scholarly journals Self-Organizing IoT Device-Based Smart Diagnosing Assistance System for Activities of Daily Living

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 785
Author(s):  
Yu Jin Park ◽  
Seol Young Jung ◽  
Tae Yong Son ◽  
Soon Ju Kang
Keyword(s):  
Daily Living ◽  
Flame Temperature ◽  
Real Life ◽  
Wearable Devices ◽  
Activity Of Daily Living ◽  
Environmental Sensors ◽  
Iot Platform ◽  
Sensor Devices ◽  
Hospital Patients ◽  
Performance Ability

Activity of daily living (ADL) is a criterion for evaluating the performance ability of daily life by recognizing various activity events occurring in real life. However, most of the data necessary for ADL evaluation are collected only through observation and questionnaire by the patient or the patient’s caregiver. Recently, Internet of Things (IoT) device studies using various environmental sensors are being used for ADL collection and analysis. In this paper, we propose an IoT Device Platform for ADL capability measurement. Wearable devices and stationary devices recognize activity events in real environments and perform user identification through various sensors. The user’s ADL data are sent to the network hub for analysis. The proposed IoT platform devices support many sensor devices such as acceleration, flame, temperature, and humidity in order to recognize various activities in real life. In addition, in this paper, using the implemented platform, ADL measurement test was performed on hospital patients. Through this test, the accuracy and reliability of the platform are analyzed.

A Deep Learning Approach for Recognizing Activity of Daily Living (ADL) for Senior Care: Exploiting Interaction Dependency and Temporal Patterns

MIS Quarterly ◽  
2021 ◽  
Vol 45 (2) ◽  
pp. 859-896
Author(s):  
Hongyi Zhu ◽  
Sagar Samtani ◽  
Randall Brown ◽  
Hsinchun Chen
Keyword(s):  
Deep Learning ◽  
Daily Living ◽  
Design Science ◽  
Real Life ◽  
Object Motion ◽  
Sensor Data ◽  
Support Vector ◽  
Activity Of Daily Living ◽  
Motion Sensors ◽  
Interaction Kernel

Ensuring the health and safety of senior citizens who live alone is a growing societal concern. The Activity of Daily Living (ADL) approach is a common means to monitor disease progression and the ability of these individuals to care for themselves. However, the prevailing sensor-based ADL monitoring systems primarily rely on wearable motion sensors, capture insufficient information for accurate ADL recognition, and do not provide a comprehensive understanding of ADLs at different granularities. Current healthcare IS and mobile analytics research focuses on studying the system, device, and provided services, and is in need of an end-to-end solution to comprehensively recognize ADLs based on mobile sensor data. This study adopts the design science paradigm and employs advanced deep learning algorithms to develop a novel hierarchical, multiphase ADL recognition framework to model ADLs at different granularities. We propose a novel 2D interaction kernel for convolutional neural networks to leverage interactions between human and object motion sensors. We rigorously evaluate each proposed module and the entire framework against state-of-the-art benchmarks (e.g., support vector machines, DeepConvLSTM, hidden Markov models, and topic-modeling-based ADLR) on two real-life motion sensor datasets that consist of ADLs at varying granularities: Opportunity and INTER. Results and a case study demonstrate that our framework can recognize ADLs at different levels more accurately. We discuss how stakeholders can further benefit from our proposed framework. Beyond demonstrating practical utility, we discuss contributions to the IS knowledge base for future design science-based cybersecurity, healthcare, and mobile analytics applications.

scholarly journals Preserving the Activities of Daily Living Independence in Late Life

2020 ◽  
Vol 4 (Supplement_1) ◽  
pp. 228-228
Author(s):  
Pao-feng Tsai ◽  
Thomas Jakobs ◽  
Reid Landes
Keyword(s):  
Training Program ◽  
Daily Living ◽  
Cost Effective ◽  
Computer Application ◽  
Maximum Effect ◽  
Activity Of Daily Living ◽  
Nursing Assistant ◽  
Future Studies ◽  
Certified Nursing Assistant

Abstract Levels of Assistance (LoA) is an effective caregiving intervention for maintaining activity of daily living (ADL) independence. It is a structured, almost prescriptive, approach to encourage completing ADLs as independently as an elder’s capabilities permit. With appropriate prompts and assistance during dressing, elders can overcome disability, express retained competencies, and experience success. Simultaneously, caregivers learn to view their functions as maintaining the quality of life of able elders, and they receive reinforcement from elders who are more confident and happier. This study is a continuation of a previous project that created and tested a computer application training program for LoA in nursing homes. We refined the app to include grooming LoA and tested on 10 certified nursing assistant (CNA)/resident dyads at a local nursing home. The pilot results showed, although we did not see consistent improvement in CNA’s dressing LoA, we achieved 10% to 30% improvement in grooming LoA. This indicates that the dressing assistance training is able to transfer to grooming LoA. With only an average of one-hour app training, this improvement is cost effective as compared to training provided by care professionals. Future studies should consider incorporating a culture change strategy to improve CNAs’ intention for assisting elders. In addition, the training program should be offered in the initial hire to achieve maximum effect.

scholarly journals Effects of intralesional platelet-rich plasma in the patients with lateral epicondylitis of elbow

2019 ◽  
Vol 12 (3) ◽  
pp. 138-141
Author(s):  
Mohammad Tariqul Islam ◽  
M. A. Shakoor ◽  
Afsana Mahjabin ◽  
Md. Ali Emran
Keyword(s):  
Visual Analogue Scale ◽  
Daily Living ◽  
Analogue Scale ◽  
Tennis Elbow ◽  
Platelet Rich Plasma ◽  
Lateral Epicondylitis ◽  
Activity Of Daily Living ◽  
Evaluation Tool ◽  
Plasma Injection ◽  
The Difference

Lateral epicondylitis (tennis elbow) is a major cause of musculoskeletal pain involving common extensor origin of the forearm. This study was done to determine the effects of platelet-rich plasma on 15 patients with lateral epicondylitis. Selected patients were given intralesional platelet-rich plasma injection, activity of daily living instructions and paracetamol. Patients were assessed every 14 days interval by visual analogue scale, and the patient rated tennis elbow evaluation. Treatment response according to visual analogue scale and patient rated tennis elbow evaluation tool, the difference of improvement was found in respect to time, from pretreatment W1 (just before 1st Intervention) score to W11 score in every alternate week (p<0.005). This indicates that intralesional platelet-rich plasma is effective in the patients with lateral epicondylitis of elbow.

scholarly journals Do Elderly People at More Severe Activity of Daily Living Limitation Stages Fall More?

2012 ◽  
Vol 91 (7) ◽  
pp. 601-610 ◽  
Author(s):  
John T. Henry-Sánchez ◽  
Jibby E. Kurichi ◽  
Dawei Xie ◽  
Qiang Pan ◽  
Margaret G. Stineman
Keyword(s):  
Elderly People ◽  
Daily Living ◽  
Activity Of Daily Living
Sign in / Sign up

Export Citation Format

Share Document