scholarly journals Improving Supervised Classification of Activities of Daily Living Using Prior Knowledge

2011 ◽  
Vol 2 (1) ◽  
pp. 17-34 ◽  
Author(s):  
Anthony Fleury ◽  
Norbert Noury ◽  
Michel Vacher
Keyword(s):  
Support Vector Machine ◽  
Activities Of Daily Living ◽  
Daily Living ◽  
Smart Home ◽  
Supervised Classification ◽  
Automatic Recognition ◽  
Support Vector ◽  
Process Data ◽  
At Home

The increase in life expectancy is producing a bottleneck at the entry in institutions. Therefore, telemedicine becomes a timely solution, which is largely explored to care after elderly people living independently at home. It requires identifying the behaviors and activities of the person at home, with non-intrusive sensors and to process data to detect the main trends in the health status. This paper presents the results of the study of prior introduction, in Support Vector Machine, to improve the automatic recognition of Activities of Daily Living. From a set of activities, performed in the experimental smart home in Grenoble, the authors obtained models for seven activities of Daily Living and tested the performances of this classification with introduction of spatial and temporal priors. Eventually, different results are discussed.

scholarly journals Improving Supervised Classification of Activities of Daily Living Using Prior Knowledge

2012 ◽  
pp. 131-147 ◽  
Author(s):  
Anthony Fleury ◽  
Norbert Noury ◽  
Michel Vacher
Keyword(s):  
Support Vector Machine ◽  
Health Status ◽  
Life Expectancy ◽  
Activities Of Daily Living ◽  
Daily Living ◽  
Automatic Recognition ◽  
Support Vector ◽  
Process Data ◽  
At Home

The increase in life expectancy is producing a bottleneck at the entry in institutions. Therefore, telemedicine becomes a timely solution, which is largely explored to care after elderly people living independently at home. It requires identifying the behaviors and activities of the person at home, with non-intrusive sensors and to process data to detect the main trends in the health status. This paper presents the results of the study of prior introduction, in Support Vector Machine, to improve the automatic recognition of Activities of Daily Living. From a set of activities, performed in the experimental smart home in Grenoble, the authors obtained models for seven activities of Daily Living and tested the performances of this classification with introduction of spatial and temporal priors. Eventually, different results are discussed.

scholarly journals Automated Classification of Normal Control and Early-Stage Dementia Based on Activities of Daily Living (ADL) Data Acquired from Smart Home Environment

2021 ◽  
Vol 18 (24) ◽  
pp. 13235
Author(s):  
Lee-Nam Kwon ◽  
Dong-Hun Yang ◽  
Myung-Gwon Hwang ◽  
Soo-Jin Lim ◽  
Young-Kuk Kim ◽  
...  
Keyword(s):  
Activities Of Daily Living ◽  
Normal Control ◽  
Daily Living ◽  
Smart Home ◽  
Early Stage ◽  
Machine Learning Techniques ◽  
Evaluation Tool ◽  
Dementia Patients ◽  
Diagnosis Of Dementia

With the global trend toward an aging population, the increasing number of dementia patients and elderly living alone has emerged as a serious social issue in South Korea. The assessment of activities of daily living (ADL) is essential for diagnosing dementia. However, since the assessment is based on the ADL questionnaire, it relies on subjective judgment and lacks objectivity. Seven healthy seniors and six with early-stage dementia participated in the study to obtain ADL data. The derived ADL features were generated by smart home sensors. Statistical methods and machine learning techniques were employed to develop a model for auto-classifying the normal controls and early-stage dementia patients. The proposed approach verified the developed model as an objective ADL evaluation tool for the diagnosis of dementia. A random forest algorithm was used to compare a personalized model and a non-personalized model. The comparison result verified that the accuracy (91.20%) of the personalized model was higher than that (84.54%) of the non-personalized model. This indicates that the cognitive ability-based personalization showed encouraging performance in the classification of normal control and early-stage dementia and it is expected that the findings of this study will serve as important basic data for the objective diagnosis of dementia.

The Development and Usability Testing of the Smart Home for Wheelchair Users - Focus on the Activities of Daily Living at Home -

2016 ◽  
Vol 11 (2) ◽  
pp. 5
Author(s):  
Myungjoon Lim ◽  
Kyung-Sun Pyo ◽  
KuemJu Lee ◽  
Jiyoung Park ◽  
Hyun Choi ◽  
...  
Keyword(s):  
Activities Of Daily Living ◽  
Daily Living ◽  
Smart Home ◽  
Usability Testing ◽  
Wheelchair Users ◽  
At Home

scholarly journals A Hybrid Classifier for Handwriting Recognition on Multi-domain Financial Bills Based on DCNN and SVM

2020 ◽  
Vol 37 (6) ◽  
pp. 1103-1110
Author(s):  
Shuaiwen Wang ◽  
Bei Yuan ◽  
Di Wu
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Rapid Growth ◽  
Global Economy ◽  
Handwriting Recognition ◽  
Automatic Recognition ◽  
Support Vector ◽  
Hybrid Classifier ◽  
Number Recognition

With the rapid growth of the global economy, the automatic recognition of financial bills becomes the primary way to reduce the burden of the traditional manual approach for bill recognition and classification. However, most automatic recognition methods cannot effectively recognize the handwritten characters on financial bills, especially when the bills come from different financial companies. To solve the problem, this paper fully explores the bill system in banks and the operations of bill number recognition, and then develops a hybrid classifier based on deep convolutional neural network (DCNN) and support vector machine (SVM), with the aim to recognize the handwritten numbers on financial bills in different domains. The DCNN with different channels was adopted to effectively mine the local handwritten numbers on financial bills from varied sources. Then, the extracted information was fed to the SVM to realize accurate classification of numbers. Our method makes full use of the distribution difference between information in different fields, and adapts to different fields based on the parameter sharing mechanism. Experimental results show that our method can recognize the handwritten numbers on financial bills more accurately (>3%) than benchmark methods.

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