Pedestrian tracking systems implemented in regular smartphones may provide a convenient mechanism for wayfinding and backtracking for people who are blind. However, virtually all existing studies only considered sighted participants, whose gait pattern may be different from that of blind walkers using a long cane or a dog guide. In this contribution, we present a comparative assessment of several algorithms using inertial sensors for pedestrian tracking, as applied to data from WeAllWalk, the only published inertial sensor dataset collected indoors from blind walkers. We consider two situations of interest. In the first situation, a map of the building is not available, in which case we assume that users walk in a network of corridors intersecting at 45° or 90°. We propose a new two-stage turn detector that, combined with an LSTM-based step counter, can robustly reconstruct the path traversed. We compare this with RoNIN, a state-of-the-art algorithm based on deep learning. In the second situation, a map is available, which provides a strong prior on the possible trajectories. For these situations, we experiment with particle filtering, with an additional clustering stage based on mean shift. Our results highlight the importance of training and testing inertial odometry systems for assisted navigation with data from blind walkers.

We present a comparative analysis of inertial-based odometry algorithms for the purpose of assisted return. An assisted return system facilitates backtracking of a path previously taken, and can be particularly useful for blind pedestrians. We present a new algorithm for path matching, and test it in simulated assisted return tasks with data from WeAllWalk, the only existing data set with inertial data recorded from blind walkers. We consider two odometry systems, one based on deep learning (RoNIN), and the second based on robust turn detection and step counting. Our results show that the best path matching results are obtained using the turns/steps odometry system.

Introduction:Among important factors in hospitalized patients' satisfaction is respect for their privacy, which can accelerate the healing process and reduce hospitalization time. This study aimed to determine the most important barriers of patients' privacy from nurses' view point in educational-therapeutic centers affiliated to Gilan University of Medical Science in Rasht city in 2015-2016. Methods and Materials:This is a cross-sectional, descriptive, analytical study that was conducted on 186 nursing managers chosen by census method and 230 nurses chosen by stratified random method. Data were collected using reliable and valid researcher-made questionnaires. SPSS21 data analysis software and descriptive statistics (mean, standard deviation) and Factor Analysis were performed. Results:The results showed that through Factor Analysis of the 23 words of the questionnaire, the most important barriers to patient privacy in the four areas were as follows: management domain with special value (7.6 ) and variance percent predicted 24.7% 2) equipment and facilities and manpower domain (environmental domain) with special value of 3.54 and variance percent predicted 20.08%,3) individual-caring domain with special value of 1.82 and variance percent predicted 13.85% ,4) cognitive domain with special value of 1.37 and variance percent predicted 12/9% and in the range of 71.73% of total variance can predict hidden obstacles privacy. Conclusion:The results showed that the management domain is the most important areas effective in noncompliance with patient privacy. It is suggested that retraining courses be held in this area so that nursing managers get more familiar with it.