Human Action Recognition Using Triaxial Accelerometer Data: Selective Approach

Mimouna, Amira; Khalifa, Anouar Ben; Amara, Najoua Essoukri Ben

doi:10.1109/ssd.2018.8570429

Cited by 15 publications

(6 citation statements)

References 15 publications

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“…Figure 10 shows (a) the distribution of these models, (b) the obtained average accuracy and (c) the average number of recognized activities of daily life. Among the different types of classical ML models, the most commonly used model was the Support Vector Machine (SVM) model [4], [6], [58], [60], [69], [78], [79], [82], [85], [92], [95], [109], [118], [127], [131], [132], [136], [138], [142], [145], [155], [168], [169], [171], [173], [182], [184], [186], [187], [189], [190], [209]- [212] which was used in 35 papers, achieving an average accuracy of 92.3% over an average of 12 activities. The second most used model is the classical k-Nearest Neighbor (kNN) model [4], [6], [42], [60], [61], [69], [78], [79], [92], [95], [96],…”

Section: B Machine Learning (Ml) Based Methodologiesmentioning

confidence: 99%

“…Concerning the Noise Removal step, 48 CML-based articles and 12 DL-based articles make use of different noise removal techniques. Among all such techniques the most used ones are: z-normalization [75], [120], min-max [70], [127], and linear interpolation [102], [111] are the most used normalization steps, preceded by a filtering step based on the application of outlier detection [70], [117], [163], Butterworth [82], [101], [117], [123], [127], [128], [152], [155], [174], [189], median [74], [101], [117], [127], [132], [147], [155], [182], [183], high-pass [92], [96], [117], [128], [169], [173], [208], or statistical [58] filters.…”

Section: Preprocessing and Feature Extractionmentioning

confidence: 99%

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Human Activity Recognition Using Inertial, Physiological and Environmental Sensors: A Comprehensive Survey

et al. 2020

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In the last decade, Human Activity Recognition (HAR) has become a vibrant research area, especially due to the spread of electronic devices such as smartphones, smartwatches and video cameras present in our daily lives. In addition, the advance of deep learning and other machine learning algorithms has allowed researchers to use HAR in various domains including sports, health and well-being applications. For example, HAR is considered as one of the most promising assistive technology tools to support elderly's daily life by monitoring their cognitive and physical function through daily activities. This survey focuses on critical role of machine learning in developing HAR applications based on inertial sensors in conjunction with physiological and environmental sensors.INDEX TERMS Human activity recognition (HAR), deep learning (DL), machine learning (ML), available datasets, sensors, accelerometer.

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Section: B Machine Learning (Ml) Based Methodologiesmentioning

confidence: 99%

Section: Preprocessing and Feature Extractionmentioning

confidence: 99%

Human Activity Recognition Using Inertial, Physiological and Environmental Sensors: A Comprehensive Survey

et al. 2020

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“…62 show that SVM and Multi-Task Conditional Restricted Boltzmann Machines (MT-CRBMs) classifiers have achieved an accuracy of 41.3% and 54.5% using BML motion capture data, respectively. For the MHAD dataset 63 , has reported an accuracy of 98% by applying SVM on accelerometer data. The random forest classifier has also achieved an accuracy of 96% and 68.2% using MHAD motion capture and audio data 64 .…”

Section: Resultsmentioning

confidence: 99%

“…Then the baseline offset is removed from time-series data. In the MHAD dataset, 3% of the signals are removed from the end of each data sample as the reported experiments show improvement in the accuracy 63 .…”

Section: Systemmentioning

confidence: 99%

Human activity recognition using magnetic induction-based motion signals and deep recurrent neural networks

Golestani

Moghaddam

2020

Nat Commun

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Recognizing human physical activities using wireless sensor networks has attracted significant research interest due to its broad range of applications, such as healthcare, rehabilitation, athletics, and senior monitoring. There are critical challenges inherent in designing a sensor-based activity recognition system operating in and around a lossy medium such as the human body to gain a trade-off among power consumption, cost, computational complexity, and accuracy. We introduce an innovative wireless system based on magnetic induction for human activity recognition to tackle these challenges and constraints. The magnetic induction system is integrated with machine learning techniques to detect a wide range of human motions. This approach is successfully evaluated using synthesized datasets, laboratory measurements, and deep recurrent neural networks.

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“…Thereby, video surveillance systems became a daily priority in public spaces around the world inasmuch as they help enhance citizens' security and, hence, reduce risks of becoming a victim of crime. Most of the researches that have been conducted during the last years were devoted to focusing on automatic Human Action Recognition (HAR) [1][2][3][4]. However, the automatic characterization of aggressive activities has been comparatively less studied [5,6].…”

Section: Introductionmentioning

confidence: 99%

A Novel Spatio-Temporal Violence Classification Framework Based on Material Derivative and LSTM Neural Network

Lejmi¹,

Khalifa²,

Mahjoub³

2020

Self Cite

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In the current era, the implementation of automated security video surveillance systems is particularly needy in terms of human violence recognition. Nevertheless, the latter encounters various interlinked difficulties which require efficient solutions as well as feasible methods that provide a relevant distinction between normal human actions and abnormal ones. In this paper, we present an overview of these issues and a literature review of the related works and current research on-going efforts on this field and suggests a novel prediction model for violence recognition, based on a preliminary spatio-temporal features extraction using the material derivative which describes the rate of change of a particle while in motion with respect to time. The classification algorithm is then carried out using a deep learning LSTM technique to classify generated features into eight specified violent and non-violent categories and a prediction value for each class of action is calculated. The whole model is trained on a public dataset and its classification capacity is evaluated on a confusion matrix which assembles all the predictions made by the system with their actual labels. The obtained results are promising and show that the proposed model can be potentially useful for detecting human violence.

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Human Action Recognition Using Triaxial Accelerometer Data: Selective Approach

Cited by 15 publications

References 15 publications

Human Activity Recognition Using Inertial, Physiological and Environmental Sensors: A Comprehensive Survey

Human Activity Recognition Using Inertial, Physiological and Environmental Sensors: A Comprehensive Survey

Human activity recognition using magnetic induction-based motion signals and deep recurrent neural networks

A Novel Spatio-Temporal Violence Classification Framework Based on Material Derivative and LSTM Neural Network

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