DeepWalking: Enabling Smartphone-based Walking Speed Estimation Using Deep Learning

Walking speed estimation is an essential component of mobile apps in various fields such as fitness, transportation, navigation, and health-care. Most existing solutions are focused on specialized medical applications that utilize body-worn motion sensors. These approaches do not serve effectively the general use case of numerous apps where the user holding a smartphone tries to find his or her walking speed solely based on smartphone sensors. However, existing smartphone-based approaches fail to provide acceptable precision for walking speed estimation. This leads to a question: is it possible to achieve comparable speed estimation accuracy using a smartphone over wearable sensor based obtrusive solutions? We find the answer from advanced neural networks. In this paper, we present DeepWalking, the first deep learning-based walking speed estimation scheme for smartphone. A deep convolutional neural network (DCNN) is applied to automatically identify and extract the most effective features from the accelerometer and gyroscope data of smartphone and to train the network model for accurate speed estimation. Experiments are performed with 10 participants using a treadmill. The average root-mean-squared-error (RMSE) of estimated walking speed is 0.16m/s which is comparable to the results obtained by state-of-the-art approaches based on a number of body-worn sensors (i.e., RMSE of 0.11m/s). The results indicate that a smartphone can be a strong tool for walking speed estimation if the sensor data are effectively calibrated and supported by advanced deep learning techniques.Comment: 6 pages, 9 figures, published in IEEE Global Communications Conference (GLOBECOM

Detection of Distracted Pedestrians using Convolutional Neural Networks

The risk of pedestrian accidents has increased due to the distracted walking increase. The research in the autonomous vehicles industry aims to minimize this risk by enhancing the route planning to produce safer routes. Detecting distracted pedestrians plays a significant role in identifying safer routes and hence decreases pedestrian accident risk. Thus, this research aims to investigate how to use the convolutional neural networks for building an algorithm that significantly improves the accuracy of detecting distracted pedestrians based on gathered cues. Particularly, this research involves the analysis of pedestrian’ images to identify distracted pedestrians who are not paying attention when crossing the road. This work tested three different architectures of convolutional neural networks. These architectures are Basic, Deep, and AlexNet. The performance of the three architectures was evaluated based on two datasets. The first is a new training dataset called SCIT and created by this work based on recorded videos of volunteers from Sheridan College Institute of Technology. The second is a public dataset called PETA, which was made up of images with various resolutions. The ConvNet model with the Deep architecture outperformed the Basic and AlexNet architectures in detecting distracted pedestrians

Integração de localização baseada em movimento na aplicação móvel EduPARK

More and more, mobile applications require precise localization solutions in a variety of environments. Although GPS is widely used as localization solution, it may present some accuracy problems in special conditions such as unfavorable weather or spaces with multiple obstructions such as public parks. For these scenarios, alternative solutions to GPS are of extreme relevance and are widely studied recently. This dissertation studies the case of EduPARK application, which is an augmented reality application that is implemented in the Infante D. Pedro park in Aveiro. Due to the poor accuracy of GPS in this park, the implementation of positioning and marker-less augmented reality functionalities presents difficulties. Existing relevant systems are analyzed, and an architecture based on pedestrian dead reckoning is proposed. The corresponding implementation is presented, which consists of a positioning solution using the sensors available in the smartphones, a step detection algorithm, a distance traveled estimator, an orientation estimator and a position estimator. For the validation of this solution, functionalities were implemented in the EduPARK application for testing purposes and usability tests performed. The results obtained show that the proposed solution can be an alternative to provide accurate positioning within the Infante D. Pedro park, thus enabling the implementation of functionalities of geocaching and marker-less augmented reality.Cada vez mais, as aplicações móveis requerem soluções de localização precisa nos mais variados ambientes. Apesar de o GPS ser amplamente usado como solução para localização, pode apresentar alguns problemas de precisão em condições especiais, como mau tempo, ou espaços com várias obstruções, como parques públicos. Para estes casos, soluções alternativas ao GPS são de extrema relevância e veem sendo desenvolvidas. A presente dissertação estuda o caso do projeto EduPARK, que é uma aplicação móvel de realidade aumentada para o parque Infante D. Pedro em Aveiro. Devido à fraca precisão do GPS nesse parque, a implementação de funcionalidades baseadas no posionamento e de realidade aumentada sem marcadores apresenta dificuldades. São analisados sistemas relevantes existentes e é proposta uma arquitetura baseada em localização de pedestres. Em seguida é apresentada a correspondente implementação, que consiste numa solução de posicionamento usando os sensores disponiveis nos smartphones, um algoritmo de deteção de passos, um estimador de distância percorrida, um estimador de orientação e um estimador de posicionamento. Para a validação desta solução, foram implementadas funcionalidades na aplicação EduPARK para fins de teste, e realizados testes com utilizadores e testes de usabilidade. Os resultados obtidos demostram que a solução proposta pode ser uma alternativa para a localização no interior do parque Infante D. Pedro, viabilizando desta forma a implementação de funcionalidades baseadas no posicionamento e de realidade aumenta sem marcadores.EduPARK é um projeto financiado por Fundos FEDER através do Programa Operacional Competitividade e Internacionalização - COMPETE 2020 e por Fundos Nacionais através da FCT - Fundação para a Ciência e a Tecnologia no âmbito do projeto POCI-01-0145-FEDER-016542.Mestrado em Engenharia Informátic

Nomadic input on mobile devices: the influence of touch input technique and walking speed on performance and offset modeling

In everyday life people use their mobile phones on-the-go with different walking speeds and with different touch input techniques. Unfortunately, much of the published research in mobile interaction does not quantify the influence of these variables. In this paper, we analyze the influence of walking speed, gait pattern and input techniques on commonly used performance parameters like error rate, accuracy and tapping speed, and we compare the results to the static condition. We examine the influence of these factors on the machine learned offset model used to correct user input and we make design recommendations. The results show that all performance parameters degraded when the subject started to move, for all input techniques. Index finger pointing techniques demonstrated overall better performance compared to thumb-pointing techniques. The influence of gait phase on tap event likelihood and accuracy was demonstrated for all input techniques and all walking speeds. Finally, it was shown that the offset model built on static data did not perform as well as models inferred from dynamic data, which indicates the speed-specific nature of the models. Also, models identified using specific input techniques did not perform well when tested in other conditions, demonstrating the limited validity of offset models to a particular input technique. The model was therefore calibrated using data recorded with the appropriate input technique, at 75% of preferred walking speed, which is the speed to which users spontaneously slow down when they use a mobile device and which presents a tradeoff between accuracy and usability. This led to an increase in accuracy compared to models built on static data. The error rate was reduced between 0.05% and 5.3% for landscape-based methods and between 5.3% and 11.9% for portrait-based methods

Cascaded Neural Networks for Identification and Posture-Based Threat Assessment of Armed People

This paper presents a near real-time, multi-stage classifier which identifies people and handguns in images, and then further assesses the threat-level that a person poses based on their body posture. The first stage consists of a convolutional neural network (CNN) that determines whether a person and a handgun are present in an image. If so, a second stage CNN is then used to estimate the pose of the person detected to have a handgun. Lastly, a feed-forward neural network (NN) makes the final threat assessment based on the joint positions of the person’s skeletal pose estimate from the previous stage. On average, this entire pipeline requires less than 1 second of processing time on a desktop computer. The model was trained using approximately 2,000 images and achieved a pistol and person detection rate of 22% and 55%, respectively. The final stage NN correctly identified the severity of the threat with 84% accuracy. The images used to train each stage of our multi-classifier model are available online. With an expanded dataset the accuracy of detecting people and pistols can likely be improved in the future

Motion Compatibility for Indoor Localization

Indoor localization -- a device's ability to determine its location within an extended indoor environment -- is a fundamental enabling capability for mobile context-aware applications. Many proposed applications assume localization information from GPS, or from WiFi access points. However, GPS fails indoors and in urban canyons, and current WiFi-based methods require an expensive, and manually intensive, mapping, calibration, and configuration process performed by skilled technicians to bring the system online for end users. We describe a method that estimates indoor location with respect to a prior map consisting of a set of 2D floorplans linked through horizontal and vertical adjacencies. Our main contribution is the notion of "path compatibility," in which the sequential output of a classifier of inertial data producing low-level motion estimates (standing still, walking straight, going upstairs, turning left etc.) is examined for agreement with the prior map. Path compatibility is encoded in an HMM-based matching model, from which the method recovers the user s location trajectory from the low-level motion estimates. To recognize user motions, we present a motion labeling algorithm, extracting fine-grained user motions from sensor data of handheld mobile devices. We propose "feature templates," which allows the motion classifier to learn the optimal window size for a specific combination of a motion and a sensor feature function. We show that, using only proprioceptive data of the quality typically available on a modern smartphone, our motion labeling algorithm classifies user motions with 94.5% accuracy, and our trajectory matching algorithm can recover the user's location to within 5 meters on average after one minute of movements from an unknown starting location. Prior information, such as a known starting floor, further decreases the time required to obtain precise location estimate

SLAM for Visually Impaired People: A Survey

In recent decades, several assistive technologies for visually impaired and blind (VIB) people have been developed to improve their ability to navigate independently and safely. At the same time, simultaneous localization and mapping (SLAM) techniques have become sufficiently robust and efficient to be adopted in the development of assistive technologies. In this paper, we first report the results of an anonymous survey conducted with VIB people to understand their experience and needs; we focus on digital assistive technologies that help them with indoor and outdoor navigation. Then, we present a literature review of assistive technologies based on SLAM. We discuss proposed approaches and indicate their pros and cons. We conclude by presenting future opportunities and challenges in this domain.Comment: 26 pages, 5 tables, 3 figure

Real-time human ambulation, activity, and physiological monitoring:taxonomy of issues, techniques, applications, challenges and limitations

Automated methods of real-time, unobtrusive, human ambulation, activity, and wellness monitoring and data analysis using various algorithmic techniques have been subjects of intense research. The general aim is to devise effective means of addressing the demands of assisted living, rehabilitation, and clinical observation and assessment through sensor-based monitoring. The research studies have resulted in a large amount of literature. This paper presents a holistic articulation of the research studies and offers comprehensive insights along four main axes: distribution of existing studies; monitoring device framework and sensor types; data collection, processing and analysis; and applications, limitations and challenges. The aim is to present a systematic and most complete study of literature in the area in order to identify research gaps and prioritize future research directions