Automated Estimation of Elder Activity Levels from Anonymized Video Data

Significant declines in quality of life for elders in assisted living communities are typically triggered by health events. Given the necessary information, such events can often be predicted, and thus, be avoided or reduced in severity. Statistics on activities of daily living and activity level over an extended period of time provide important data for functional assessment and health prediction. However, persistent activity monitoring and continuous collection of this type of data is extremely labor-intensive, time-consuming, and costly. In this work, we propose a method for automated estimation of activity levels based on silhouettes segmented from video data, and subsequent extraction of higher order information from the silhouettes. By building a regression model from this higher order information, our system can automatically estimate elder activity levels

Activity Analysis, Summarization, and Visualization for Indoor Human Activity Monitoring

DOI 10.1109/TCSVT.2008.2005612In this work, we study how continuous video monitoring and intelligent video processing can be used in eldercare to assist the independent living of elders and to improve the efficiency of eldercare practice. More specifically, we develop an automated activity analysis and summarization for eldercare video monitoring. At the object level, we construct an advanced silhouette extraction, human detection and tracking algorithm for indoor environments. At the feature level, we develop an adaptive learning method to estimate the physical location and moving speed of a person from a single camera view without calibration. At the action level, we explore hierarchical decision tree and dimension reduction methods for human action recognition. We extract important ADL (activities of daily living) statistics for automated functional assessment. To test and evaluate the proposed algorithms and methods, we deploy the camera system in a real living environment for about a month and have collected more than 200 hours (in excess of 600 G bytes) of activity monitoring videos. Our extensive tests over these massive video datasets demonstrate that the proposed automated activity analysis system is very efficient.This work was supported in part by National Institute of Health under Grant 5R21AG026412

A Real-time System for In-home Activity Monitoring of Elders

In this paper, we propose a real-time system for in-home activity monitoring and functional assessment for elder care. We describe the development of the whole system which could be used to assist the independent living of elders and improve the efficiency of eldercare practice. With this system, data is collected, silhouettes extracted, features further analyzed and visualized into graphs from which eldercare professionals are able to understand massive video monitoring data within a short period of time. Our experimental results demonstrate that the proposed system is efficient in indoor elder activities monitoring and easily utilized by eldercare professionals.This work was supported in part by National Institutes of Health under grant 1R21AG026412-01A2

Activity Segmentation of Infrared Images Using Fuzzy Clustering Techniques

Every year, many older adults are at risk for falling, especially in the dark. Infrared lighting provides a nonintrusive lighting in the dark and our research shows a technique of segmenting human activities using fuzzy clustering of image moments even in the dark. While our research is still in the preliminary stages, it shows promise of being able to detect several different activities and in the future might prevent several falls from taking place

Activity Segmentation of Infrared Images Using Fuzzy Clustering Techniques

Every year, many older adults are at risk for falling, especially in the dark. Infrared lighting provides a nonintrusive lighting in the dark and our research shows a technique of segmenting human activities using fuzzy clustering of image moments even in the dark. While our research is still in the preliminary stages, it shows promise of being able to detect several different activities and in the future might prevent several falls from taking place

Modeling Semantic Relations between Visual Attributes and Object Categories via Dirichlet Forest Prior

ABSTRACT In this paper, we deal with two research issues: the automation of visual attribute identification and semantic relation learning between visual attributes and object categories. The contribution is two-fold, firstly, we provide uniform framework to reliably extract both categorical attributes and depictive attributes. Secondly, we incorporate the obtained semantic associations between visual attributes and object categories into a text-based topic model and extract descriptive latent topics from external textual knowledge sources. Specifically, we show that in mining natural language descriptions from external knowledge sources, the relation between semantic visual attributes and object categories can be encoded as Must-Links and Cannot-Links, which can be represented by Dirichlet-Forest prior. To alleviate the workload of manual supervision and labeling in image categorization process, we introduce a semi-supervised training framework using soft-margin semi-supervised SVM classifier. We also show that the large-scale image categorization results can be significantly improved by combining automatically acquired visual attributes. Experimental results show that the proposed model achieves better ability in describing object-related attributes and makes the inferred latent topics more descriptive

Modeling Semantic Relations between Visual Attributes and Object Categories via Dirichlet Forest Prior

ABSTRACT In this paper, we deal with two research issues: the automation of visual attribute identification and semantic relation learning between visual attributes and object categories. The contribution is two-fold, firstly, we provide uniform framework to reliably extract both categorical attributes and depictive attributes. Secondly, we incorporate the obtained semantic associations between visual attributes and object categories into a text-based topic model and extract descriptive latent topics from external textual knowledge sources. Specifically, we show that in mining natural language descriptions from external knowledge sources, the relation between semantic visual attributes and object categories can be encoded as Must-Links and Cannot-Links, which can be represented by Dirichlet-Forest prior. To alleviate the workload of manual supervision and labeling in image categorization process, we introduce a semi-supervised training framework using soft-margin semi-supervised SVM classifier. We also show that the large-scale image categorization results can be significantly improved by combining automatically acquired visual attributes. Experimental results show that the proposed model achieves better ability in describing object-related attributes and makes the inferred latent topics more descriptive

Greatly improved piezoelectricity and thermal stability of (Na, Sm) Co-doped CaBi2Nb2O9 ceramics

Calcium bismuth niobate (CaBi2Nb2O9) is regarded as one of the most potential high-temperature piezoelectric materials owing to its highest Curie point in bismuth layer-structured ferroelectrics. Nevertheless, low piezoelectric coefficient and low resistivity at high temperature considerably restrict its development as key electronic components. Herein, markedly improved piezoelectric properties and DC resistivity of CaBi2Nb2O9 ceramics through Na+ and Sm3+ co-doping are reported. The nominal compositions Ca1-2x(Na, Sm)xBi2Nb2O9 (x ​= ​0, 0.01, 0.025, and 0.05) ceramics have been prepared via the conventional solid state method. An optimum composition of Ca0.95(Na, Sm)0.025Bi2Nb2O9 is obtained, which possesses a high Curie point of ∼949 ​°C, a piezoelectric coefficient of ∼12.8 ​pC/N, and a DC electrical resistivity at 500 ​°C of ∼4 ​× ​107 ​Ω ​·cm. The improved d33 is probably ascribed to the reduction in domain size and the increase in domain wall density caused by the reduced grain size. More importantly, after annealing at 900 ​°C for 2 ​h, the piezoelectric coefficient still maintains about 90% of the initial d33 value, which displays a significant improvement compared to pure CaBi2Nb2O9 ceramic with only 44% of the initial d33 value. This work exhibits a feasible approach to simultaneously obtain high piezoelectric property and thermal stability in CaBi2Nb2O9 ceramics by Na+/Sm3+ co-doping