Radar data representation for classification of activities of daily living

Author(s):  
Lee-Nam Kwon ◽  
Dong-Hun Yang ◽  
Myung-Gwon Hwang ◽  
Soo-Jin Lim ◽  
Young-Kuk Kim ◽  
...  

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.


2019 ◽  
Vol 8 (3) ◽  
pp. 2984-2988

Smart phones have become an integral part of everyday human life. These phones are packed with various sensors for different purposes. Most of them are used for understanding the environment in which the user uses the phone so that the device could respond rapidly. Indirectly the phone extracts context information of the users like the activity performed using accelerometer and gyroscope sensors. This information can be used for a variety of applications like home automation, smart environment, etc to perform automatic changes to the environment without direct input from the user. This paper deals with the classification of activities of daily living like walking, jogging, sitting, standing, upstairs and downstairs using the data collected from accelerometer sensor within the smart phone. A comparative analysis has been performed on different machine learning techniques for activity classification.


2015 ◽  
Vol 15 (3) ◽  
pp. 1377-1387 ◽  
Author(s):  
Jian Yuan ◽  
Kok Kiong Tan ◽  
Tong Heng Lee ◽  
Gerald Choon Huat Koh

2017 ◽  
Vol 2017 ◽  
pp. 1-8 ◽  
Author(s):  
Maria-Rosario Luquin ◽  
Jaime Kulisevsky ◽  
Pablo Martinez-Martin ◽  
Pablo Mir ◽  
Eduardo S. Tolosa

To date, no consensus exists on the key factors for diagnosing advanced Parkinson disease (APD). To obtain consensus on the definition of APD, we performed a prospective, multicenter, Spanish nationwide, 3-round Delphi study (CEPA study). An ad hoc questionnaire was designed with 33 questions concerning the relevance of several clinical features for APD diagnosis. In the first-round, 240 neurologists of the Spanish Movement Disorders Group participated in the study. The results obtained were incorporated into the questionnaire and both, results and questionnaire, were sent out to and fulfilled by 26 experts in Movement Disorders. Review of results from the second-round led to a classification of symptoms as indicative of “definitive,” “probable,” and “possible” APD. This classification was confirmed by 149 previous participating neurologists in a third-round, where 92% completely or very much agreed with the classification. Definitive symptoms of APD included disability requiring help for the activities of daily living, presence of motor fluctuations with limitations to perform basic activities of daily living without help, severe dysphagia, recurrent falls, and dementia. These results will help neurologists to identify some key factors in APD diagnosis, thus allowing users to categorize the patients for a homogeneous recognition of this condition.


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