scholarly journals Implementation of IoT, Wearable Devices, Google Assistant and Google Cloud Platform for Elderly Home Care System

2021 ◽  
Author(s):  
Jung-Tang Huang ◽  
Li-Ying Chang ◽  
Hsin-Chang Lin
Keyword(s):  
Home Care ◽  
Wearable Devices ◽  
Cloud Platform ◽  
Elderly Home ◽  
Care System

scholarly journals Design of Home Care System For Rural Elderly Based on Artificial Intelligence

2021 ◽  
Vol 1757 (1) ◽  
pp. 012057
Author(s):  
Liang Liang Wang ◽  
Lian Qin Jia ◽  
Feng Qin Chu ◽  
Ming Xia Li
Keyword(s):  
Artificial Intelligence ◽  
Home Care ◽  
Rural Elderly ◽  
Care System

scholarly journals Allocation of Rehabilitation Services for Older Adults in the Ontario Home Care System

2016 ◽  
Vol 68 (4) ◽  
pp. 346-354 ◽  
Author(s):  
Joshua J. Armstrong ◽  
Joanie Sims-Gould ◽  
Paul Stolee
Keyword(s):  
Older Adults ◽  
Home Care ◽  
Rehabilitation Services ◽  
Care System

Anomaly detection for elderly home care

2020 ◽  
Vol 16 (4) ◽  
pp. 418
Author(s):  
N.A. Kurnianingsih ◽  
Lukito Edi Nugroho ◽  
N.A. Widyawan ◽  
Lutfan Lazuardi ◽  
Anton Satria Prabuwono ◽  
...  
Keyword(s):  
Anomaly Detection ◽  
Home Care ◽  
Elderly Home

scholarly journals Towards an Accessible Use of a Brain-Computer Interfaces-Based Home Care System through a Smartphone

2020 ◽  
Vol 2020 ◽  
pp. 1-17
Author(s):  
Koun-Tem Sun ◽  
Kai-Lung Hsieh ◽  
Syuan-Rong Syu
Keyword(s):  
Home Care ◽  
Event Related Potentials ◽  
End Users ◽  
Disabled People ◽  
Eeg Signals ◽  
Computer Interfaces ◽  
Related Potentials ◽  
The One ◽  
The Brain ◽  
Care System

This study proposes a home care system (HCS) based on a brain-computer interface (BCI) with a smartphone. The HCS provides daily help to motor-disabled people when a caregiver is not present. The aim of the study is two-fold: (1) to develop a BCI-based home care system to help end-users control their household appliances, and (2) to assess whether the architecture of the HCS is easy for motor-disabled people to use. A motion-strip is used to evoke event-related potentials (ERPs) in the brain of the user, and the system immediately processes these potentials to decode the user’s intentions. The system, then, translates these intentions into application commands and sends them via Bluetooth to the user’s smartphone to make an emergency call or to execute the corresponding app to emit an infrared (IR) signal to control a household appliance. Fifteen healthy and seven motor-disabled subjects (including the one with ALS) participated in the experiment. The average online accuracy was 81.8% and 78.1%, respectively. Using component N2P3 to discriminate targets from nontargets can increase the efficiency of the system. Results showed that the system allows end-users to use smartphone apps as long as they are using their brain waves. More important, only one electrode O1 is required to measure EEG signals, giving the system good practical usability. The HCS can, thus, improve the autonomy and self-reliance of its end-users.

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