Abstract
In this paper, we propose a unified deep learning model for monitoring elderly in execution of daily life activities such as eating, sleeping or taking medication. The proposed approach consists of three stages which are activity recognition, anomaly detection and next activity prediction. Such a system can provide useful information for the elderly, caregivers and medical teams to identify activities and generate preventive and corrective measures. In literature, these stages are discussed separately, however, in our approach, we make use of each stage to progress into the next stage. At first, activity recognition based on different extracted features is performed using a deep neural network (DNN), then an overcomplete-deep autoencoder (OCD-AE) is employed to separate the normal from anomalous activities. Finally, a cleaned sequence of consecutive activities is constructed and used by a long short-term memory (LSTM) algorithm to predict the next activity. Since the last two stages depend on the activity recognition stage, we propose to increase its accuracy by exploiting different extracted features. The performance of the proposed unified approach has been evaluated on real smart home datasets to demonstrate its ability to recognize activities, detect anomalies and predict the next activity.
Highlights
The care of elderly people who are unable to effectively develop activities of daily living (ADL) requires a lot of attention and dedication, as both their lifestyle and health are affected
We propose to increase the accuracy of activity recognition using different extracted features from pre-labelled activity instances, train and test the ability of the deep neural network (DNN) model to classify a given activity, and secondly, a proposed overcomplete autoencoder (OCD-AE) is used to identify anomalous instances within each activity class
The sequence of activities are first converted into letters, both input and output of long short-term memory (LSTM) network are converted into one-hot encoded form, where each symbol in the sequence is represented by a vector of bits with a length equal to the number of symbols in a sequence
Summary
The care of elderly people who are unable to effectively develop activities of daily living (ADL) requires a lot of attention and dedication, as both their lifestyle and health are affected. Sensor-based ambient systems in smart homes can be used to recognise various behaviours and complex activities [7] by monitoring the interaction between objects and inhabitants This process can be done using machine learning (ML) techniques which can effectively classify ADLs performed by people. Since it is difficult to obtain real observed data, boxplot analysis (such as median, maximum, minimum and outliers) can be used to have approximate information about activities such duration, number of active sensors and so on This technique has been widely used in previous works such as [21]. The activities in a sequence are converted to capital letters and used by LZ78 and ALZ algorithms, while for LSTM it is converted into one-hot encoding
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