Radar data representation for classification of activities of daily living

2020 ◽  
pp. 99-122
Keyword(s):  
Activities Of Daily Living ◽  
Daily Living ◽  
Radar Data ◽  
Data Representation

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.

scholarly journals Classification of Activities of Daily living using Smart Phones

2019 ◽  
Vol 8 (3) ◽  
pp. 2984-2988
Keyword(s):  
Activities Of Daily Living ◽  
Daily Living ◽  
Human Life ◽  
Smart Phone ◽  
Smart Phones ◽  
Machine Learning Techniques ◽  
Accelerometer Sensor ◽  
Learning Techniques ◽  
Direct Input

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.

Decision support systems for home monitoring applications: Classification of activities of daily living and epileptic seizures

2016 ◽  
pp. 271-291
Author(s):  
Stijn Luca Luca ◽  
Lode Vuegen Vuegen ◽  
Hugo Van hamme Van hamme ◽  
Peter Karsmakers Karsmakers ◽  
Bart Vanrumste Vanrumste
Keyword(s):  
Decision Support ◽  
Activities Of Daily Living ◽  
Decision Support Systems ◽  
Daily Living ◽  
Support Systems ◽  
Home Monitoring ◽  
Epileptic Seizures ◽  
Monitoring Applications

Power-Efficient Interrupt-Driven Algorithms for Fall Detection and Classification of Activities of Daily Living

2015 ◽  
Vol 15 (3) ◽  
pp. 1377-1387 ◽  
Author(s):  
Jian Yuan ◽  
Kok Kiong Tan ◽  
Tong Heng Lee ◽  
Gerald Choon Huat Koh
Keyword(s):  
Activities Of Daily Living ◽  
Daily Living ◽  
Fall Detection ◽  
Power Efficient

scholarly journals Consensus on the Definition of Advanced Parkinson’s Disease: A Neurologists-Based Delphi Study (CEPA Study)

2017 ◽  
Vol 2017 ◽  
pp. 1-8 ◽  
Author(s):  
Maria-Rosario Luquin ◽  
Jaime Kulisevsky ◽  
Pablo Martinez-Martin ◽  
Pablo Mir ◽  
Eduardo S. Tolosa
Keyword(s):  
Activities Of Daily Living ◽  
Movement Disorders ◽  
Delphi Study ◽  
Daily Living ◽  
Ad Hoc ◽  
Motor Fluctuations ◽  
Key Factors ◽  
Advanced Parkinson’S Disease ◽  
Definition Of

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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