The movement of a phone in an environment with different brightness, makes the luminance prediction challenging. The
ambient light sensor takes time to modify the brightness of the screen based on the environment it is placed in. This
causes an unsatisfactory user experience and delays in adjustment of the screen brightness. In this research, a method is
proposed for enhancing the prediction of luminance using accelerometer, gyroscope and speed measurement technique.
The speed of the phone is identified using Sum-of-Sine parameters. The lux values are then fused with the accelerometer
and gyroscope data to present more accurate luminance values for the ALS based on the movement of the phone. An
investigation is made during the movement of the user in a standard lighting environment. This enhances the user
experience and improves the screen brightness precision. The accuracy has given an R-Square value of up to 0.97.
The present generation of mobile handheld devices comes equipped with a large number of sensors. The key sensors include the Ambient Light Sensor, Proximity Sensor, Gyroscope, Compass and the Accelerometer. Many mobile applications are driven based on the readings obtained from either one or two of these sensors. However the presence of multiple-sensors will enable the determination of more detailed activities that are carried out by the user of a mobile device, thus enabling smarter mobile applications to be developed that responds more appropriately to user behavior and device usage. In the proposed research we use recent advances in machine learning to fuse together the data obtained from all key sensors of a mobile device. We investigate the possible use of single and ensemble classifier based approaches to identify a mobile device’s behavior in the space it is present. Feature selection algorithms are used to remove non-discriminant features that often lead to poor classifier performance. As the sensor readings are noisy and include a significant proportion of missing values and outliers, we use machine learning based approaches to clean the raw data obtained from the sensors, before use. Based on selected practical case studies, we demonstrate the ability to accurately recognize device behavior based on multi-sensor data fusion.
The present generation of Ambient Light Sensors (ALS) of a mobile handheld device suffer from two practical shortcomings. The ALSs are narrow angle, i.e. they respond effectively only within a narrow angle of operation and there is a latency of operation. As a result mobile applications that operate based on the ALS readings could perform sub-optimally especially when operated in environments with non-uniform illumination. The applications will either adopt with unacceptable levels of latency or/and may demonstrate a discrete nature of operation. In this paper we propose a framework to predict the ambient illumination of an environment in which a mobile device is present. The predictions are based on an illumination model that is developed based on a small number of readings taken during an application calibration stage. We use a machine learning based approach in developing the models. Five different regression models were developed, implemented and compared based on Polynomial, Gaussian, Sum of Sine, Fourier and Smoothing Spline functions. Approaches to remove noisy data, missing values and outliers were used prior to the modelling stage to remove their negative effects on modelling. The prediction accuracy for all models were found to be above 0.99 when measured using R-Squared test with the best performance being from Smoothing Spline. In this paper we will discuss mathematical complexity of each model and investigate how to make compromises in finding the best model.