Abstract Pain is a complex phenomenon, the experience of which varies widely across individuals. At worst, chronic pain can lead to anxiety and depression. Cost-effective strategies are urgently needed to improve the treatment of pain, and thus we propose a novel home-based pain measurement system for the longitudinal monitoring of pain experience and variation in different patients with chronic low back pain. The autonomous nervous system and audio-visual features are analyzed from heart rate signals, voice characteristics and facial expressions using a unique measurement protocol. Self-reporting is utilized for the follow-up of changes in pain intensity, induced by well-designed physical maneuvers, and for studying the consecutive trends in pain. We describe the study protocol, including hospital measurements and questionnaires and the implementation of the home measurement devices. We also present different methods for analyzing the multimodal data: electroencephalography, audio, video and heart rate. Our intention is to provide new insights using technical methodologies that will be beneficial in the future not only for patients with low back pain but also patients suffering from any chronic pain.

We develop and evaluate a data hiding method that enables smartphones to encrypt and embed sensitive information into carrier streams of sensor data. Our evaluation considers multiple handsets and a variety of data types, and we demonstrate that our method has a computational cost that allows real-time data hiding on smartphones with negligible distortion of the carrier stream. These characteristics make it suitable for smartphone applications involving privacy-sensitive data such as medical monitoring systems and digital forensics tools.

Abstract: The intellectual property infringement in music due to the proliferation of the internet and the ease of creating and distributing identical digital objects has brought watermarking techniques to the forefront of digital rights protection. Towards this direction, a significant number of watermarking techniques have been proposed in recent years in order to create robust and imperceptible audio watermarks. In this work we propose an au-dio watermarking technique which efficiently and secretly embeds information, or equivalently watermarks, into an audio digital signal. Our technique is based on the main idea of a recently proposed image water-marking technique expanding thus the digital objects that can be efficiently watermarked through the use of self-inverting permutations. More precisely, our audio watermarking technique uses the 1D representation of self-inverting permutations and utilizes marking at specific areas thanks to partial modifications of the au-dio’s Discrete Fourier Transform (DFT); these modifications are made on the magnitude of specific frequency bands. We have evaluated the embedding and extracting algorithms by testing them on various and different in characteristics audio signals that were in WAV format and we have obtained positive results. The algorithms have been developed and tested using the mathematical software package Matlab. 1