An Energy-Efficient Multi-Tier Architecture for Fall Detection Using Smartphones.

Automatic detection of fall events is vital to providing fast medical assistance to the causality, particularly when the injury causes loss of consciousness. Optimization of the energy consumption of mobile applications, especially those which run 24/7 in the background, is essential for longer use of smartphones. In order to improve energy-efficiency without compromising on the fall detection performance, we propose a novel 3-tier architecture that combines simple thresholding methods with machine learning algorithms. The proposed method is implemented on a mobile application, called uSurvive, for Android smartphones. It runs as a background service and monitors the activities of a person in daily life and automatically sends a notification to the appropriate authorities and/or user defined contacts when it detects a fall. The performance of the proposed method was evaluated in terms of fall detection performance and energy consumption. Real life performance tests conducted on two different models of smartphone demonstrate that our 3-tier architecture with feature reduction could save up to 62% of energy compared to machine learning only solutions. In addition to this energy saving, the hybrid method has a 93% of accuracy, which is superior to thresholding methods and better than machine learning only solutions.

Advanced computing solutions for analysis of laryngeal disorders.

Clinical diagnosis of voice pathologies is performed by analyzing audio, color, shape, and vibration patterns of the laryngeal recordings which are taken with medical imaging devices such as video-laryngostroboscope, direct laryngoscopy, and high-speed videoendoscopes. This paper examines state-of-the-art methods and reveals open issues and problems of computing solutions for analysis and identification of laryngeal disorders. We propose a categorical representation of the most significant applications published so far in terms of their scopes, used methodologies, and achieved results. Laryngeal image/video analysis is discussed in four main categories: segmentation of vocal folds, classification of vocal fold disorders, vocal fold vibration analysis, and vocal fold image stitching. By this study, we reveal new opportunities and potentials of vision-based computerized solutions for evaluation, early diagnosis, and prevention of laryngeal disorders. Graphical abstract.

Assessment of videolaryngostroboscopy images based on visible vessels of vocal folds.

Extraction of vessel structures and the vessel features automatically forms an essential step for computer-aided diagnosis. Visible vessels of vocal folds become a diagnostic aid for vocal fold pathologies by publication of limited number of researches which analyze the effects of vocal fold pathologies on visual characteristics of blood vessels. In this paper we present a novel system that extracts blood vessels centerlines on vocal folds images and detects pathologically altered vocal folds exploiting visual characteristics of vessels.

Dysphonic speech reconstruction: construction of a novel system for effective and efficient communication.

In this article, a MELP-based real-time system that produces synthetic speech for patients with chronic dysphonia while preserving their continual structure of speech, was presented. Dysphonic patients, even though strained, could express themselves within face-to-face communications. However, when speaking in a group or using communication devices such as mobile phones, they face serious difficulties in vocal communication since it becomes harder to understand their speech. This system will make the speech of such a patient much more understandable by reconstructing the patients' dysphonic speech in real time. This system can easily be fitting into an embedded system of the size and shape of a mobile phone, so that it could be carried all the time by the patients.