Binary Sensors-Based Privacy-Preserved Activity Recognition of Elderly Living Alone Using an RNN

The recent growth of the elderly population has led to the requirement for constant home monitoring as solitary living becomes popular. This protects older people who live alone from unwanted instances such as falling or deterioration caused by some diseases. However, although wearable devices and c...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2021-08, Vol.21 (16), p.5371
Main Authors: Tan, Tan-Hsu, Badarch, Luubaatar, Zeng, Wei-Xiang, Gochoo, Munkhjargal, Alnajjar, Fady S., Hsieh, Jun-Wei
Format: Article
Language:English
Subjects:
Activity recognition
Adaptive systems
begin time-stamp
Classification
convolutional neural network
Datasets
device-free
Human motion
Internet of Things
long short-term memory
Motion sensors
Neural networks
Older people
previous activity
Privacy
privacy-preserving
Recurrent neural networks
Sensor arrays
Sensors
Wearable technology
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Summary:The recent growth of the elderly population has led to the requirement for constant home monitoring as solitary living becomes popular. This protects older people who live alone from unwanted instances such as falling or deterioration caused by some diseases. However, although wearable devices and camera-based systems can provide relatively precise information about human motion, they invade the privacy of the elderly. One way to detect the abnormal behavior of elderly residents under the condition of maintaining privacy is to equip the resident’s house with an Internet of Things system based on a non-invasive binary motion sensor array. We propose to concatenate external features (previous activity and begin time-stamp) along with extracted features with a bi-directional long short-term memory (Bi-LSTM) neural network to recognize the activities of daily living with a higher accuracy. The concatenated features are classified by a fully connected neural network (FCNN). The proposed model was evaluated on open dataset from the Center for Advanced Studies in Adaptive Systems (CASAS) at Washington State University. The experimental results show that the proposed method outperformed state-of-the-art models with a margin of more than 6.25% of the F1 score on the same dataset.
ISSN:1424-8220
1424-8220
DOI:10.3390/s21165371