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Article

Human Activity Recognition through Recurrent Neural Networks for Human–Robot Interaction in Agriculture

1
Institute for Bio-Economy and Agri-Technology (IBO), Centre of Research and Technology-Hellas (CERTH), 6th km Charilaou-Thermi Rd, GR 57001 Thessaloniki, Greece
2
farmB Digital Agriculture, Doiraniis 17, GR54639 Thessaloniki, Greece
3
Department of Computer Science & Telecommunications, University of Thessaly, GR35131 Lamia, Greece
*
Author to whom correspondence should be addressed.
Appl. Sci. 2021, 11(5), 2188; https://doi.org/10.3390/app11052188
Received: 29 January 2021 / Revised: 23 February 2021 / Accepted: 24 February 2021 / Published: 2 March 2021
(This article belongs to the Special Issue Applied Agri-Technologies)
The present study deals with human awareness, which is a very important aspect of human–robot interaction. This feature is particularly essential in agricultural environments, owing to the information-rich setup that they provide. The objective of this investigation was to recognize human activities associated with an envisioned synergistic task. In order to attain this goal, a data collection field experiment was designed that derived data from twenty healthy participants using five wearable sensors (embedded with tri-axial accelerometers, gyroscopes, and magnetometers) attached to them. The above task involved several sub-activities, which were carried out by agricultural workers in real field conditions, concerning load lifting and carrying. Subsequently, the obtained signals from on-body sensors were processed for noise-removal purposes and fed into a Long Short-Term Memory neural network, which is widely used in deep learning for feature recognition in time-dependent data sequences. The proposed methodology demonstrated considerable efficacy in predicting the defined sub-activities with an average accuracy of 85.6%. Moreover, the trained model properly classified the defined sub-activities in a range of 74.1–90.4% for precision and 71.0–96.9% for recall. It can be inferred that the combination of all sensors can achieve the highest accuracy in human activity recognition, as concluded from a comparative analysis for each sensor’s impact on the model’s performance. These results confirm the applicability of the proposed methodology for human awareness purposes in agricultural environments, while the dataset was made publicly available for future research. View Full-Text
Keywords: sensor fusion; accelerometer; gyroscope; magnetometer; machine learning; deep learning; long short-term memory networks; robotics; safe human-robot interaction; situation awareness sensor fusion; accelerometer; gyroscope; magnetometer; machine learning; deep learning; long short-term memory networks; robotics; safe human-robot interaction; situation awareness
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MDPI and ACS Style

Anagnostis, A.; Benos, L.; Tsaopoulos, D.; Tagarakis, A.; Tsolakis, N.; Bochtis, D. Human Activity Recognition through Recurrent Neural Networks for Human–Robot Interaction in Agriculture. Appl. Sci. 2021, 11, 2188. https://doi.org/10.3390/app11052188

AMA Style

Anagnostis A, Benos L, Tsaopoulos D, Tagarakis A, Tsolakis N, Bochtis D. Human Activity Recognition through Recurrent Neural Networks for Human–Robot Interaction in Agriculture. Applied Sciences. 2021; 11(5):2188. https://doi.org/10.3390/app11052188

Chicago/Turabian Style

Anagnostis, Athanasios, Lefteris Benos, Dimitrios Tsaopoulos, Aristotelis Tagarakis, Naoum Tsolakis, and Dionysis Bochtis. 2021. "Human Activity Recognition through Recurrent Neural Networks for Human–Robot Interaction in Agriculture" Applied Sciences 11, no. 5: 2188. https://doi.org/10.3390/app11052188

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