Wearable sensors based human behavioral pattern recognition using statistical features and reweighted genetic algorithm

Quaid, Majid Ali Khan; Jalal, Ahmad

doi:10.1007/s11042-019-08463-7

Cited by 124 publications

(50 citation statements)

References 29 publications

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“…Finally, we introduce the reweighted genetic algorithm [ 48 ], which consists in giving weights to specific features while avoiding others. In this way, we did not need to try all possible combinations of weights, which would increase computation with the conventional genetic algorithm.…”

Section: Methodsmentioning

confidence: 99%

A Study of Accelerometer and Gyroscope Measurements in Physical Life-Log Activities Detection Systems

Jalal

Quaid

Tahir

et al. 2020

Sensors

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Nowadays, wearable technology can enhance physical human life-log routines by shifting goals from merely counting steps to tackling significant healthcare challenges. Such wearable technology modules have presented opportunities to acquire important information about human activities in real-life environments. The purpose of this paper is to report on recent developments and to project future advances regarding wearable sensor systems for the sustainable monitoring and recording of human life-logs. On the basis of this survey, we propose a model that is designed to retrieve better information during physical activities in indoor and outdoor environments in order to improve the quality of life and to reduce risks. This model uses a fusion of both statistical and non-statistical features for the recognition of different activity patterns using wearable inertial sensors, i.e., triaxial accelerometers, gyroscopes and magnetometers. These features include signal magnitude, positive/negative peaks and position direction to explore signal orientation changes, position differentiation, temporal variation and optimal changes among coordinates. These features are processed by a genetic algorithm for the selection and classification of inertial signals to learn and recognize abnormal human movement. Our model was experimentally evaluated on four benchmark datasets: Intelligent Media Wearable Smart Home Activities (IM-WSHA), a self-annotated physical activities dataset, Wireless Sensor Data Mining (WISDM) with different sporting patterns from an IM-SB dataset and an SMotion dataset with different physical activities. Experimental results show that the proposed feature extraction strategy outperformed others, achieving an improved recognition accuracy of 81.92%, 95.37%, 90.17%, 94.58%, respectively, when IM-WSHA, WISDM, IM-SB and SMotion datasets were applied.

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Section: Methodsmentioning

confidence: 99%

A Study of Accelerometer and Gyroscope Measurements in Physical Life-Log Activities Detection Systems

Jalal

Quaid

Tahir

et al. 2020

Sensors

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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%

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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“…In order to solve the problem of feature selection and classification of sensor data, a genetic algorithm-based approach is used by M.A. Quaid et al [ 34 ]. Statistical and acoustic features are extracted and then features are reweighted.…”

Section: Related Workmentioning

confidence: 99%

Automatic Recognition of Human Interaction via Hybrid Descriptors and Maximum Entropy Markov Model Using Depth Sensors

Jalal

Khalid

Kim

2020

Entropy

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Automatic identification of human interaction is a challenging task especially in dynamic environments with cluttered backgrounds from video sequences. Advancements in computer vision sensor technologies provide powerful effects in human interaction recognition (HIR) during routine daily life. In this paper, we propose a novel features extraction method which incorporates robust entropy optimization and an efficient Maximum Entropy Markov Model (MEMM) for HIR via multiple vision sensors. The main objectives of proposed methodology are: (1) to propose a hybrid of four novel features—i.e., spatio-temporal features, energy-based features, shape based angular and geometric features—and a motion-orthogonal histogram of oriented gradient (MO-HOG); (2) to encode hybrid feature descriptors using a codebook, a Gaussian mixture model (GMM) and fisher encoding; (3) to optimize the encoded feature using a cross entropy optimization function; (4) to apply a MEMM classification algorithm to examine empirical expectations and highest entropy, which measure pattern variances to achieve outperformed HIR accuracy results. Our system is tested over three well-known datasets: SBU Kinect interaction; UoL 3D social activity; UT-interaction datasets. Through wide experimentations, the proposed features extraction algorithm, along with cross entropy optimization, has achieved the average accuracy rate of 91.25% with SBU, 90.4% with UoL and 87.4% with UT-Interaction datasets. The proposed HIR system will be applicable to a wide variety of man–machine interfaces, such as public-place surveillance, future medical applications, virtual reality, fitness exercises and 3D interactive gaming.

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Wearable sensors based human behavioral pattern recognition using statistical features and reweighted genetic algorithm

Cited by 124 publications

References 29 publications

A Study of Accelerometer and Gyroscope Measurements in Physical Life-Log Activities Detection Systems

A Study of Accelerometer and Gyroscope Measurements in Physical Life-Log Activities Detection Systems

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

Automatic Recognition of Human Interaction via Hybrid Descriptors and Maximum Entropy Markov Model Using Depth Sensors

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