RGB-D based human action recognition using evolutionary self-adaptive extreme learning machine with knowledge-based control parameters

Pareek, Preksha; Thakkar, Ankit

doi:10.1007/s12652-021-03348-w

Cited by 13 publications

(5 citation statements)

References 85 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…SKPDE-ELM (hybrid) [44] Depth motion map (DMM), local binary pattern (LBP) ELM optimized by SKPDE MSRAction3D [48], MSRDaily Activity3D [49], MSRGesture3D [50], UTD-MHAD [51] Deep autoencoder based (intermediate) [36] RGB and depth features Not specified Online RGBD action [52], MSRDaily Activity3D [49],…”

Section: Fusion Methods Fusion Inputs Encoders Datasetsmentioning

confidence: 99%

See 1 more Smart Citation

Comparison Analysis of Multimodal Fusion for Dangerous Action Recognition in Railway Construction Sites

Amel,

Siebert,

Mahmoudi

2024

Electronics

View full text Add to dashboard Cite

The growing demand for advanced tools to ensure safety in railway construction projects highlights the need for systems that can smoothly integrate and analyze multiple data modalities, such as multimodal learning algorithms. The latter, inspired by the human brain’s ability to integrate many sensory inputs, has emerged as a promising field in artificial intelligence. In light of this, there has been a rise in research on multimodal fusion approaches, which have the potential to outperform standard unimodal solutions. However, the integration of multiple data sources presents significant challenges to be addressed. This work attempts to apply multimodal learning to detect dangerous actions using RGB-D inputs. The key contributions include the evaluation of various fusion strategies and modality encoders, as well as identifying the most effective methods for capturing complex cross-modal interactions. The superior performance of the MultConcat multimodal fusion method was demonstrated, achieving an accuracy of 89.3%. Results also underscore the critical need for robust modality encoders and advanced fusion techniques to outperform unimodal solutions.

show abstract

Section: Fusion Methods Fusion Inputs Encoders Datasetsmentioning

confidence: 99%

“…Ref. [44] primarily focuses on depth-based input for human action recognition, employing depth motion maps (DMM) and local binary patterns (LBPs) applied to depth data for feature extraction and representation.…”

Section: Machine Learning-based Solutionsmentioning

confidence: 99%

Comparison Analysis of Multimodal Fusion for Dangerous Action Recognition in Railway Construction Sites

Amel,

Siebert,

Mahmoudi

2024

Electronics

View full text Add to dashboard Cite

show abstract

“…• Model 6: Pareek and Thakkar (2023) proposed a human action recognition approach for depthbased input by designing a single-layer feed-forward network using self-adaptive differential evolution with a knowledge-based control parameter -extreme learning machine.…”

Section: Dataset and Baseline Modelmentioning

confidence: 99%

Home Activity Recognition for Rural Elderly Based on Deep Learning and Smartphone Sensors

Zhang,

Tong,

Lin

2024

Journal of Organizational and End User Computing

View full text Add to dashboard Cite

With the exacerbation of the rural aging population trend, home-based health monitoring for the rural elderly has become a societal focal point, demanding an effective technological means to elevate the level of rural elderly health management. However, traditional algorithms for monitoring rural elderly behavior face myriad challenges, such as effectively capturing temporal and spatial features. Consequently, addressing the need to enhance the accuracy and robustness of rural elderly behavior recognition has become an urgent problem to solve. This study responds to this challenge by comprehensively employing deep learning and temporal modeling techniques, designing, and validating a short-term and long-term dual-layer home-based health monitoring system for the rural elderly.In the short-term layer, the model utilizes smartphones to collect health information from the rural elderly in various ways and performs real-time anomaly behavior detection.

show abstract

“…where d[T(n), R(w(n))] represents the distance measure of nth frame vector T(n) and m � w(n)th frame vector R(m) and D is the cumulative distance of the vector. In practical application, the global limit of optimal matching path of DTW algorithm is shown in Figure 1(a), and the local path limit is shown in Figure 1(b) [12], and its stop point (N, M) satisfies…”

Section: Introduction To Dtw Algorithmmentioning

confidence: 99%

Research on Contactless Identification and Evaluation of Unarmed Fitness Activity

2022

Scientific Programming

View full text Add to dashboard Cite

For low accuracy in the recognition and evaluation of unarmed fitness activities, analyzing the impact of the diversity of the execution speed of unarmed fitness activities on the recognition and evaluation results, a contactless recognition and evaluation method of unarmed fitness activities has been proposed by using the normalization to improve dynamic time warping (DTW) algorithm. As can be seen, the proposed method can reduce the influence of activity execution speed change on activity recognition and evaluation, so as to improve the contactless recognition accuracy of unarmed fitness activities. Meanwhile, the overall recognition accuracy can reach 0.91, and the positive rate can be more than 0.84, which has strong robustness.

show abstract

RGB-D based human action recognition using evolutionary self-adaptive extreme learning machine with knowledge-based control parameters

Cited by 13 publications

References 85 publications

Comparison Analysis of Multimodal Fusion for Dangerous Action Recognition in Railway Construction Sites

Comparison Analysis of Multimodal Fusion for Dangerous Action Recognition in Railway Construction Sites

Home Activity Recognition for Rural Elderly Based on Deep Learning and Smartphone Sensors

Research on Contactless Identification and Evaluation of Unarmed Fitness Activity

Contact Info

Product

Resources

About