From Kinect skeleton data to hand gesture recognition with radar

Li, Jiayi; Shrestha, Ashish; Kernec, Julien Le; Fioranelli, Francesco

doi:10.1049/joe.2019.0557

Cited by 11 publications

(3 citation statements)

References 30 publications

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“…These results show that exploiting the complex nature of the signals is essential in improving performances, as suggested in [37] calling to use complex numbers in machine learning architectures Future directions for this project would look at data augmentation to increase the size of the dataset for better model generalisation. Popular augmentation methods include image rotation, reflection and scaling along the x or y axis, cropping and translation [38,39]. However, in radar, not all those techniques are valid as radar images have physical meaning.…”

Section: Discussionmentioning

confidence: 99%

Elderly Care - Human Activity Recognition Using Radar with an Open Dataset and Hybrid Maps

Zhang

Abbasi

Fioranelli

et al. 2022

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Section: Discussionmentioning

confidence: 99%

Elderly Care - Human Activity Recognition Using Radar with an Open Dataset and Hybrid Maps

Zhang

Abbasi

Fioranelli

et al. 2022

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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“…For the simulation model, the construction of the model and the set up of the radar refers to the work in [28]. The human model is constructed using the Boulic kinematic model [29] with 17 joints (e.g., head, neck, shoulder, knee, ankle, toe), which represent the skeleton structure of a person (Figure 11a) and the sampling rate is 2048 Hz per walking cycle.…”

Section: Number Of Features 11mentioning

confidence: 99%

Hierarchical Radar Data Analysis for Activity and Personnel Recognition

Fioranelli

et al. 2020

Remote Sensing

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Radar-based classification of human activities and gait have attracted significant attention with a large number of approaches proposed in terms of features and classification algorithms. A common approach in activity classification attempts to find the algorithm (features plus classifier) that can deal with multiple activities analysed in one study such as walking, sitting, drinking and crawling. However, using the same set of features for multiple activities can be suboptimal per activity and not take into account the diversity of kinematic movements that could be captured by diverse features. In this paper, we propose a hierarchical classification approach that uses a large variety of features including but not limited to energy features like entropy and energy curve, physical features like centroid and bandwidth, image-based features like skewness extracted from multiple radar data domains. Feature selection is used at each step of the hierarchical model to select the best set of features to discriminate the target activity from the others, showing improvements with respect to the more conventional approach of using a multiclass model. The proposed approach is validated on a large dataset with 1078 recorded samples of varying length from 5 s to 10 s of experimental data, yielding 95.4% accuracy to classify six activities. The approach is also validated on a personnel recognition task to identify individual subjects from their walking gait, yielding 83.7% accuracy for ten subjects and 68.2% for a significantly larger group of subjects, i.e., 60 people.

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“…For example, due to data collection costs or because healthy individuals cannot be included in the studies for ethical reasons. Finally, from the industrial field, it is worth mentioning fault detection [ 5 ], and from the area of human–machine interaction, we would highlight gesture, emotion [ 6 ] recognition and the various areas of activity recognition [ 7 , 8 , 9 ], sports [ 10 ], gaming [ 11 ], and fall detectors [ 12 , 13 ], which are primarily, but not exclusively, used in elderly monitoring systems or medical applications.…”

Section: Introductionmentioning

confidence: 99%

A Comparative Study of the Use of Stratified Cross-Validation and Distribution-Balanced Stratified Cross-Validation in Imbalanced Learning

Szeghalmy

Fazekas

2023

Sensors

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Nowadays, the solution to many practical problems relies on machine learning tools. However, compiling the appropriate training data set for real-world classification problems is challenging because collecting the right amount of data for each class is often difficult or even impossible. In such cases, we can easily face the problem of imbalanced learning. There are many methods in the literature for solving the imbalanced learning problem, so it has become a serious question how to compare the performance of the imbalanced learning methods. Inadequate validation techniques can provide misleading results (e.g., due to data shift), which leads to the development of methods designed for imbalanced data sets, such as stratified cross-validation (SCV) and distribution optimally balanced SCV (DOB-SCV). Previous studies have shown that higher classification performance scores (AUC) can be achieved on imbalanced data sets using DOB-SCV instead of SCV. We investigated the effect of the oversamplers on this difference. The study was conducted on 420 data sets, involving several sampling methods and the DTree, kNN, SVM, and MLP classifiers. We point out that DOB-SCV often provides a little higher F1 and AUC values for classification combined with sampling. However, the results also prove that the selection of the sampler–classifier pair is more important for the classification performance than the choice between the DOB-SCV and the SCV techniques.

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From Kinect skeleton data to hand gesture recognition with radar

Cited by 11 publications

References 30 publications

Elderly Care - Human Activity Recognition Using Radar with an Open Dataset and Hybrid Maps

Elderly Care - Human Activity Recognition Using Radar with an Open Dataset and Hybrid Maps

Hierarchical Radar Data Analysis for Activity and Personnel Recognition

A Comparative Study of the Use of Stratified Cross-Validation and Distribution-Balanced Stratified Cross-Validation in Imbalanced Learning

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