Improving Performance of Object Detection using the Mechanisms of Visual Recognition in Humans

Object recognition systems are usually trained and evaluated on high resolution images. However, in real world applications, it is common that the images have low resolutions or have small sizes. In this study, we first track the performance of the state-of-the-art deep object recognition network, Faster- RCNN, as a function of image resolution. The results reveals negative effects of low resolution images on recognition performance. They also show that different spatial frequencies convey different information about the objects in recognition process. It means multi-resolution recognition system can provides better insight into optimal selection of features that results in better recognition of objects. This is similar to the mechanisms of the human visual systems that are able to implement multi-scale representation of a visual scene simultaneously. Then, we propose a multi-resolution object recognition framework rather than a single-resolution network. The proposed framework is evaluated on the PASCAL VOC2007 database. The experimental results show the performance of our adapted multi-resolution Faster-RCNN framework outperforms the single-resolution Faster-RCNN on input images with various resolutions with an increase in the mean Average Precision (mAP) of 9.14% across all resolutions and 1.2% on the full-spectrum images. Furthermore, the proposed model yields robustness of the performance over a wide range of spatial frequencies

Human Gait Database for Normal Walk Collected by Smart Phone Accelerometer

The goal of this study is to introduce a comprehensive gait database of 93 human subjects who walked between two endpoints during two different sessions and record their gait data using two smartphones, one was attached to the right thigh and another one on the left side of the waist. This data is collected with the intention to be utilized by a deep learning-based method which requires enough time points. The metadata including age, gender, smoking, daily exercise time, height, and weight of an individual is recorded. this data set is publicly available

Evaluation of ultra-wideband in vivo radio channel and its effects on system performance

This paper presents bit‐error‐rate (BER) performance analysis and improvement using equalizers for an in vivo radio channel at ultra‐wideband frequencies (3.1 GHz to 10.6 GHz). By conducting simulations using a bandwidth of 50 MHz, we observed that the in vivo radio channel is affected by small‐scale fading. This fading results in intersymbol interference affecting upcoming symbol transmission, causing delayed versions of the symbols to arrive at the receiver side and causes increase in BER. A 29‐taps channel was observed from the experimentally measured data using a human cadaver, and BER was calculated for the measured in vivo channel response along with the ideal additive white Gaussian noise and Rayleigh channel models. Linear and nonlinear adaptive equalizers, ie, decision feedback equalizer (DFE) and least mean square (LMS), were used to improve the BER performance of the in vivo radio channel. It is noticed that both the equalizers improve the BER but DFE has better BER compared to LMS and shows the 2‐dB and 4‐dB performance gains of DFE over the LMS at Eb/No = 12 dB and at Eb/No = 14 dB, respectively. The current findings will help guide future researchers and designers in enhancing systems performance of an ultra‐wideband in vivo wireless systems

The Role of Eye Gaze in Security and Privacy Applications: Survey and Future HCI Research Directions

For the past 20 years, researchers have investigated the use of eye tracking in security applications. We present a holistic view on gaze-based security applications. In particular, we canvassed the literature and classify the utility of gaze in security applications into a) authentication, b) privacy protection, and c) gaze monitoring during security critical tasks. This allows us to chart several research directions, most importantly 1) conducting field studies of implicit and explicit gaze-based authentication due to recent advances in eye tracking, 2) research on gaze-based privacy protection and gaze monitoring in security critical tasks which are under-investigated yet very promising areas, and 3) understanding the privacy implications of pervasive eye tracking. We discuss the most promising opportunities and most pressing challenges of eye tracking for security that will shape research in gaze-based security applications for the next decade

From Motor Control to Scene Perception: Using Machine Learning to Model Human Behavior and Cognition

Machine learning is an important multidisciplinary field of research, which aims to construct models that learn from data and make predictions based on it. Such methods have been widely used in understanding and analyzing human behavioral and physical attributes. In the first part of this thesis, two dimensions of implementing machine learning algorithms for solving two important real world problems are discussed. The first problem focuses on modeling human physical characteristics (e.g., walking) from accelerometer data measured by smartphones. We build highly accurate models that can recognize human daily activities and can identify users based on their gait characteristics. The second problem is modeling of human eye-movement behavior, specifically in order to identify different individuals during reading activity. The highly specific characteristics of human cognition and behavior during the reading process reflected in human eye-movement features make them very suitable for user identification. Our approach dramatically outperforms previous methods, making it possible to build eye-movement biometric systems for user identification and personalized interfaces. The second part of this thesis studies deep learning solutions for three visual scene perception and object recognition problems. The goal is to investigate to which extent deep convolutional neural networks resemble the human visual system for scene perception and object recognition in three problems: (1) classification of scenes based on their global properties, (2) deploying a multi-resolution technique for object recognition, and (3) evaluating the influence of the high-level context of scene grammar on object and scene recognition. The first problem proposes to derive global properties of a scene as high-level scene descriptions from deep features of convolutional neural networks in scene classification tasks. The second problem shows that fine-tuning the Faster-RCNN (the state-of-the-art object recognition network) to multi-resolution data inspired by the human multi-resolution visual system improves the network performance and robustness over a range of spatial frequencies. Finally, the third problem studies the effects of violating the high level scene syntactic and semantic rules on human eye-movement behavior and deep neural scene and object recognition networks

Assessment of Dental Care and its Related Barriers in Pregnant Women of Hamadan City

Background and Objectives: Oral health behaviors of pregnant women are important due to their effects on mother and child’s health. The objective of this study was to investigate dental care and its related barriers among pregnant women in Hamadan city, Iran. Materials and Methods: This cross-sectional study was carried out on 280 pregnant women in Hamadan city in 2012. We used stratified cluster sampling to select the subjects and a researcher-made questionnaire was used. The questionnaire included demographic information, common dental problems, visit of a dentist during pregnancy and tooth brushing, as well as the perceived barriers of these two behaviors. The reliability and validity of the tool were evaluated using estimates of internal consistency and the opinions of a panel of experts, respectively. Data were analyzed in SPSS-16 using logistic regression test. Results: The mean age of pregnant women was 27.2 ± 4.88 years. Tooth brushing after each meal and use of mouthwash rates were 12% and 20%, respectively. Half of the participants had not seen a dentist during their current pregnancy. The most important barriers to brush twice a day were impatience and lack of energy. Dental visit barriers were cost, low priority, and stress related to dentistry. Factors associated with no dental visit included poor economic status (P= 0.011) and working outside of home (P= 0.045). Conclusions: Given the important adverse outcomes associated with poor oral health on mothers and babies, interventions to reduce the barriers of dental cares are crucial

Workout Type Recognition and Repetition Counting with CNNs from 3D Acceleration Sensed on the Chest

Sports and workout activities have become important parts of modern life. Nowadays, many people track characteristics about their sport activities with their mobile devices, which feature inertial measurement unit (IMU) sensors. In this paper we present a methodology to detect and recognize workout, as well as to count repetitions done in a recognized type of workout, from a single 3D accelerometer worn at the chest. We consider four different types of workout (pushups, situps, squats and jumping jacks). Our technical approach to workout type recognition and repetition counting is based on machine learning with a convolutional neural network. Our evaluation utilizes data of 10 subjects, which wear a Movesense sensors on their chest during their workout. We thereby find that workouts are recognized correctly on average 89.9% of the time, and the workout repetition counting yields an average detection accuracy of 97.9% over all types of workout.Peer reviewe

Speech Intelligibility in Persian Children with Down Syndrome

Objectives: One of the most effective methods to describe speech disorders is the measurement of speech intelligibility. The speech intelligibility indicates the extent of acoustic signals that correctly speaker produces and hearer receives. The purpose of this study was to investigate the speech intelligibility in the Persian children with Down syndrome, age range was 3 to 5 years, who had spoken Persian. Methods: this cross- sectional study investigates 12 children (6 girls and 6 boys) with Down syndrome who had referred to speech therapy clinic in Hamadan city and 12 normal children (6 girls and 6 boys) who went to the kindergarten in Hamadan city. The pictures of speech intelligibility test (in Persian language) were used to collect speech samples of participants. The participant’s voice was recorded by voice recorder and was investigated in two age groups. Results: The results of this study indicated the means of speech intelligibility was 92.25 for normal children and 35.08 for children with Down syndrome. The correlation between age and speech intelligibility for normal children was 0.866 and for children with Down syndrome was 0.352. The mean of speech intelligibility 2 for normal boys was 93 and for normal girls 91.5 and for boys with Down syndrome 34.66 and for girls with Down syndrome 35.5. Discussion: The difference between normal children and children with Down syndrome was Significant. One of the factors that affects speech intelligibility for children with Down syndrome is difficulty with voluntarily programming, combining, organizing, and sequencing the movements necessary for speech