Comparative Analysis of Sensor-Based Human Activity Recognition Using Artificial Intelligence

Swaminathan, Alagappan

doi:10.1007/978-3-031-16364-7_1

IFIP Advances in Information and Communication Technology

2022

DOI: 10.1007/978-3-031-16364-7_1

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Comparative Analysis of Sensor-Based Human Activity Recognition Using Artificial Intelligence

Alagappan Swaminathan

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Cited by 2 publications

References 34 publications

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Context‐aware hand gesture interaction for human–robot collaboration in construction

Wang,

Veeramani,

Dai

et al. 2024

Computer aided Civil Eng

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Construction robots play a pivotal role in enabling intelligent processes within the construction industry. User‐friendly interfaces that facilitate efficient human–robot collaboration are essential for promoting robot adoption. However, most of the existing interfaces do not consider contextual information in the collaborative environment. The situation where humans and robots work together in the same jobsite creates a unique environmental context. Overlooking contextual information would limit the potential to optimize interaction efficiency. This paper proposes a novel context‐aware method that utilizes a two‐stream network to enhance human–robot interaction in construction settings. In the proposed network, the first‐person view‐based stream focuses on the relevant spatiotemporal regions for context extraction, while the motion sensory data‐based stream obtains features related to hand motions. By fusing the vision context and motion data, the method achieves gesture recognition for efficient communication between construction workers and robots. Experimental evaluation on a dataset from five construction sites demonstrates an overall classification accuracy of 92.6%, underscoring the practicality and potential benefits of the proposed method.

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Context‐aware hand gesture interaction for human–robot collaboration in construction

Wang,

Veeramani,

Dai

et al. 2024

Computer aided Civil Eng

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Pulmonary disease detection and classification in patient respiratory audio files using long short-term memory neural networks

Zhang,

Swaminathan,

Uddin

2023

Front. Med.

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IntroductionIn order to improve the diagnostic accuracy of respiratory illnesses, our research introduces a novel methodology to precisely diagnose a subset of lung diseases using patient respiratory audio recordings. These lung diseases include Chronic Obstructive Pulmonary Disease (COPD), Upper Respiratory Tract Infections (URTI), Bronchiectasis, Pneumonia, and Bronchiolitis.MethodsOur proposed methodology trains four deep learning algorithms on an input dataset consisting of 920 patient respiratory audio files. These audio files were recorded using digital stethoscopes and comprise the Respiratory Sound Database. The four deployed models are Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), CNN ensembled with unidirectional LSTM (CNN-LSTM), and CNN ensembled with bidirectional LSTM (CNN-BLSTM).ResultsThe aforementioned models are evaluated using metrics such as accuracy, precision, recall, and F1-score. The best performing algorithm, LSTM, has an overall accuracy of 98.82% and F1-score of 0.97.DiscussionThe LSTM algorithm's extremely high predictive accuracy can be attributed to its penchant for capturing sequential patterns in time series based audio data. In summary, this algorithm is able to ingest patient audio recordings and make precise lung disease predictions in real-time.

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Comparative Analysis of Sensor-Based Human Activity Recognition Using Artificial Intelligence

Cited by 2 publications

References 34 publications

Context‐aware hand gesture interaction for human–robot collaboration in construction

Context‐aware hand gesture interaction for human–robot collaboration in construction

Pulmonary disease detection and classification in patient respiratory audio files using long short-term memory neural networks

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