Data-driven Context Detection Leveraging Passively Sensed Nearables for Recognizing Complex Activities of Daily Living

2021 ◽  
Vol 2 (2) ◽  
pp. 1-22
Author(s):  
Ali Akbari ◽  
Reese Grimsley ◽  
Roozbeh Jafari
Keyword(s):  
Activities Of Daily Living ◽  
Daily Living ◽  
Data Driven ◽  
Context Detection ◽  
Complex Activities

Mining intricate temporal rules for recognizing complex activities of daily living under uncertainty

2016 ◽  
Vol 60 ◽  
pp. 1015-1028 ◽  
Author(s):  
Li Liu ◽  
Shu Wang ◽  
Yuxin Peng ◽  
Zigang Huang ◽  
Ming Liu ◽  
...  
Keyword(s):  
Activities Of Daily Living ◽  
Daily Living ◽  
Complex Activities

Assessment and therapy of complex activities of daily living in apraxia

2001 ◽  
Vol 11 (2) ◽  
pp. 147-169 ◽  
Author(s):  
Georg Goldenberg ◽  
Maike Daumüller ◽  
Sonja Hagmann
Keyword(s):  
Activities Of Daily Living ◽  
Daily Living ◽  
Complex Activities

scholarly journals Complex activities of daily living in mild cognitive impairment: conceptual and diagnostic issues

2006 ◽  
Vol 35 (3) ◽  
pp. 240-245 ◽  
Author(s):  
Robert Perneczky ◽  
Corina Pohl ◽  
Christian Sorg ◽  
Julia Hartmann ◽  
Katja Komossa ◽  
...  
Keyword(s):  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Activities Of Daily Living ◽  
Daily Living ◽  
Complex Activities

TD-P-019: PRE-CLINICAL (ALZHEIMER'S) DIAGNOSIS (PCD) BY TRACKING MICRO ERRORS DURING COMPLEX ACTIVITIES OF DAILY LIVING: A DIGITAL BIOMARKER

2006 ◽  
Vol 14 (7S_Part_3) ◽  
pp. P194-P195
Author(s):  
Ioannis Tarnanas ◽  
Zachary Rapp
Keyword(s):  
Activities Of Daily Living ◽  
Daily Living ◽  
Complex Activities

scholarly journals The simulation of action disorganisation in complex activities of daily living

2005 ◽  
Vol 22 (8) ◽  
pp. 959-1004 ◽  
Author(s):  
Richard P. Cooper ◽  
Myrna F. Schwartz ◽  
Peter Yule ◽  
Tim Shallice
Keyword(s):  
Activities Of Daily Living ◽  
Daily Living ◽  
Complex Activities

scholarly journals Hierarchical Decline of the Initiative and Performance of Complex Activities of Daily Living in Dementia

2017 ◽  
Vol 30 (2) ◽  
pp. 96-103 ◽  
Author(s):  
Clarissa M. Giebel ◽  
Caroline Sutcliffe ◽  
David Challis
Keyword(s):  
Activities Of Daily Living ◽  
Social Life ◽  
Daily Living ◽  
Everyday Functioning ◽  
Performance Deficits ◽  
Targeted Interventions ◽  
People With Dementia ◽  
And Performance ◽  
Severity Of Dementia ◽  
Complex Activities

Objectives: While basic activities of daily living hierarchically decline in dementia, little is known about the decline of individual instrumental activities of daily living (IADLs). The objective of this study was to assess initiative and performance deficits in IADLs in dementia. Methods: A total of 581 carers completed the revised Interview for Deterioration in Daily Living Activities in Dementia 2 to rate their relative’s everyday functioning. Results: Initiating and performing IADLs deteriorated hierarchically, while people with dementia were consistently most impaired in initiating using the computer and managing finances. Initiating preparing a cold or hot meal and managing finances were more impaired than their performance, whereas performing maintaining an active social life for example were more impaired than their initiative. Conclusion: Findings can help identify the severity of dementia by understanding deficits in initiative and performance. This study has implications for the development of targeted interventions depending on the stage of dementia.

P2-236: FUNCTIONAL PERFORMANCE IN THE COMPLEX ACTIVITIES OF DAILY LIVING: CHANGE AND LOSS PROFILE IN A HEALTHY AGED POPULATION

2014 ◽  
Vol 10 ◽  
pp. P562-P562
Author(s):  
Luisa Edith Labos ◽  
Sofia Trojanowski ◽  
Miriam Del Rio ◽  
Alejandro Renato
Keyword(s):  
Activities Of Daily Living ◽  
Daily Living ◽  
Functional Performance ◽  
Aged Population ◽  
Complex Activities

[P2-462]: DIAGNOSTIC PERFORMANCE OF THE EXTENDED FUNCTIONAL STUDY (EFE) FOR COMPLEX ACTIVITIES OF DAILY LIVING IN PATIENTS WITH MILD COGNITIVE IMPAIRMENT (MCI) AND CONTROLS

2017 ◽  
Vol 13 (7S_Part_16) ◽  
pp. P817-P817
Author(s):  
Maria Elena Guajardo ◽  
Maria Elvira Söderlund ◽  
Vanina Pagotto ◽  
Daniel Bernardo Seinhart ◽  
Luis Camera ◽  
...  
Keyword(s):  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Activities Of Daily Living ◽  
Daily Living ◽  
Diagnostic Performance ◽  
Functional Study ◽  
Complex Activities

scholarly journals Data-Driven Knowledge-Based System for Self-Measuring Activities of Daily Living in IoT-Based Test

2020 ◽  
Vol 10 (14) ◽  
pp. 4972
Author(s):  
Youngsul Shin ◽  
Yu Jin Park ◽  
Soon Ju Kang
Keyword(s):  
Activities Of Daily Living ◽  
Daily Living ◽  
Data Driven ◽  
Knowledge Based System ◽  
Aged People ◽  
Knowledge Based ◽  
Dementia Patients ◽  
Initial Stage ◽  
The Given ◽  
The Internet Of Things

This paper proposes a data-driven knowledge-based system with which aged people can measure the degree of activities of daily living (ADL) by themselves. The proposed system, called E-coach for ADL Test (EAT), provides participants with self-measurement procedures, using e-coaching, which is a guidance mechanism to lead the participants from an initial stage to a target goal. The EAT traces the behavior of the participants to gather ADL data that tell how well they perform the given e-coaching. Driven by the Internet of Things data, the knowledge-based inference of the EAT carries out the e-coaching mechanism that figures out what state the self-measurement procedures stay on and what guidance is necessary for the next state. The EAT ensures that all the procedures for ADL measurement are executed automatically without any help from medical professionals. The experiment described in this paper demonstrates that the EAT distinguishes between dementia patients and normal people. The measurement report assists medical doctors in the diagnosis of certain medical conditions that these people may have.

scholarly journals Neural Network Ensembles for Sensor-Based Human Activity Recognition Within Smart Environments

Sensors ◽  
2019 ◽  
Vol 20 (1) ◽  
pp. 216 ◽  
Author(s):  
Naomi Irvine ◽  
Chris Nugent ◽  
Shuai Zhang ◽  
Hui Wang ◽  
Wing W. Y. NG
Keyword(s):  
Neural Network ◽  
Conflict Resolution ◽  
Activities Of Daily Living ◽  
Activity Recognition ◽  
Human Activity ◽  
Daily Living ◽  
Human Activity Recognition ◽  
Data Driven ◽  
Smart Environments ◽  
Class Level

In this paper, we focus on data-driven approaches to human activity recognition (HAR). Data-driven approaches rely on good quality data during training, however, a shortage of high quality, large-scale, and accurately annotated HAR datasets exists for recognizing activities of daily living (ADLs) within smart environments. The contributions of this paper involve improving the quality of an openly available HAR dataset for the purpose of data-driven HAR and proposing a new ensemble of neural networks as a data-driven HAR classifier. Specifically, we propose a homogeneous ensemble neural network approach for the purpose of recognizing activities of daily living within a smart home setting. Four base models were generated and integrated using a support function fusion method which involved computing an output decision score for each base classifier. The contribution of this work also involved exploring several approaches to resolving conflicts between the base models. Experimental results demonstrated that distributing data at a class level greatly reduces the number of conflicts that occur between the base models, leading to an increased performance prior to the application of conflict resolution techniques. Overall, the best HAR performance of 80.39% was achieved through distributing data at a class level in conjunction with a conflict resolution approach, which involved calculating the difference between the highest and second highest predictions per conflicting model and awarding the final decision to the model with the highest differential value.

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