Wearable Sensors for Activity Analysis using SMO-based Random Forest over Smart home and Sports Datasets

Tahir, Sheikh Badar ud din; Jalal, Ahmad; Batool, Mouazma

doi:10.1109/icacs47775.2020.9055944

Cited by 52 publications

(21 citation statements)

References 33 publications

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“…Regarding activity recognition based on IMUs, research has addressed scenarios that resemble devices that are expected to be actually worn by the users, such as smartphones and smartwatches [ 87 ]. Feature extraction methods include combinations between sequential minimal optimization (SMO) and Random Forest [ 25 ], statistical features feeding genetic algorithms [ 88 ], and Markov models [ 89 ]. DL architectures, such as Deep Neural Networks (DNN), Convolutional Neural Networks (CNN), autoencoders, Restricted Boltzmann Machines (RBM), and Recurrent Neural Networks (RNN) have also been successfully applied to this modality [ 33 ].…”

Section: Human Activity Recognitionmentioning

confidence: 99%

“…Besides, one modality of data can perform better than another in certain conditions. Ambient sensors may be quite informative on some well-defined scenarios in a smart home [ 24 ], while wearable sensors can be more suitable for actions that rely on limb motions [ 25 ]. Therefore, most recently, multimodal approaches for activity recognition have been investigated [ 20 ] as more robust alternatives when compared to single-modality approaches.…”

Section: Introductionmentioning

confidence: 99%

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Activity Recognition for Ambient Assisted Living with Videos, Inertial Units and Ambient Sensors

Ranieri

MacLeod

Dragone

et al. 2021

Sensors

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Worldwide demographic projections point to a progressively older population. This fact has fostered research on Ambient Assisted Living, which includes developments on smart homes and social robots. To endow such environments with truly autonomous behaviours, algorithms must extract semantically meaningful information from whichever sensor data is available. Human activity recognition is one of the most active fields of research within this context. Proposed approaches vary according to the input modality and the environments considered. Different from others, this paper addresses the problem of recognising heterogeneous activities of daily living centred in home environments considering simultaneously data from videos, wearable IMUs and ambient sensors. For this, two contributions are presented. The first is the creation of the Heriot-Watt University/University of Sao Paulo (HWU-USP) activities dataset, which was recorded at the Robotic Assisted Living Testbed at Heriot-Watt University. This dataset differs from other multimodal datasets due to the fact that it consists of daily living activities with either periodical patterns or long-term dependencies, which are captured in a very rich and heterogeneous sensing environment. In particular, this dataset combines data from a humanoid robot’s RGBD (RGB + depth) camera, with inertial sensors from wearable devices, and ambient sensors from a smart home. The second contribution is the proposal of a Deep Learning (DL) framework, which provides multimodal activity recognition based on videos, inertial sensors and ambient sensors from the smart home, on their own or fused to each other. The classification DL framework has also validated on our dataset and on the University of Texas at Dallas Multimodal Human Activities Dataset (UTD-MHAD), a widely used benchmark for activity recognition based on videos and inertial sensors, providing a comparative analysis between the results on the two datasets considered. Results demonstrate that the introduction of data from ambient sensors expressively improved the accuracy results.

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Section: Human Activity Recognitionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Activity Recognition for Ambient Assisted Living with Videos, Inertial Units and Ambient Sensors

Ranieri

MacLeod

Dragone

et al. 2021

Sensors

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“…Sustainability 2020, 12, x FOR PEER REVIEW 11 of 24 sin(α + β) = sin α cos β + sin β cos α (13) After extracting the sine features, we mapped them in a vector and a concatenate sine feature vector with the main feature vector. Figure 9 shows the results of sine features over two different classes:…”

Section: Key Body Points: Angle Point Featurementioning

confidence: 99%

“…Using safe city data, we can detect anomalous events in daily human life-log environments. When an anomalous event occurs, the system generates an alarm and activates nearby emergency service institutions [10][11][12][13]. These projects help save lives, time, manpower, and cost, but it remains a challenging domain for researchers.…”

Section: Introductionmentioning

confidence: 99%

Human Posture Estimation and Sustainable Events Classification via Pseudo-2D Stick Model and K-ary Tree Hashing

2020

Self Cite

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This paper suggests that human pose estimation (HPE) and sustainable event classification (SEC) require an advanced human skeleton and context-aware features extraction approach along with machine learning classification methods to recognize daily events precisely. Over the last few decades, researchers have found new mechanisms to make HPE and SEC applicable in daily human life-log events such as sports, surveillance systems, human monitoring systems, and in the education sector. In this research article, we propose a novel HPE and SEC system for which we designed a pseudo-2D stick model. To extract full-body human silhouette features, we proposed various features such as energy, sine, distinct body parts movements, and a 3D Cartesian view of smoothing gradients features. Features extracted to represent human key posture points include rich 2D appearance, angular point, and multi-point autocorrelation. After the extraction of key points, we applied a hierarchical classification and optimization model via ray optimization and a K-ary tree hashing algorithm over a UCF50 dataset, an hmdb51 dataset, and an Olympic sports dataset. Human body key points detection accuracy for the UCF50 dataset was 80.9%, for the hmdb51 dataset it was 82.1%, and for the Olympic sports dataset it was 81.7%. Event classification for the UCF50 dataset was 90.48%, for the hmdb51 dataset it was 89.21%, and for the Olympic sports dataset it was 90.83%. These results indicate better performance for our approach compared to other state-of-the-art methods.

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“…In another work, to recognize online human action and activity, Jalal et al [27] performed multi-features fusion along with skeleton joints and shape features of humans. For feature extractions in activity recognition, Tahir et al [28] applied 1-D LBP and 1-D Hadamard wavelet transform along with Random Forest. On depth video sequences, Kamal et al [29] utilized modified HMM to complete another fusion process of temporal joint features and spatial depth shape features.…”

Section: Introductionmentioning

confidence: 99%

Gradient local auto-correlation features for depth human action recognition

Bulbul¹,

Ali

2021

SN Appl. Sci.

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Human action classification is a dynamic research topic in computer vision and has applications in video surveillance, human–computer interaction, and sign-language recognition. This paper aims to present an approach for the categorization of depth video oriented human action. In the approach, the enhanced motion and static history images are computed and a set of 2D auto-correlation gradient feature vectors is obtained from them to describe an action. Kernel-based Extreme Learning Machine is used with the extracted features to distinguish the diverse action types promisingly. The proposed approach is thoroughly assessed for the action datasets namely MSRAction3D, DHA, and UTD-MHAD. The approach achieves an accuracy of 97.44% for MSRAction3D, 99.13% for DHA, and 88.37% for UTD-MHAD. The experimental results and analysis demonstrate that the classification performance of the proposed method is considerable and surpasses the state-of-the-art human action classification methods. Besides, from the complexity analysis of the approach, it is turn out that our method is consistent for the real-time operation with low computational complexity.

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Wearable Sensors for Activity Analysis using SMO-based Random Forest over Smart home and Sports Datasets

Cited by 52 publications

References 33 publications

Activity Recognition for Ambient Assisted Living with Videos, Inertial Units and Ambient Sensors

Activity Recognition for Ambient Assisted Living with Videos, Inertial Units and Ambient Sensors

Human Posture Estimation and Sustainable Events Classification via Pseudo-2D Stick Model and K-ary Tree Hashing

Gradient local auto-correlation features for depth human action recognition

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