Orientation Sensor Data Research Articles

Using behavioral biometric sensors of mobile phones for user authentication

In this paper, we use Hand Movement Orientation and Grasp (HMOG) sensor data to authenticate smart phone users. The way a user holds, grasps a mobile phone or touches to it are all key factors for authentication. At the moment of a user makes an event on his/her smart phone, three sensors automatically collect data about magnitude, angular speed and acceleration. Moreover, touching and holding events also produce data about pressure and coordinates. In this paper, we build four types of machine learning algorithms (decision forest, boosted decision tree, support vector machine, logistic regression) to predict user authentication. The data used in this experiment (HMOG) are collected from 100 students. We compare the results of the algorithms and for our scenario, we show that decision forest algorithm with normalized data gives best results.

Detection of In-Progress Phone Calls Using Smartphone Proximity and Orientation Sensors

Smartphones are equipped with embedded sensors, which have been widely used for human activity recognition, context monitoring, and localisation. In this paper, we use smartphone sensors to detect in-progress phone calls and classify the caller's activity states. Phone call detection has a number of practical uses: it can be used to monitor activities at places where calls are forbidden and it can be used to assist activity recognition schemes. We propose a real-time phone call detection scheme using smartphone proximity and orientation sensor data and design Android applications to record, upload and display sensor data. We classify the caller's activity states into three categories: sitting/standing, lying down, and walking. Features are extracted from proximity and orientation sensors to train classifiers using different classification algorithms (Naive Bayes, logistic regression, and support vector machine). Experiments show our system achieves an overall accuracy of 91% in successfully detecting and classifying phone calls.

Detection of in-progress phone calls using smartphone proximity and orientation sensors

Smartphones are equipped with embedded sensors, which have been widely used for human activity recognition, context monitoring, and localisation. In this paper, we use smartphone sensors to detect in-progress phone calls and classify the caller's activity states. Phone call detection has a number of practical uses: it can be used to monitor activities at places where calls are forbidden and it can be used to assist activity recognition schemes. We propose a real-time phone call detection scheme using smartphone proximity and orientation sensor data and design Android applications to record, upload and display sensor data. We classify the caller's activity states into three categories: sitting/standing, lying down, and walking. Features are extracted from proximity and orientation sensors to train classifiers using different classification algorithms (Naive Bayes, logistic regression, and support vector machine). Experiments show our system achieves an overall accuracy of 91% in successfully detecting and classifying phone calls.

A Crowd-Sourcing Indoor Localization Algorithm via Optical Camera on a Smartphone Assisted by Wi-Fi Fingerprint RSSI.

Indoor positioning based on existing Wi-Fi fingerprints is becoming more and more common. Unfortunately, the Wi-Fi fingerprint is susceptible to multiple path interferences, signal attenuation, and environmental changes, which leads to low accuracy. Meanwhile, with the recent advances in charge-coupled device (CCD) technologies and the processing speed of smartphones, indoor positioning using the optical camera on a smartphone has become an attractive research topic; however, the major challenge is its high computational complexity; as a result, real-time positioning cannot be achieved. In this paper we introduce a crowd-sourcing indoor localization algorithm via an optical camera and orientation sensor on a smartphone to address these issues. First, we use Wi-Fi fingerprint based on the K Weighted Nearest Neighbor (KWNN) algorithm to make a coarse estimation. Second, we adopt a mean-weighted exponent algorithm to fuse optical image features and orientation sensor data as well as KWNN in the smartphone to refine the result. Furthermore, a crowd-sourcing approach is utilized to update and supplement the positioning database. We perform several experiments comparing our approach with other positioning algorithms on a common smartphone to evaluate the performance of the proposed sensor-calibrated algorithm, and the results demonstrate that the proposed algorithm could significantly improve accuracy, stability, and applicability of positioning.

TouchSignatures: Identification of user touch actions and PINs based on mobile sensor data via JavaScript

Conforming to W3C specifications, mobile web browsers allow JavaScript code in a web page to access motion and orientation sensor data without the user's permission. The associated risks to user security and privacy are however not considered in W3C specifications. In this work, for the first time, we show how user security can be compromised using these sensor data via browser, despite that the data rate is 3–5 times slower than what is available in app. We examine multiple popular browsers on Android and iOS platforms and study their policies in granting permissions to JavaScript code with respect to access to motion and orientation sensor data. Based on our observations, we identify multiple vulnerabilities, and propose TouchSignatures which implements an attack where malicious JavaScript code on an attack tab listens to such sensor data measurements. Based on these streams, TouchSignatures is able to distinguish the user's touch actions (i.e., tap, scroll, hold, and zoom) and her PINs, allowing a remote website to learn the client-side user activities. We demonstrate the practicality of this attack by collecting data from real users and reporting high success rates using our proof-of-concept implementations. We also present a set of potential solutions to address the vulnerabilities. The W3C community and major mobile browser vendors including Mozilla, Google, Apple and Opera have acknowledged our work and are implementing some of our proposed countermeasures.

Low-Cost MEMS Sensors and Vision System for Motion and Position Estimation of a Scooter

The possibility to identify with significant accuracy the position of a vehicle in a mapping reference frame for driving directions and best-route analysis is a topic which is attracting a lot of interest from the research and development sector. To reach the objective of accurate vehicle positioning and integrate response events, it is necessary to estimate position, orientation and velocity of the system with high measurement rates. In this work we test a system which uses low-cost sensors, based on Micro Electro-Mechanical Systems (MEMS) technology, coupled with information derived from a video camera placed on a two-wheel motor vehicle (scooter). In comparison to a four-wheel vehicle; the dynamics of a two-wheel vehicle feature a higher level of complexity given that more degrees of freedom must be taken into account. For example a motorcycle can twist sideways; thus generating a roll angle. A slight pitch angle has to be considered as well; since wheel suspensions have a higher degree of motion compared to four-wheel motor vehicles. In this paper we present a method for the accurate reconstruction of the trajectory of a “Vespa” scooter; which can be used as alternative to the “classical” approach based on GPS/INS sensor integration. Position and orientation of the scooter are obtained by integrating MEMS-based orientation sensor data with digital images through a cascade of a Kalman filter and a Bayesian particle filter.