[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127606-en":3,"doc-seo-127606-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},127606,549768064778,"Finn","https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8",8,"Research & Report","Using AI to decode the behavioral responses of an insect to chemical stimuli: towards machine-animal computational technologies","Orthoptera rely on richly receptor-equipped antennae, enabling accurate olfactory sensing and making them suitable as biosensors for environmental and agricultural monitoring. This study tests whether the house cricket Acheta domesticus can detect chemical cues by analyzing antenna movements and identifying stimulus-specific antennal displays exposed to cues such as sucrose or ammonia. A SLEAP-based pose-estimation network is optimized via grid search (mAP 83.74%), and stimulus classification uses sequence-keypoint networks tuned with genetic algorithms, validated by iterated K-fold. The paper also introduces a cricket-recording dataset linking behavior to chemical stimuli, enabling extensible Biohybrid Intelligent Sensing Systems.","International Journal of Machine Learning and Cybernetics [https://doi.org/10.1007/s13042-023-02009-y](https://doi.org/10.1007/s13042-023-02009-y)  \nUsing AI to decode the behavioral responses of an insect to chemical stimuli: towards machine‑animal computational technologies  \nEdoardo Fazzari1,2 · Fabio Carrara3 · Fabrizio Falchi1,3 · Cesare Stefanini1,2 · Donato Romano1,2  \nReceived: 6 May 2023 / Accepted: 14 October 2023 © The Author(s) 2023  \nAbstract  \nOrthoptera are insects with excellent olfactory sense abilities due to their antennae richly equipped with receptors. This makes them interesting model organisms to be used as biosensors for environmental and agricultural monitoring. Herein, we investigated if the house cricket Acheta domesticus can be used to detect different chemical cues by examining the movements of their antennae and attempting to identify specific antennal displays associated to different chemical cues exposed (e.g., sucrose or ammonia powder). A neural network based on state-of-the-art techniques (i.e., SLEAP) for pose estimation was built to identify the proximal and distal ends of the antennae. The network was optimised via grid search, resulting in a mean Average Precision (mAP) of 83.74% . To classify the stimulus type, another network was employed to take in a series of keypoint sequences, and output the stimulus classification. To find the best one-dimensional convolutional and recurrent neural networks, a genetic algorithm-based optimisation method was used. These networks were validated with iterated K-fold validation, obtaining an average accuracy of 45.33% for the former and 44% for the latter. Notably, we published and introduced the first dataset on cricket recordings that relate this animal’s behaviour to chemical stimuli. Overall, this study proposes a novel and simple automated method that can be extended to other animals for the creation of Biohybrid Intelligent Sensing Systems (e.g., automated video-analysis of an organism’s behaviour) to be exploited in various ecological scenarios.  \nKeywords Biosensor · Deep learning · Pose estimation · Sequence classification · Cricket · Biohybrid system  \n* Edoardo Fazzari [edoardo.fazzari@santannapisa.it](edoardo.fazzari@santannapisa.it)  \nFabio Carrara  \n[fabio.carrara@isti.cnr.it](fabio.carrara@isti.cnr.it)  \nFabrizio Falchi  \n[fabrizio.falchi@cnr.it](fabrizio.falchi@cnr.it)  \nCesare Stefanini  \n[cesare.stefanini@santannapisa.it](cesare.stefanini@santannapisa.it)  \nDonato Romano  \n[donato.romano@santannapisa.it](donato.romano@santannapisa.it)  \n1 The BioRobotics Institute, Sant’Anna School of Advanced Studies, Viale Rinaldo Piaggio, 56025 Pontedera, Italy  \n2 Department of Excellence in Robotics and AI, Sant’Anna School of Advanced Studies, Piazza Martiri della Libertà, 56127 Pisa, Italy  \n3 Institute of Information Science and Technologies, National Research Council of Italy, via G. Moruzzi, 56124 Pisa, Italy  \n1 Introduction  \nIn recent years, Artificial Intelligence (AI) has become a critical tool in various biological research fields, such as medicine [1], agriculture [2] and environmental monitoring [3]. Deep Learning, a subset of AI, has been instrumental in surpassing human performance in complex, time-consuming tasks [4] . This has enabled the development of new precision techniques that contribute to improving environmental sustainability, and with significant socio-economic implications [5] .  \nThe advent of such precision techniques through the application of Deep Learning has paved the way for the construction of Biohybrid Intelligent Sensing Systems (BISSs), which represents an innovative approach to animal biosensors that utilizes artificial intelligence to detect changes and analyze the environment. As a result, the integration of AI in biological research fields, such as medicine, agriculture, and environmental monitoring, has led to significant advancements that have the potential to enhance the performance of BISSs and ultim","cbCaisQxC4t8FJUG","https://ap.wps.com/l/cbCaisQxC4t8FJUG","pdf",1087042,1,10,"English","en",105,"# Introduction\n## Motivation: AI in biological research\n## Animal biosensors and the need for automation\n## AI-based pose estimation for biosensing","[{\"question\":\"How does the study use AI to analyze cricket responses to chemical stimuli?\",\"answer\":\"It records antenna movements of the house cricket and applies AI for pose estimation to identify antenna endpoints and stimulus-specific antennal displays.\"},{\"question\":\"What is the role of SLEAP in the proposed pipeline?\",\"answer\":\"SLEAP-based neural networks perform pose estimation to generate keypoint sequences representing antenna position and motion under different chemical cues.\"},{\"question\":\"How are different stimulus types classified and how were models evaluated?\",\"answer\":\"Stimulus classification is performed from keypoint sequences using deep networks optimized with a genetic algorithm, and validated using iterated K-fold to measure average accuracy.\"}]","Using AI to decode the behavioral responses of an insect to chemical stimuli: towards machine-animal computational technologies | 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does the study use AI to analyze cricket responses to chemical stimuli?","Question",{"text":76,"@type":77},"It records antenna movements of the house cricket and applies AI for pose estimation to identify antenna endpoints and stimulus-specific antennal displays.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"What is the role of SLEAP in the proposed pipeline?",{"text":81,"@type":77},"SLEAP-based neural networks perform pose estimation to generate keypoint sequences representing antenna position and motion under different chemical cues.",{"name":83,"@type":74,"acceptedAnswer":84},"How are different stimulus types classified and how were models evaluated?",{"text":85,"@type":77},"Stimulus classification is performed from keypoint sequences using deep networks optimized with a genetic algorithm, and validated using iterated K-fold to measure average 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