Skeleton data pre-processing for human pose recognition using Neural Network

Guerra, Bruna Maria Vittoria; Ramat, Stefano; Gandolfi, Roberto; Beltrami, G.; Schmid, Micaela

doi:10.1109/embc44109.2020.9175588

Cited by 11 publications

(5 citation statements)

References 17 publications

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“…The Kinect sensor was used in this study as a portable, low-cost, and practical instrument [19,65] to extract the skeletal movement data for elderly populations. The application of the Kinect sensor was previously validated to assess balance and impairment in the elderly people compared to the established standard tools such as marker based motion capture systems, force plates, and wearable sensors [64,66,67].…”

Section: Discussionmentioning

confidence: 99%

Frailty Level Classification of the Community Elderly Using Microsoft Kinect-Based Skeleton Pose: A Machine Learning Approach

Akbari

Nikkhoo

Wang

et al. 2021

Sensors

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Frailty is one of the most important geriatric syndromes, which can be associated with increased risk for incident disability and hospitalization. Developing a real-time classification model of elderly frailty level could be beneficial for designing a clinical predictive assessment tool. Hence, the objective of this study was to predict the elderly frailty level utilizing the machine learning approach on skeleton data acquired from a Kinect sensor. Seven hundred and eighty-seven community elderly were recruited in this study. The Kinect data were acquired from the elderly performing different functional assessment exercises including: (1) 30-s arm curl; (2) 30-s chair sit-to-stand; (3) 2-min step; and (4) gait analysis tests. The proposed methodology was successfully validated by gender classification with accuracies up to 84 percent. Regarding frailty level evaluation and prediction, the results indicated that support vector classifier (SVC) and multi-layer perceptron (MLP) are the most successful estimators in prediction of the Fried’s frailty level with median accuracies up to 97.5 percent. The high level of accuracy achieved with the proposed methodology indicates that ML modeling can identify the risk of frailty in elderly individuals based on evaluating the real-time skeletal movements using the Kinect sensor.

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Section: Discussionmentioning

confidence: 99%

Frailty Level Classification of the Community Elderly Using Microsoft Kinect-Based Skeleton Pose: A Machine Learning Approach

Akbari

Nikkhoo

Wang

et al. 2021

Sensors

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show abstract

“…Several researchers have begun to recognize human activity in images or videos. Guerra et al [19] detect activities of daily living by preprocessing collected data from the Microsoft Kinect motion-sensing device to minimize systematic error. Reserach by Reily et al [20] proposed a new method to recognize human activity by simultaneous feature extraction from human posture and the activity's objects.…”

Section: Related Workmentioning

confidence: 99%

Discover human poses similarity and action recognition based on machine learning

2023

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In the computer vision field, human action recognition depending on pose estimation recently made considerable progress, especially by using deep learning, which improves recognition performance. Therefore, it has been employed in various applications, including sports and physical activity follow-up. This paper presents a technique for recognizing the human posture in different images and matching their pose similarity. This aims to evaluate the viability of employing computer vision techniques to verify a person's body pose during exercise and determine whether the pose is executed properly. Exercise is one strategy we use to maintain our health throughout life. Gymnastics and yoga are two examples of this type of exercise. The proposed algorithm identifies human action by recognizing the body's key points. The OpenPose library has been used to detect 18 key points of the human body. The action classification task is performed using the support vector machine (SVM) algorithm. Then, the algorithm computes the similarity of the human pose by comparing a model image to a test image to determine the matching score. Evaluations show that our method can perform at a competitive or state-of-the-art performance on a number of body pose datasets.

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“…Different feature selection methods, as well as different machine and deep learning architectures, were proposed for the classification block in previous studies by our group [28,29]. The most promising solution was proposed in Guerra et al [30], where a genetic algorithm was applied to select eight kinematic features and a sequence-to-sequence model was trained to identify five classes.…”

Section: Introductionmentioning

confidence: 99%

Recurrent Network Solutions for Human Posture Recognition Based on Kinect Skeletal Data

Guerra

Ramat

Beltrami

et al. 2023

Sensors

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Ambient Assisted Living (AAL) systems are designed to provide unobtrusive and user-friendly support in daily life and can be used for monitoring frail people based on various types of sensors, including wearables and cameras. Although cameras can be perceived as intrusive in terms of privacy, low-cost RGB-D devices (i.e., Kinect V2) that extract skeletal data can partially overcome these limits. In addition, deep learning-based algorithms, such as Recurrent Neural Networks (RNNs), can be trained on skeletal tracking data to automatically identify different human postures in the AAL domain. In this study, we investigate the performance of two RNN models (2BLSTM and 3BGRU) in identifying daily living postures and potentially dangerous situations in a home monitoring system, based on 3D skeletal data acquired with Kinect V2. We tested the RNN models with two different feature sets: one consisting of eight human-crafted kinematic features selected by a genetic algorithm, and another consisting of 52 ego-centric 3D coordinates of each considered skeleton joint, plus the subject’s distance from the Kinect V2. To improve the generalization ability of the 3BGRU model, we also applied a data augmentation method to balance the training dataset. With this last solution we reached an accuracy of 88%, the best we achieved so far.

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Skeleton data pre-processing for human pose recognition using Neural Network

Cited by 11 publications

References 17 publications

Frailty Level Classification of the Community Elderly Using Microsoft Kinect-Based Skeleton Pose: A Machine Learning Approach

Frailty Level Classification of the Community Elderly Using Microsoft Kinect-Based Skeleton Pose: A Machine Learning Approach

Discover human poses similarity and action recognition based on machine learning

Recurrent Network Solutions for Human Posture Recognition Based on Kinect Skeletal Data

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