Human activity recognition system using smartphone based on machine learning algorithms

Gupta, Prakhar; Arora, Abhishek

doi:10.1063/5.0110384

Cited by 2 publications

(2 citation statements)

References 17 publications

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“…These metrics help to measure the effectiveness of the models in predicting the target variable. The calculation process for each evaluation metric is demonstrated in Equations ( 6)- (9). Accuracy = (TN + TP)/(TP + TN +FP + FN) (6) Precision = TP/(TP +FP) (7) Recall = TP/(TP + FN) ( 8)…”

Section: Performance Measurementmentioning

confidence: 99%

“…This spatial information, combined with reduced dimensionality, enhances the efficiency of the recognition process. Three-dimensional estimation of human pose is crucial in this context, as it enables an accurate understanding and interpretation of various human activities [9]. By capturing the spatial and temporal relationships among body joints and segments, 3D pose estimation enables the extraction of significant features that are crucial for discerning between various activities [10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

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Multi-Camera-Based Human Activity Recognition for Human–Robot Collaboration in Construction

Jang,

Jeong,

Younesi Heravi

et al. 2023

Sensors

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As the use of construction robots continues to increase, ensuring safety and productivity while working alongside human workers becomes crucial. To prevent collisions, robots must recognize human behavior in close proximity. However, single, or RGB-depth cameras have limitations, such as detection failure, sensor malfunction, occlusions, unconstrained lighting, and motion blur. Therefore, this study proposes a multiple-camera approach for human activity recognition during human–robot collaborative activities in construction. The proposed approach employs a particle filter, to estimate the 3D human pose by fusing 2D joint locations extracted from multiple cameras and applies long short-term memory network (LSTM) to recognize ten activities associated with human and robot collaboration tasks in construction. The study compared the performance of human activity recognition models using one, two, three, and four cameras. Results showed that using multiple cameras enhances recognition performance, providing a more accurate and reliable means of identifying and differentiating between various activities. The results of this study are expected to contribute to the advancement of human activity recognition and utilization in human–robot collaboration in construction.

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Section: Performance Measurementmentioning

confidence: 99%