Multi Sensor-Based Implicit User Identification

Smartphones have ubiquitously integrated into our home and work environments,however,users normally rely on explicit but inefficient identification processes in a controlled environment.Therefore,when a device is stolen,a thief can have access to the owner’s personal information and services against the stored passwords.As a result of this potential scenario,this work proposes an automatic legitimate user identification system based on gait biometrics extracted from user walking patterns captured by smartphone sensors.A set of preprocessing schemes are applied to calibrate noisy and invalid samples and augment the gait-induced time and frequency domain features,then further optimized using a non-linear unsupervised feature selection method.The selected features create an underlying gait biometric representation able to discriminate among individuals and identify them uniquely.Different classifiers are adopted to achieve accurate legitimate user identification.Extensive experiments on a group of 16 individuals in an indoor environment show the effectiveness of the proposed solution:with 5 to 70 samples per window,KNN and bagging classifiers achieve 87–99%accuracy,82–98%for ELM,and 81–94%for SVM.The proposed pipeline achieves a 100%true positive and 0%false-negative rate for almost all classifiers.

Near real-time characterisation of urban environments: a holistic approach for monitoring dengue fever risk areas

Despite frequent use of digital devices in everyday life,cost-effective measurement of public health issues in urban areas is still challenging.This study was,therefore,planned to extract land-use types using object-based and spatial metric approaches to explore the dengue incidence in relation to the surrounding environment in near real-time using Google and Advanced Land Observation Satellite images.The characterised image showed useful classification of an urban areawith 77%accuracy and 0.68 kappa.Geospatial analysis on public health data indicated that most of the dengue cases were found in densely populated areas surrounded by dense vegetation.People living in independent houses having sparsely vegetated surroundings were found to be less vulnerable.Disease incidence was more prevalent in people of 5-24 years of age(67%);while in terms of occupation,mostly students,the unemployed,labourers and farmers(88%)were affected.In general,males were affected slightly more(10%)than females.Proximity analyses indicated that most of the dengue cases were around institutions(40%),religious places(18%)and markets(15%).Thus,usage of Digital Earth scalable tools for monitoring health issues would open new ways for maintaining a healthy and sustainable society in the years ahead.