Abstract
Human activity recognition has become essential to a wide range of applications, such as smart home monitoring, health-care, surveillance. However, it is challenging to deliver a sufficiently robust human activity recognition system from raw sensor data with noise in a smart environment setting. Moreover, imbalanced human activity datasets with less frequent activities create extra challenges for accurate activity recognition. Deep learning algorithms have achieved promising results on balanced datasets, but their performance on imbalanced datasets without explicit algorithm design cannot be promised. Therefore, we aim to realise an activity recognition system using multi-modal sensors to address the issue of class imbalance in deep learning and improve recognition accuracy. This paper proposes a joint diverse temporal learning framework using Long Short Term Memory and one-dimensional Convolutional Neural Network models to improve human activity recognition, especially for less represented activities. We extensively evaluate the proposed method for Activities of Daily Living recognition using binary sensors dataset. A comparative study on five smart home datasets demonstrate that our proposed approach outperforms the existing individual temporal models and their hybridization. Furthermore, this is particularly the case for minority classes in addition to reasonable improvement on the majority classes of human activities.
Highlights
Human activity recognition (HAR) is the active research field for monitoring human behaviours, which stimulates various applications in fields healthcare monitoring [1], security monitoring [2], and resident situation assessment [3] and behaviour pattern recognition in pro-active home care [4].In the home care scenario, HAR is a key component of smart home technology that makes independent living as a viable solution for elderly people, and enhances and maintains the quality of life and care [5,6]
Regarding the Kasteren datasets A, B, and C, the F-scores of the proposed joint learning of temporal models compared with the long short-term memory (LSTM), 1D CNN, and hybrid model are shown in Tables 5 and 6
The results show that the joint learning of temporal models outperforms the individual temporal and hybrid learners by more than 4% in total from all the datasets
Summary
Human activity recognition (HAR) is the active research field for monitoring human behaviours, which stimulates various applications in fields healthcare monitoring [1], security monitoring [2], and resident situation assessment [3] and behaviour pattern recognition in pro-active home care [4].In the home care scenario, HAR is a key component of smart home technology that makes independent living as a viable solution for elderly people, and enhances and maintains the quality of life and care [5,6]. Smart homes with human activities monitoring have been used for transparent surrounding context representation, which have enabled various health technology applications, such as disease progress and recovery tracking, or anomaly detection with a typical example of fall detection. The recent advancement of machine learning has significantly progressed HAR systems and achieved performance improvements in many aspects of their applications, such as elderly-care alert systems and assistance in emergencies [7]. Long-term human activity monitoring yields feasibility to determine and assess the wellness. Activities, such as sleeping, eating, and showering in smart homes, are key events to enable the tracking and assess of the functional health status of elderly people [8].
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