A hybrid approach of knowledge-driven and data-driven reasoning for activity recognition in smart homes

Sukor, Abdul Syafiq Abdull; Zakaria, Ammar; Rahim, Norasmadi Abdul; Kamarudin, Latifah Munirah; Setchi, Rossi; Nishizaki, Hiromitsu

doi:10.3233/jifs-169976

Cited by 34 publications

(19 citation statements)

References 34 publications

(41 reference statements)

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“…A generic hybrid approach to recognize the composite activities occurring in a sequential or parallel order, such as preparing dinner or dish washing, integrates ontology, temporal knowledge and the inference rules (Okeyo et al 2014). To overcome the limitation of insufficient data for a knowledge driven approach, the knowledge driven and the data driven approaches are combined, while the domain knowledge is also used to improve the learning (Sukor et al 2019).…”

Section: Related Workmentioning

confidence: 99%

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Activity recognition in a smart home using local feature weighting and variants of nearest-neighbors classifiers

Fahad

Tahir

2020

J Ambient Intell Human Comput

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Recognition of activities, such as preparing meal or watching TV, performed by a smart home resident, can promote the independent living of elderly in a safe and comfortable environment of their own homes, for an extended period of time. Different activities performed at the same location have commonalities resulting in less inter-class variations; while the same activity performed multiple times, or by multiple residents, varies in its execution resulting in high intra-class variations. We propose a Local Feature Weighting approach (LFW) that assigns weights based on both inter-class and intra-class importance of a feature in an activity. Multiple sensors are deployed at different locations in a smart home to gather information. We exploit the obtained information, such as frequency and duration of activation of sensors, and the total sensors in an activity for feature weighting. The weights for the same features vary among activities, since a feature may have more importance for one activity but less for the other. For the classification, we exploit the two variants of K-Nearest Neighbors (KNN): Evidence Theoretic KNN (ETKNN) and Fuzzy KNN (FKNN). The evaluation of the proposed approach on three datasets, from CASAS smart home project, demonstrates its ability in the correct recognition of activities compared to the existing approaches.

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Section: Related Workmentioning

confidence: 99%

“…To overcome the limitation of insufficient data for a knowledge driven approach, the knowledge driven and the data driven approaches are combined, while the domain knowledge is also used to improve the learning (Sukor et al. 2019 ).…”

Section: Related Workmentioning

confidence: 99%

Activity recognition in a smart home using local feature weighting and variants of nearest-neighbors classifiers

Fahad

Tahir

2020

J Ambient Intell Human Comput

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“…To overcome the awareness of human-computer interaction, the potential features in the on-body device must be learned. HAR using wearable devices data is at the core of intelligent assistive technology, due to its proliferative applications in smart homes [1], intelligent traffic control [2], medical/health assistance [3,4], skill based check [5], even in the security field [6]. Particularly for the elderly people who are in remote and need to continuous monitor, HAR can greatly increase their safety [7].…”

Section: Introductionmentioning

confidence: 99%

Asymmetric Residual Neural Network for Accurate Human Activity Recognition

et al. 2019

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Human Activity Recognition (HAR) using deep neural network has become a hot topic in human-computer interaction. Machine can effectively identify human naturalistic activities by learning from a large collection of sensor data. Activity recognition is not only an interesting research problem, but also has many real-world practical applications. Based on the success of residual networks in achieving a high level of aesthetic representation of the automatic learning, we propose a novel Asymmetric Residual Network, named ARN. ARN is implemented using two identical path frameworks consisting of (1) a short time window, which is used to capture spatial features, and (2) a long time window, which is used to capture fine temporal features. The long time window path can be made very lightweight by reducing its channel capacity, yet still being able to learn useful temporal representations for activity recognition. In this paper, we mainly focus on proposing a new model to improve the accuracy of HAR. In order to demonstrate the effectiveness of ARN model, we carried out extensive experiments on benchmark datasets (i.e., OPPORTUNITY, UniMiB-SHAR) and compared with some conventional and state-of-the-art learning-based methods. Then, we discuss the influence of networks parameters on performance to provide insights about its optimization. Results from our experiments show that ARN is effective in recognizing human activities via wearable datasets.

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“…HAI is the process of recognizing the actions of a person by using the video sequences which contain a complete action execution and retrieving the videos of interest. It can be used in different applications like video surveillance, human-machine interface, smart home [1], healthcare systems [2], etc. In day-by-day, unrealistic numbers of videos are created because of the surveillance systems, movies, YouTube, etc.…”

Section: Introductionmentioning

confidence: 99%

Body Joints and Trajectory Guided 3D Deep Convolutional Descriptors for Human Activity Identification

Srilakshmi*,

Radha

2019

IJITEE

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Human Activity Identification (HAI) in videos is one of the trendiest research fields in the computer visualization. Among various HAI techniques, Joints-pooled 3D-Deep convolutional Descriptors (JDD) have achieved effective performance by learning the body joint and capturing the spatiotemporal characteristics concurrently. However, the time consumption for estimating the locale of body joints by using large-scale dataset and computational cost of skeleton estimation algorithm were high. The recognition accuracy using traditional approaches need to be improved by considering both body joints and trajectory points together. Therefore, the key goal of this work is to improve the recognition accuracy using an optical flow integrated with a two-stream bilinear model, namely Joints and Trajectory-pooled 3D-Deep convolutional Descriptors (JTDD). In this model, an optical flow/trajectory point between video frames is also extracted at the body joint positions as input to the proposed JTDD. For this reason, two-streams of Convolutional 3D network (C3D) multiplied with the bilinear product is used for extracting the features, generating the joint descriptors for video sequences and capturing the spatiotemporal features. Then, the whole network is trained end-to-end based on the two-stream bilinear C3D model to obtain the video descriptors. Further, these video descriptors are classified by linear Support Vector Machine (SVM) to recognize human activities. Based on both body joints and trajectory points, action recognition is achieved efficiently. Finally, the recognition accuracy of the JTDD model and JDD model are compared.

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A hybrid approach of knowledge-driven and data-driven reasoning for activity recognition in smart homes

Cited by 34 publications

References 34 publications

Activity recognition in a smart home using local feature weighting and variants of nearest-neighbors classifiers

Activity recognition in a smart home using local feature weighting and variants of nearest-neighbors classifiers

Asymmetric Residual Neural Network for Accurate Human Activity Recognition

Body Joints and Trajectory Guided 3D Deep Convolutional Descriptors for Human Activity Identification

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