocal a ser reconocida. El software será desarrollado previamente en Matlab, pues éste es uno de los programas más versátiles que existen para la realización de cálculos matemáticos.Ingeniería de Sistemas Audiovisuale

AMIL: Localizing Neighboring Mobile Devices Through a Simple Gesture

Abstract-Smartphone users are often grouped to exchange files or perform collaborative tasks when meeting together. We argue that the location information of group members is critical to many mobile applications. Existing localization solutions mostly rely on anchor nodes or infrastructures to perform ranging and positioning. These approaches are inefficient for ad hoc scenarios. In this paper, we propose AMIL, an Acoustic MobilityInduced TDoA (Time-Difference-of-Arrival)-based Localization scheme for smartphones. In AMIL, a smartphone user can use simple gestures (e.g., hold the phone and draw a triangle in the air) to quickly obtain the relative coordinates of neighboring mobile devices. We have implemented and evaluated AMIL on off-the-shelf smartphones. The field tests have shown that our scheme can achieve less than three degree orientation errors and can successfully build a simple map of 12 people in an office room with average error of 50cm

Deep Fisher Discriminant Learning for Mobile Hand Gesture Recognition

Gesture recognition becomes a popular analytics tool for extracting the characteristics of user movement and enables numerous practical applications in the biometrics field. Despite recent advances in this technique, complex user interaction and the limited amount of data pose serious challenges to existing methods. In this paper, we present a novel approach for hand gesture recognition based on user interaction on mobile devices. We have developed two deep models by integrating Bidirectional Long-Short Term Memory (BiLSTM) network and Bidirectional Gated Recurrent Unit (BiGRU) with Fisher criterion, termed as F-BiLSTM and F-BiGRU respectively. These two Fisher discriminative models can classify user’s gesture effectively by analyzing the corresponding acceleration and angular velocity data of hand motion. In addition, we build a large Mobile Gesture Database (MGD) containing 5547 sequences of 12 gestures. With extensive experiments, we demonstrate the superior performance of the proposed method compared to the state-of-the-art BiLSTM and BiGRU on MGD database and two other benchmark databases (i.e., BUAA mobile gesture and SmartWatch gesture). The source code and MGD database will be made publicly available at https://github.com/bczhangbczhang/Fisher-Discriminant-LSTM