Detecting Dementia-Related Wandering Locomotion of Elders by Leveraging Active Infrared Sensors

For elders with dementia, wandering is among the most problematic, frequent and dangerous behavior. Managing wandering behavior has become increasingly imperative due to its high prevalence, negative outcomes and burden on caregivers. We study to propose an active infrared-based method to identify wandering locomotion by monitoring rhythmical repetition of an elder’s indoor motion events. Specifically, we utilize our customized active infrared sensors to collect human indoor motions that will be converted into motion events by using hardware redundancy technique. Each motion event is a directed motion obtained via introducing temporal and dimensions into the spatial motion data. Based on the most cited spatial-temporal patterns of wandering locomotion, a spatiotemporal model is then proposed to identify wandering locomotion from an ongoing sequence of motion events. Experimental evaluation on eight individuals’ real-world motion datasets has shown that our proposed method is able to effectively identify wandering locomotion from repetitive events collected from active infrared sensors with a value over 98% for both accuracy and precision based on properly chosen parameters. Wandering in elders with dementia that follow specific spatiotemporal patterns can be reliably identified by analyzing repetitive motion events collected from active infrared sensors based on the well-known spatiotemporal patterns of wandering locomotion.

A decomposition based algorithm for maximal contractions

This paper proposes a decomposition based algo- rithm for revision problems in classical propositional logic. A set of decomposing rules are presented to analyze the satis- fiability of formulas. The satisfiability of a formula is equivalent to the satisfiability of a set of literal sets. A decomposing function is constructed to calculate all satisfiable literal sets of a given formula. When expressing the satisfiability of a formula, these literal sets are equivalent to all satisfied models of such formula. A revision algorithm based on this decomposing function is proposed, which can calculate maximal contractions of a given problem. In order to reduce the memory requirement, a filter function is introduced. The improved algorithm has a good performance in both time and space perspectives.

A Large Chinese Text Dataset in the Wild

In this paper,we introduce a very large Chinese text dataset,in the wild.While optical character recognition(OCR)in document images is well studied and many commercial tools are available,the detection and recognition of text in natural images is still a challenging problem,especially for some more complicated character sets such as Chinese text.Lack of training data has always been a problem,especially for deep learning methods which require massive training data.In this paper,we provide details of a newly created dataset of Chinese text with about 1 million Chinese characters from 3850 unique ones annotated by experts in over 30 000 street view images.This is a challenging dataset with good diversity containing planar text,raised text,text under poor illumination,distant text,partially occluded text,etc.For each character,the annotation includes its underlying character,bounding box,and six attributes.The attributes indicate the charactcr's background complexity,appearance,style,etc.Besides the dataset,we give baseline results using state-of-the-art methods for tliree tasks:character recognition(top-1 accuracy of 80.5%),character detection(AP of 70.9%),and text line detection(AED of 22.1).The dataset,source code,and trained models are publicly available.