Analysis of the sleep quality of elderly people using biomedical signals.

This paper presents a technical solution that analyses sleep signals captured by biomedical sensors to find possible disorders during rest. Specifically, the method evaluates electrooculogram (EOG) signals, skin conductance (GSR), air flow (AS), and body temperature. Next, a quantitative sleep quality analysis determines significant changes in the biological signals, and any similarities between them in a given time period. Filtering techniques such as the Fourier transform method and IIR filters process the signal and identify significant variations. Once these changes have been identified, all significant data is compared and a quantitative and statistical analysis is carried out to determine the level of a person's rest. To evaluate the correlation and significant differences, a statistical analysis has been calculated showing correlation between EOG and AS signals (p=0,005), EOG, and GSR signals (p=0,037) and, finally, the EOG and Body temperature (p=0,04). Doctors could use this information to monitor changes within a patient.

Vocal folds morphological pathologies detection using Gabor filtering and Principal Component Analysis.

BACKGROUND: Given the importance of the voice in our daily lives, any study focused on its pathologies and the way of caring and promoting the health of them is of common interest. OBJECTIVE: This paper describes a method to automatically aid indetecting vocal folds benign pathologies based on glottal space segmentation vocal fold video sequences captured by a laryngoscope. METHODS: The proposed algorithm is based on automatic segmentation supported by Gabor filters, and features classification with Principal Component Analysis (PCA) to achieve the expected results. RESULTS: The authors wish to emphasize that the proposed algorithm is independent from the images' resolution and zoom, but their quality depends on specialist experience with the instrumentation. Segmentation block provides good results for 95% of images and classification block distinguishes successfully between pathological and healthy images in 92.1% of cases. The proposed system's findings have been compared with the diagnosis made by doctors and it obtains the same results in all the 45 sequences. CONCLUSIONS: One of the proposed study's key elements has been which objective measurements are of significance for the specialist. In this case, it is those that enable the specialist to calculate the size of the pathology (previously classified automatically) that he/she is observing, thus enabling them to provide the patient with more information or to prescribe treatment and even measure its development.

Caregiver and social assistant robot for rehabilitation and coaching for the elderly.

BACKGROUND: Socially assistive robotics (SAR) has been a major field of investigation during the last decade and, as it develops, the groups the technology can be applied to and all ways in which these can be assisted are rapidly increasing. OBJECTIVE: The main objective is to design and develop a complete robotic agent, so that it performs physical and mental activities for elderly people to maintain their healthy life habits and, as a final result, improve their quality of life. METHODS: LEGO Mindstorms NXT® robot's unique capacity for adaptability and engaging its users to develop coaching activities and assistive rehabilitation for the elderly. Such activities will aim to enhance healthy habits and provide training in physical and mental rehabilitation. The robot is attached to an iPod Touch that acts as its interface. RESULTS: The robot has been tested by a voluntary group of residents, also from that retirement home. Results in the variables of the questionnaire show scores above 4 points out of 5 for all the categories. CONCLUSIONS: Based on the tests, an easy to use Robot is prepared to deliver basic coaching for physical activities as proposed by the client, the staff of La Misericordia, who confirmed their satisfaction regarding this aspect.