Bhanu Teja Gullapalli
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Abstract
Opioid use disorder is a medical condition with major social and economic consequences. While ubiquitous physiological sensing technologies have been widely adopted and extensively used to monitor day-to-day activities and deliver targeted interventions to improve human health, the use of these technologies to detect drug use in natural environments has been largely underexplored. The long-term goal of our work is to develop a mobile technology system that can identify high-risk opioid-related events (i.e., development of tolerance in the setting of prescription opioid use, return-to-use events in the setting of opioid use disorder) and deploy just-in-time interventions to mitigate the risk of overdose morbidity and mortality. In the current paper, we take an initial step by asking a crucial question: Can opioid use be detected using physiological signals obtained from a wrist-mounted sensor? Thirty-six individuals who were admitted to the hospital for an acute painful condition and received opioid analgesics as part of their clinical care were enrolled. Subjects wore a noninvasive wrist sensor during this time (1-14 days) that continuously measured physiological signals (heart rate, skin temperature, accelerometry, electrodermal activity, and interbeat interval). We collected a total of 2070 hours (≈ 86 days) of physiological data and observed a total of 339 opioid administrations. Our results are encouraging and show that using a Channel-Temporal Attention TCN (CTA-TCN) model, we can detect an opioid administration in a time-window with an F1-score of 0.80, a specificity of 0.77, sensitivity of 0.80, and an AUC of 0.77. We also predict the exact moment of administration in this time-window with a normalized mean absolute error of 8.6% and R2 coefficient of 0.85.
- Amin Ahsan Ali, Syed Monowar Hossain, Karen Hovsepian, Md Mahbubur Rahman, Kurt Plarre, and Santosh Kumar. 2012. mPuff: automated detection of cigarette smoking puffs from respiration measurements. In Proceedings of the 11th international conference on Information Processing in Sensor Networks. 269--280.Google Scholar
Digital Library
- Marco Ancona, Enea Ceolini, Cengiz Öztireli, and Markus Gross. 2017. Towards better understanding of gradient-based attribution methods for deep neural networks. arXiv preprint arXiv:1711.06104 (2017).Google Scholar
- Anshika Arora, Pinaki Chakraborty, and MPS Bhatia. 2020. Analysis of Data from Wearable Sensors for Sleep Quality Estimation and Prediction Using Deep Learning. Arabian Journal for Science and Engineering 45, 12 (2020), 10793--10812.Google ScholarCross Ref
- Ferhat Attal, Samer Mohammed, Mariam Dedabrishvili, Faicel Chamroukhi, Latifa Oukhellou, and Yacine Amirat. 2015. Physical human activity recognition using wearable sensors. Sensors 15, 12 (2015), 31314--31338.Google ScholarCross Ref
- Kavita M Babu, Jeffrey Brent, and David N Juurlink. 2019. Prevention of opioid overdose. New England Journal of Medicine 380, 23 (2019), 2246--2255.Google ScholarCross Ref
- Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2014. Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473 (2014).Google Scholar
- Shaojie Bai, J Zico Kolter, and Vladlen Koltun. 2018. An empirical evaluation of generic convolutional and recurrent networks for sequence modeling. arXiv preprint arXiv:1803.01271 (2018).Google Scholar
- Randall Clint Baselt and Robert H Cravey. 1982. Disposition of toxic drugs and chemicals in man. Vol. 8. Biomedical publications Davis, CA.Google Scholar
- Mina Behzadi, Siyavash Joukar, and Ahmad Beik. 2018. Opioids and cardiac arrhythmia: a literature review. Medical principles and practice 27, 5 (2018), 401--414.Google Scholar
- Alexander J Boe, Lori L McGee Koch, Megan K O'Brien, Nicholas Shawen, John A Rogers, Richard L Lieber, Kathryn J Reid, Phyllis C Zee, and Arun Jayaraman. 2019. Automating sleep stage classification using wireless, wearable sensors. NPJ digital medicine 2, 1 (2019), 1--9.Google Scholar
- Sansanee Boonnithi and Sukanya Phongsuphap. 2011. Comparison of heart rate variability measures for mental stress detection. In 2011 Computing in Cardiology. IEEE, 85--88.Google Scholar
- Alan S Campbell, Jayoung Kim, and Joseph Wang. 2018. Wearable electrochemical alcohol biosensors. Current opinion in electrochemistry 10 (2018), 126--135.Google Scholar
- Stephanie Carreiro, Hua Fang, Jianying Zhang, Kelley Wittbold, Shicheng Weng, Rachel Mullins, David Smelson, and Edward W Boyer. 2015. iMStrong: deployment of a biosensor system to detect cocaine use. Journal of medical systems 39, 12 (2015), 1--8.Google ScholarCross Ref
- Stephanie Carreiro, Kelley Wittbold, Premananda Indic, Hua Fang, Jianying Zhang, and Edward W Boyer. 2016. Wearable biosensors to detect physiologic change during opioid use. Journal of medical toxicology 12, 3 (2016), 255--262.Google ScholarCross Ref
- Soujanya Chatterjee, Alexander Moreno, Steven Lloyd Lizotte, Sayma Akther, Emre Ertin, Christopher P Fagundes, Cho Lam, James M Rehg, Neng Wan, David W Wetter, et al. 2020. SmokingOpp: Detecting the Smoking'Opportunity'Context Using Mobile Sensors. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 4, 1 (2020), 1--26.Google ScholarDigital Library
- Alexander Chen and Michael A Ashburn. 2015. Cardiac effects of opioid therapy. Pain Medicine 16, suppl_1 (2015), S27-S31.Google ScholarCross Ref
- Xi Cheng, Xiang Li, Jian Yang, and Ying Tai. 2018. SESR: Single image super resolution with recursive squeeze and excitation networks. In 2018 24th International Conference on Pattern Recognition (ICPR). IEEE, 147--152.Google ScholarCross Ref
- Lucio Ciabattoni, Francesco Ferracuti, Sauro Longhi, Lucia Pepa, Luca Romeo, and Federica Verdini. 2017. Real-time mental stress detection based on smartwatch. In 2017 IEEE International Conference on Consumer Electronics (ICCE). IEEE, 110--111.Google ScholarCross Ref
- Jacob Cohen. 1968. Weighted kappa: nominal scale agreement provision for scaled disagreement or partial credit. Psychological bulletin 70, 4 (1968), 213.Google Scholar
- Rui Dai, Luca Minciullo, Lorenzo Garattoni, Gianpiero Francesca, and François Bremond. 2019. Self-Attention Temporal Convolutional Network for Long-Term Daily Living Activity Detection. In 2019 16th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS). IEEE, 1--7.Google Scholar
- Sjoerd de Hoogd, Pyry AJ Välitalo, Albert Dahan, Simone van Kralingen, Michael MW Coughtrie, Eric PA van Dongen, Bert van Ramshorst, and Catherijne AJ Knibbe. 2017. Influence of morbid obesity on the pharmacokinetics of morphine, morphine-3-glucuronide, and morphine-6-glucuronide. Clinical pharmacokinetics 56, 12 (2017), 1577--1587.Google Scholar
- Jordi de La Torre, Domenec Puig, and Aida Valls. 2018. Weighted kappa loss function for multi-class classification of ordinal data in deep learning. Pattern Recognition Letters 105 (2018), 144--154.Google ScholarDigital Library
- Dandan Ding, Junchao Tong, and Lingyi Kong. 2020. A deep learning approach for quality enhancement of surveillance video. Journal of Intelligent Transportation Systems 24, 3 (2020), 304--314.Google ScholarCross Ref
- Jeffrey Donahue, Lisa Anne Hendricks, Sergio Guadarrama, Marcus Rohrbach, Subhashini Venugopalan, Kate Saenko, and Trevor Darrell. 2015. Long-term recurrent convolutional networks for visual recognition and description. In Proceedings of the IEEE conference on computer vision and pattern recognition. 2625--2634.Google ScholarCross Ref
- Robert H Eich, Robert Gilbert, and J Howard Auchincloss Jr. 1957. The acute effects of smoking on the mechanics of respiration in chronic obstructive pulmonary emphysema. American Review of Tuberculosis and Pulmonary Diseases 76, 1 (1957), 22--32.Google Scholar
- Jennifer M Fenn, Jennifer S Laurent, and Stacey C Sigmon. 2015. Increases in body mass index following initiation of methadone treatment. Journal of substance abuse treatment 51 (2015), 59--63.Google ScholarCross Ref
- Scott M Fishman, Barth Wilsey, Jane Yang, Gary M Reisfield, Tara B Bandman, and David Borsook. 2000. Adherence monitoring and drug surveillance in chronic opioid therapy. Journal of pain and symptom management 20, 4 (2000), 293--307.Google ScholarCross Ref
- Ana Catarina Fonseca and José M Ferro. 2013. Drug abuse and stroke. Current neurology and neuroscience reports 13, 2 (2013), 1--9.Google Scholar
- S Fu. 2016. Adulterants in urine drug testing. Advances in clinical chemistry 76 (2016), 123--163.Google Scholar
- Lei Gao, AK Bourke, and John Nelson. 2014. Evaluation of accelerometer based multi-sensor versus single-sensor activity recognition systems. Medical engineering & physics 36, 6 (2014), 779--785.Google Scholar
- Maurizio Garbarino, Matteo Lai, Dan Bender, Rosalind W Picard, and Simone Tognetti. 2014. Empatica E3---A wearable wireless multi-sensor device for real-time computerized biofeedback and data acquisition. In 2014 4th International Conference on Wireless Mobile Communication and Healthcare-Transforming Healthcare Through Innovations in Mobile and Wireless Technologies (MOBIHEALTH). IEEE, 39--42.Google ScholarCross Ref
- Alex Graves and Jürgen Schmidhuber. 2005. Framewise phoneme classification with bidirectional LSTM and other neural network architectures. Neural networks 18, 5--6 (2005), 602--610.Google Scholar
- Bhanu Teja Gullapalli, Annamalai Natarajan, Gustavo A Angarita, Robert T Malison, Deepak Ganesan, and Tauhidur Rahman. 2019. On-body sensing of cocaine craving, euphoria and drug-seeking behavior using cardiac and respiratory signals. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 3, 2 (2019), 1--31.Google ScholarDigital Library
- Paul A Harris, Robert Taylor, Brenda L Minor, Veida Elliott, Michelle Fernandez, Lindsay O'Neal, Laura McLeod, Giovanni Delacqua, Francesco Delacqua, Jacqueline Kirby, et al. 2019. The REDCap consortium: Building an international community of software platform partners. Journal of biomedical informatics 95 (2019), 103208.Google ScholarDigital Library
- Cheng He, Yun-jin Yao, and Xue-song Ye. 2017. An emotion recognition system based on physiological signals obtained by wearable sensors. In Wearable sensors and robots. Springer, 15--25.Google Scholar
- Howard A Heit and Douglas L Gourlay. 2004. Urine drug testing in pain medicine. Journal of pain and symptom management 27, 3 (2004), 260--267.Google ScholarCross Ref
- Sepp Hochreiter. 1998. The vanishing gradient problem during learning recurrent neural nets and problem solutions. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 6, 02 (1998), 107--116.Google ScholarDigital Library
- Syed Monowar Hossain, Amin Ahsan Ali, Md Mahbubur Rahman, Emre Ertine David Epstein, Ashley Kennedy, Kenzie Preston, Annie Umbricht, Yixin Chen, and Santosh Kumar. 2014. Identifying drug (cocaine) intake events from acute physiological response in the presence of free-living physical activity. In IPSN-14 Proceedings of the 13th International Symposium on Information Processing in Sensor Networks. IEEE, 71--82.Google ScholarCross Ref
- Jie Hu, Li Shen, and Gang Sun. 2018. Squeeze-and-excitation networks. In Proceedings of the IEEE conference on computer vision and pattern recognition. 7132--7141.Google ScholarCross Ref
- Masudul H Imtiaz, Volkan Y Senyurek, Prajakta Belsare, Stephen Tiffany, and Edward Sazonov. 2019. Objective detection of cigarette smoking from physiological sensor signals. In 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 3563--3566.Google ScholarCross Ref
- Igor Izrailtyan, Jiejing Qiu, Frank J Overdyk, Mary Erslon, and Tong J Gan. 2018. Risk factors for cardiopulmonary and respiratory arrest in medical and surgical hospital patients on opioid analgesics and sedatives. PloS one 13, 3 (2018), e0194553.Google ScholarCross Ref
- Margaret Jarvis, Jessica Williams, Matthew Hurford, Dawn Lindsay, Piper Lincoln, Leila Giles, Peter Luongo, and Taleen Safarian. 2017. Appropriate use of drug testing in clinical addiction medicine. Journal of addiction medicine 11, 3 (2017), 163--173.Google ScholarCross Ref
- Wenchao Jiang and Zhaozheng Yin. 2015. Human activity recognition using wearable sensors by deep convolutional neural networks. In Proceedings of the 23rd ACM international conference on Multimedia. 1307--1310.Google ScholarDigital Library
- Priyanka Kakria, NK Tripathi, and Peerapong Kitipawang. 2015. A real-time health monitoring system for remote cardiac patients using smartphone and wearable sensors. International journal of telemedicine and applications 2015 (2015).Google ScholarDigital Library
- Fazle Karim, Somshubra Majumdar, Houshang Darabi, and Shun Chen. 2017. LSTM fully convolutional networks for time series classification. IEEE access 6 (2017), 1662--1669.Google Scholar
- Nathaniel Katz and Gilbert J Fanciullo. 2002. Role of urine toxicology testing in the management of chronic opioid therapy. The Clinical journal of pain 18, 4 (2002), S76-S82.Google Scholar
- Heli Koskimäki, Henna Mönttinen, Pekka Siirtola, Hanna-Leena Huttunen, Raija Halonen, and Juha Röning. 2017. Early detection of migraine attacks based on wearable sensors: experiences of data collection using Empatica E4. In Proceedings of the 2017 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2017 ACM International Symposium on Wearable Computers. 506--511.Google ScholarDigital Library
- Yongjin Kwon, Kyuchang Kang, and Changseok Bae. 2014. Unsupervised learning for human activity recognition using smartphone sensors. Expert Systems with Applications 41, 14 (2014), 6067--6074.Google ScholarCross Ref
- Colin Lea, Michael D Flynn, Rene Vidal, Austin Reiter, and Gregory D Hager. 2017. Temporal convolutional networks for action segmentation and detection. In proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 156--165.Google ScholarCross Ref
- Liang Li, Qian Yu, Baoteng Xu, Qifan Bai, Yunpeng Zhang, Huijun Zhang, Chengjie Mao, Chunfeng Liu, Tianyu Shen, and Shouyan Wang. 2017. Multi-sensor wearable devices for movement monitoring in Parkinson's disease. In 2017 8th International IEEE/EMBS Conference on Neural Engineering (NER). IEEE, 70--73.Google ScholarCross Ref
- Xia Li, Jianlong Wu, Zhouchen Lin, Hong Liu, and Hongbin Zha. 2018. Recurrent squeeze-and-excitation context aggregation net for single image deraining. In Proceedings of the European Conference on Computer Vision (ECCV). 254--269.Google ScholarDigital Library
- C Lloret-Linares, A Lopes, X Declèves, A Serrie, S Mouly, J-F Bergmann, and S Perrot. 2013. Challenges in the optimisation of post-operative pain management with opioids in obese patients: a literature review. Obesity surgery 23, 9 (2013), 1458--1475.Google Scholar
- Md Shaad Mahmud, Hua Fang, Honggang Wang, Stephanie Carreiro, and Edward Boyer. 2018. Automatic Detection of Opioid Intake Using Wearable Biosensor. In 2018 International Conference on Computing, Networking and Communications (ICNC). IEEE, 784--788.Google Scholar
- M Marion Lee, M Sanford Silverman, M Hans Hansen, M Vikram Patel, and MD Laxmaiah Manchikanti. 2011. A comprehensive review of opioid-induced hyperalgesia. Pain physician 14 (2011), 145--161.Google Scholar
- Cameron McCarthy, Nikhilesh Pradhan, Calum Redpath, and Andy Adler. 2016. Validation of the Empatica E4 wristband. In 2016 IEEE EMBS international student conference (ISC). IEEE, 1--4.Google Scholar
- Sakorn Mekruksavanich, Narit Hnoohom, and Anuchit Jitpattanakul. 2018. Smartwatch-based sitting detection with human activity recognition for office workers syndrome. In 2018 International ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunications Engineering (ECTI-NCON). IEEE, 160--164.Google ScholarCross Ref
- Dawn Milner. 2004. The physiological effects of smoking on the respiratory system. Nursing times 100, 24 (2004), 56--59.Google Scholar
- Nir Milstein and Ilanit Gordon. 2020. Validating measures of electrodermal activity and heart rate variability derived from the empatica E4 utilized in research settings that involve interactive dyadic states. Frontiers in Behavioral Neuroscience 14 (2020).Google Scholar
- Farzaneh Montazerifar, Mansour Karajibani, and Kobra Lashkaripour. 2012. Effect of methadone maintenance therapy on anthropometric indices in opioid dependent patients. International journal of high risk behaviors & addiction 1, 3 (2012), 100.Google Scholar
- Jessica L Moreno, Matthew S Duprey, Bryan D Hayes, Kirsten Brooks, Sabrina Khalil, Sarah E Wakeman, Russell J Roberts, Jared S Jacobson, and John W Devlin. 2019. Agreement between self-reported psychoactive substance use and urine toxicology results for adults with opioid use disorder admitted to hospital. Toxicology communications 3, 1 (2019), 94--101.Google ScholarCross Ref
- Nitin Nair, Chinchu Thomas, and Dinesh Babu Jayagopi. 2018. Human activity recognition using temporal convolutional network. In Proceedings of the 5th international Workshop on Sensor-based Activity Recognition and Interaction. 1--8.Google ScholarDigital Library
- Vinod Nair and Geoffrey E Hinton. 2010. Rectified linear units improve restricted boltzmann machines. In Icml.Google Scholar
- Rajalakshmi Nandakumar, Shyamnath Gollakota, and Jacob E Sunshine. 2019. Opioid overdose detection using smartphones. Science translational medicine 11, 474 (2019).Google Scholar
- Annamalai Natarajan, Gustavo Angarita, Edward Gaiser, Robert Malison, Deepak Ganesan, and Benjamin M Marlin. 2016. Domain adaptation methods for improving lab-to-field generalization of cocaine detection using wearable ECG. In Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing. 875--885.Google ScholarDigital Library
- Annamalai Natarajan, Abhinav Parate, Edward Gaiser, Gustavo Angarita, Robert Malison, Benjamin Marlin, and Deepak Ganesan. 2013. Detecting cocaine use with wearable electrocardiogram sensors. In Proceedings of the 2013 ACM international joint conference on Pervasive and ubiquitous computing. 123--132.Google ScholarDigital Library
- Lewis S. Nelson and Dean Olsen. 2019. Opioids. McGraw-Hill Education, New York, NY. accesspharmacy.mhmedical.com/content.aspx?aid=1163010439Google Scholar
- Aaron van den Oord, Sander Dieleman, Heiga Zen, Karen Simonyan, Oriol Vinyals, Alex Graves, Nal Kalchbrenner, Andrew Senior, and Koray Kavukcuoglu. 2016. Wavenet: A generative model for raw audio. arXiv preprint arXiv:1609.03499 (2016).Google Scholar
- Qi Wei Oung, M Hariharan, Hoi Leong Lee, Shafriza Nisha Basah, Mohamed Sarillee, and Chia Hau Lee. 2015. Wearable multimodal sensors for evaluation of patients with Parkinson disease. In 2015 IEEE International Conference on Control System, Computing and Engineering (ICCSCE). IEEE, 269--274.Google ScholarCross Ref
- Abhinav Parate, Meng-Chieh Chiu, Chaniel Chadowitz, Deepak Ganesan, and Evangelos Kalogerakis. 2014. Risq: Recognizing smoking gestures with inertial sensors on a wristband. In Proceedings of the 12th annual international conference on Mobile systems, applications, and services. 149--161.Google ScholarDigital Library
- Gavril W Pasternak. 1993. Pharmacological mechanisms of opioid analgesics. Clinical neuropharmacology 16, 1 (1993), 1--18.Google Scholar
- Joseph V Pergolizzi Jr, Robert B Raffa, and Melanie H Rosenblatt. 2020. Opioid withdrawal symptoms, a consequence of chronic opioid use and opioid use disorder: Current understanding and approaches to management. Journal of clinical pharmacy and therapeutics 45, 5 (2020), 892--903.Google ScholarCross Ref
- Martin Ragot, Nicolas Martin, Sonia Em, Nico Pallamin, and Jean-Marc Diverrez. 2017. Emotion recognition using physiological signals: laboratory vs. wearable sensors. In International Conference on Applied Human Factors and Ergonomics. Springer, 15--22.Google Scholar
- Bethany R Raiff, Çağdaş Karataş, Erin A McClure, Dario Pompili, and Theodore A Walls. 2014. Laboratory validation of inertial body sensors to detect cigarette smoking arm movements. Electronics 3, 1 (2014), 87--110.Google ScholarCross Ref
- Aaqib Saeed, Tanir Ozcelebi, and Johan Lukkien. 2019. Multi-task self-supervised learning for human activity detection. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 3, 2 (2019), 1--30.Google ScholarDigital Library
- Pritam Sarkar and Ali Etemad. 2020. Self-supervised learning for ecg-based emotion recognition. In ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 3217--3221.Google ScholarCross Ref
- Philip Schmidt, Attila Reiss, Robert Duerichen, Claus Marberger, and Kristof Van Laerhoven. 2018. Introducing wesad, a multimodal dataset for wearable stress and affect detection. In Proceedings of the 20th ACM international conference on multimodal interaction. 400--408.Google ScholarDigital Library
- Johannes Schneider, Marc Schroth, Jörg Ottenbacher, and Wilhelm Stork. 2018. A novel wearable sensor device for continuous monitoring of cardiac activity during sleep. In 2018 IEEE Sensors Applications Symposium (SAS). IEEE, 1--6.Google ScholarDigital Library
- Angela AT Schuurmans, Peter de Looff, Karin S Nijhof, Catarina Rosada, Ron HJ Scholte, Arne Popma, and Roy Otten. 2020. Validity of the Empatica E4 wristband to measure heart rate variability (HRV) parameters: A comparison to electrocardiography (ECG). Journal of medical systems 44, 11 (2020), 1--11.Google ScholarDigital Library
- Ramprasaath R Selvaraju, Michael Cogswell, Abhishek Das, Ramakrishna Vedantam, Devi Parikh, and Dhruv Batra. 2017. Grad-cam: Visual explanations from deep networks via gradient-based localization. In Proceedings of the IEEE international conference on computer vision. 618--626.Google ScholarCross Ref
- Volkan Y Senyurek, Masudul H Imtiaz, Prajakta Belsare, Stephen Tiffany, and Edward Sazonov. 2019. A comparison of SVM and CNN-LSTM based approach for detecting smoke inhalations from respiratory signal. In 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 3262--3265.Google ScholarCross Ref
- Volkan Y Senyurek, Masudul H Imtiaz, Prajakta Belsare, Stephen Tiffany, and Edward Sazonov. 2020. A CNN-LSTM neural network for recognition of puffing in smoking episodes using wearable sensors. Biomedical Engineering Letters 10, 2 (2020), 195--203.Google ScholarCross Ref
- Mansi Shah and Martin R Huecker. 2020. Opioid withdrawal. In StatPearls [Internet]. StatPearls Publishing.Google Scholar
- Muhammad Shoaib, Hans Scholten, Paul JM Havinga, and Ozlem Durmaz Incel. 2016. A hierarchical lazy smoking detection algorithm using smartwatch sensors. In 2016 IEEE 18th International Conference on e-Health Networking, Applications and Services (Healthcom). IEEE, 1--6.Google ScholarCross Ref
- Avanti Shrikumar, Peyton Greenside, Anna Shcherbina, and Anshul Kundaje. 2016. Not just a black box: Learning important features through propagating activation differences. arXiv preprint arXiv:1605.01713 (2016).Google Scholar
- Howard S Smith. 2009. Opioid metabolism. In Mayo Clinic Proceedings, Vol. 84. Elsevier, 613--624.Google Scholar
- Offie P Soldin and Donald R Mattison. 2009. Sex differences in pharmacokinetics and pharmacodynamics. Clinical pharmacokinetics 48, 3 (2009), 143--157.Google Scholar
- Andrew Stokes, Kaitlyn M Berry, Jason M Collins, Chia-Wen Hsiao, Jason R Waggoner, Stephen S Johnston, Eric M Ammann, Robin F Scamuffa, Sonia Lee, Dielle J Lundberg, et al. 2019. The contribution of obesity to prescription opioid use in the United States. Pain 160, 10 (2019), 2255.Google ScholarCross Ref
- Alexander B Stone, Richard D Urman, Alan D Kaye, and Michael C Grant. 2018. Labeling morphine milligram equivalents on opioid packaging: a potential patient safety intervention. Current pain and headache reports 22, 7 (2018), 1--4.Google Scholar
- Qu Tang, Damon J Vidrine, Eric Crowder, and Stephen S Intille. 2014. Automated detection of puffing and smoking with wrist accelerometers. In Proceedings of the 8th International Conference on Pervasive Computing Technologies for Healthcare. 80--87.Google ScholarDigital Library
- Anong Tantisuwat and Premtip Thaveeratitham. 2014. Effects of smoking on chest expansion, lung function, and respiratory muscle strength of youths. Journal of physical therapy science 26, 2 (2014), 167--170.Google ScholarCross Ref
- Mika P Tarvainen, Juha-Pekka Niskanen, Jukka A Lipponen, Perttu O Ranta-Aho, and Pasi A Karjalainen. 2014. Kubios HRV-heart rate variability analysis software. Computer methods and programs in biomedicine 113, 1 (2014), 210--220.Google Scholar
- Arijit Ukil and Soma Bandyopadhyay. 2019. Automated cardiac health screening using smartphone and wearable sensors through anomaly analytics. In Mobile Solutions and Their Usefulness in Everyday Life. Springer, 145--172.Google Scholar
- Ramachandran Varatharajan, Gunasekaran Manogaran, Malarvizhi Kumar Priyan, and Revathi Sundarasekar. 2018. Wearable sensor devices for early detection of Alzheimer disease using dynamic time warping algorithm. Cluster Computing 21, 1 (2018), 681--690.Google ScholarCross Ref
- Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. In Advances in neural information processing systems. 5998--6008.Google Scholar
- Wanpen Vongpatanasin, Yasser Mansour, Bahman Chavoshan, Debbie Arbique, and Ronald G Victor. 1999. Cocaine stimulates the human cardiovascular system via a central mechanism of action. Circulation 100, 5 (1999), 497--502.Google ScholarCross Ref
- Nana Wilson, Mbabazi Kariisa, Puja Seth, Herschel Smith IV, and Nicole L Davis. 2020. Drug and opioid-involved overdose deaths---United States, 2017--2018. Morbidity and Mortality Weekly Report 69, 11 (2020), 290--297.Google ScholarCross Ref
- Danny Wyatt, Matthai Philipose, and Tanzeem Choudhury. 2005. Unsupervised activity recognition using automatically mined common sense. In AAAI, Vol. 5. 21--27.Google Scholar
- Kelvin Xu, Jimmy Ba, Ryan Kiros, Kyunghyun Cho, Aaron Courville, Ruslan Salakhudinov, Rich Zemel, and Yoshua Bengio. 2015. Show, attend and tell: Neural image caption generation with visual attention. In International conference on machine learning. PMLR, 2048--2057.Google Scholar
- David Z Yang, Billy Sin, Joshua Beckhusen, Dawei Xia, Rebecca Khaimova, and Ilia Iliev. 2019. Opioid-induced hyperalgesia in the nonsurgical setting: a systematic review. American journal of therapeutics 26, 3 (2019), e397-e405.Google ScholarCross Ref
- Marcello Zanghieri, Simone Benatti, Alessio Burrello, Victor Kartsch, Francesco Conti, and Luca Benini. 2019. Robust real-time embedded EMG recognition framework using temporal convolutional networks on a multicore IoT processor. IEEE transactions on biomedical circuits and systems 14, 2 (2019), 244--256.Google ScholarCross Ref
- MR Zarrindast, A Vahedy, MR Heidari, and M Ghazi Khansari. 1994. On the mechanism (s) of morphine-induced hypothermia. Journal of Psychopharmacology 8, 4 (1994), 222--226.Google ScholarCross Ref
- Matthew D Zeiler and Rob Fergus. 2014. Visualizing and understanding convolutional networks. In European conference on computer vision. Springer, 818--833.Google ScholarCross Ref
Index Terms
- OpiTrack: A Wearable-based Clinical Opioid Use Tracker with Temporal Convolutional Attention Networks
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