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.

Clinical and economic characteristics associated with direct costs of Alzheimer's, frontotemporal and vascular dementia in Argentina

2010 ◽  
Vol 23 (4) ◽  
pp. 554-561 ◽  
Author(s):  
Galeno Rojas ◽  
Leonardo Bartoloni ◽  
Carol Dillon ◽  
Cecilia M. Serrano ◽  
Monica Iturry ◽  
...  
Keyword(s):  
Activities Of Daily Living ◽  
Vascular Dementia ◽  
Daily Living ◽  
Economic Cost ◽  
Direct Costs ◽  
Medication Costs ◽  
Dementia Patients ◽  
Hospitalization Costs ◽  
Diagnosis Of Dementia ◽  
Differential Costs

ABSTRACTBackground: The economic cost of dementia is high and can be predicted by cognitive and neuropsychiatric profiles. The differential costs of the various subtypes of dementia are unknown in Argentina, and this study therefore aimed to compare these costs.Methods: Patients with a diagnosis of dementia of Alzheimer-type (DAT), frontotemporal dementia (FTD) and vascular dementia (VaD), and their primary caregivers, were evaluated between 2002 and 2008.Results: 104 patients with dementia (DAT = 44, FTD = 34, VaD = 26) were screened and matched by age and educational level with 29 healthy subjects. Demographic variables showed no significant differences among dementia patients. The annual direct costs were US$4625 for DAT, US$4924 for FTD, and US$5112 for VaD (p > 0.05 between groups). In the post hoc analysis VaD showed higher hospitalization costs than DAT (p < 0.001). VaD exhibited lower medication costs than FTD (p < 0.001). DAT exhibited higher anti-dementia drug costs; FTD had higher psychotropic costs. In the multivariate analysis, depression, activities of daily living, and caregiver burden were correlated with direct costs (r2 = 0.76).Conclusions: The different dementia types have different costs. Overall, costs increased with the presence of behavioral symptoms, depression and functional impairment of activities of daily living.

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 Cognitive, functional and behavioral assessment: Alzheimer's disease

2011 ◽  
Vol 5 (3) ◽  
pp. 153-166 ◽  
Author(s):  
Márcia L.F. Chaves ◽  
Claudia C. Godinho ◽  
Claudia S. Porto ◽  
Leticia Mansur ◽  
Maria Teresa Carthery-Goulart ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Activities Of Daily Living ◽  
Daily Living ◽  
Neuropsychiatric Symptoms ◽  
Behavioral Assessment ◽  
Cognitive Domain ◽  
Sufficient Evidence ◽  
Level Of Evidence ◽  
Diagnosis Of Dementia

Abstract A review of the evidence on cognitive, functional and behavioral assessment for the diagnosis of dementia due to Alzheimer's disease (AD) is presented with revision and broadening of the recommendations on the use of tests and batteries in Brazil for the diagnosis of dementia due to AD. A systematic review of the literature (MEDLINE, LILACS and SCIELO database) was carried out by a panel of experts. Studies on the validation and/or adaptation of tests, scales and batteries for the Brazilian population were analyzed and classified according to level of evidence. There were sufficient data to recommend the IQCODE, DAFS-R, DAD, ADL-Q and Bayer scale for the evaluation of instrumental activities of daily living, and the Katz scale for the assessment of basic activities of daily living. For the evaluation of neuropsychiatric symptoms, the Neuropsychiatric Inventory (NPI) and the CAMDEX were found to be useful, as was the Cornell scale for depression in dementia. The Mini-Mental State Examination has clinical utility as a screening test, as do the multifunctional batteries (CAMCOG-R, ADAS-COG, CERAD and MDRS) for brief evaluations of several cognitive domains. There was sufficient evidence to recommend the CDR scale for clinical and severity assessment of dementia. Tests for Brazilian Portuguese are recommended by cognitive domain based on available data.

Communication Through Haptics During Human Collaborative Manipulation

2018 ◽  
Vol 15 (03) ◽  
pp. 1850003
Author(s):  
Maria Javaid
Keyword(s):  
Activities Of Daily Living ◽  
Daily Living ◽  
User Study ◽  
Decision Rules ◽  
Elderly Person ◽  
Machine Learning Techniques ◽  
Communication Interface ◽  
Assistive Robot ◽  
Haptic Communication ◽  
User Friendly

This paper describes research towards understanding haptic communication during planar object manipulation. In particular, a classification algorithm that classifies four stages of manipulation of a planar object is described. This research was performed as a part of a broader research project which has the goal of developing a user-friendly communication interface for an elderly-assistive robot. The manipulation of planar object was studied in detail as it happened very frequently during user study involving a caregiver helping an elderly person with the activities of daily living. For observing human haptic interaction, a sensory glove was developed. Further data collection was conducted in the laboratory setting and data was analyzed using various machine learning techniques. Based on this analysis, decision rules were derived that give insight into human-to-human collaborative manipulation of planar objects and successfully identified several classes of manipulative actions. The developed decision tree-based algorithm was then tested on the data of a user study that involved a caregiver assisting an elderly person in the activities of daily living. The developed algorithm also successfully classifies manipulation actions in real-time. This information is particularly interesting as it does not depend on any particular sensor and thus can be used by other researchers to further study haptic communication.

scholarly journals Edge Computing-Based Self-Organized Device Network for Awareness Activities of Daily Living in the Home

2020 ◽  
Vol 10 (7) ◽  
pp. 2475
Author(s):  
Seong Su Keum ◽  
Yu Jin Park ◽  
Soon Ju Kang
Keyword(s):  
Activities Of Daily Living ◽  
Real Time ◽  
Daily Living ◽  
Smart Home ◽  
Real Life ◽  
Edge Computing ◽  
Brain Diseases ◽  
Portable Devices ◽  
Brain Health ◽  
Self Organized

Activities of daily living (ADL) are important indicators for awareness of brain health in the elderly, and hospitals use ADL as a standard test for diagnosing chronic brain diseases such as dementia. However, since it is difficult to judge real-life ADL in hospitals, doctors typically predict ADL ability through interviews with patients or accompanying caregivers. Recently, many studies have attempted to diagnose accurate brain health by collecting and analyzing the real-life ADL of patients in their living environments. However, most of these were conducted by constructing and implementing expensive smart homes with the concept of centralized computing, and ADL data were collected from simple data about patients’ home appliance usage and the surrounding environment. Despite the high cost of building a smart home, the collected ADL data are inadequate for predicting accurate brain health. In this study, we developed and used three types of portable devices (wearable, tag, and stationary) that can be easily installed and operated in typical existing houses. We propose a self-organized device network structure based on edge computing that can perform user perception, location perception, and behavioral perception simultaneously. This approach enables us to collect user activity data, analyze ADL in real-time to determine if the user’s behavior was successful or abnormal, and record the physical ability of the user to move between fixed spaces. The characteristics of this proposed system enable us to distinguish patients from other family members and provide real-time notifications after a forgetful or mistaken action. We implemented devices that constitute the edge network of the smart home scenario and evaluated the performance of this system to verify its usefulness.

scholarly journals Aerobic Exercise Sustains Performance of Instrumental Activities of Daily Living in Early-Stage Alzheimer Disease

2019 ◽  
Vol 42 (3) ◽  
pp. E129-E134 ◽  
Author(s):  
Eric D. Vidoni ◽  
Jaime Perales ◽  
Mohammed Alshehri ◽  
Abdul-Mannaan Giles ◽  
Catherine F. Siengsukon ◽  
...  
Keyword(s):  
Alzheimer Disease ◽  
Activities Of Daily Living ◽  
Aerobic Exercise ◽  
Daily Living ◽  
Early Stage ◽  
Instrumental Activities
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