Publications

Selected papers are listed below. A more complete and up-to-date list of the group’s publications can be found on Prof. Agu’s Google Scholar page:

Smartphone Wound Assessment and Decision Support (SmartWAnDS)

  • Palawat Busaranuvong, Emmanuel Agu, Deepak Kumar, Shefalika Gautam, Reza Saadati Fard, Bengisu Tulu, and Diane Strong. “Guided Conditional Diffusion Classifier (ConDiff) for Enhanced Prediction of Infection in Diabetic Foot Ulcers, IEEE Open Journal of Engineering in Medicine and Biology (OJEMB), (accepted, to appear)
  • Ziyang Liu, Emmanuel Agu, Peder Pedersen, Clifford Lindsay, Bengisu Tulu and Diane Strong, Chronic Wound Image Augmentation and Assessment using Semi-Supervised Progressive Multi-Granularity EfficientNet, IEEE Open Journal of Engineering in Medicine & Biology (OJEMB), 2023
  • Ziyang Liu, John Josvin and Emmanuel Agu, 2022. Diabetic Foot Ulcer Ischemia and Infection Classification using EfficientNet Deep Learning Models, IEEE Open Journal of Engineering in Medicine, and Biology (OJEMB), 3, pp.189-201
  • Ziyang Liu, Emmanuel Agu, Peder Pedersen, Clifford Lindsay, Bengisu Tulu, Diane Strong, 2021. Comprehensive assessment of fine-grained wound images using a patch-based CNN with context-preserving attention. IEEE open journal of engineering in medicine and biology, 2, pp.224-234
  • Holly Nguyen, Emmanuel Agu, Bengisu Tulu, Diane Strong, Haadi Mombini, Peder Pedersen, Clifford Lindsay, Raymond Dunn, Lorraine Loretz, Machine Learning Classification of Actionable Care Decisions on Lower Extremity Wounds, Elsevier Smart Health, 18, p.100139
  • Ameya Wagh, Shubham Jain, Apratim Mukherjee, Emmanuel Agu, Peder Pedersen, Diane Strong, Bengisu Tulu, Clifford Lindsay and Ziyang Liu, Semantic Segmentation of Smartphone Wound Images: Comparative Analysis of AHRF and CNN-Based Approaches, IEEE Access, vol. 8, pp. 181590-181604, 2020, doi: 10.1109/ACCESS.2020.3014175
  • Holly Nguyen, Emmanuel Agu, Bengisu Tulu, Diane Strong, Haadi Mombini, Peder Pedersen, Clifford Lindsay, Raymond Dunn, Lorraine Loretz, Machine Learning Classification of Actionable Care Decisions on Lower Extremity Wounds, Elsevier Smart Health, 18, p.100139
  • Xixuan Zhao, Ziyang Liu; Emmanuel Agu; Ameya Wagh; Shubham P Jain; Clifford Lindsay; Bengisu Tulu; Diane Strong; Jiangming Kan, Fine-grained diabetic wound depth and granulation tissue amount assessment using bilinear convolutional neural network, IEEE Access 7 (2019): 179151-179162.
  • Lei Wang, Peder C. Pedersen, Emmanuel Agu, Diane Strong, Bengisu Tulu, Qian He, Area determination of diabetic foot ulcer images using a cascaded two-stage SVM based classification, IEEE Transactions on Biomedical Engineering, vol. 64, no. 9, pp. 2098-2109, Sept. 2017.
  • Lei Wang, Peder C. Pedersen, Diane Strong, Bengisu Tulu, Emmanuel Agu and Ronald Ignotz, Smartphone Based Wound Assessment System for Patients with Diabetics, IEEE Transactions on Biomedical Engineering vol. 62, no.2, February 2015

Cardiovascular Disease Assessment:

  • Monica Ahluwalia, Jacques Kpodonu, Emmanuel Agu, 2023. Risk Stratification in Hypertrophic Cardiomyopathy: Leveraging Artificial Intelligence to Provide Guidance in the Future. JACC: Advances, 2(7), p.100562
  • Monica Ahluwalia, MD FACC, Anekwe Onwuanyi, MD, Emmanuel Agu, PhD, Jacques Kpodonu, MD FACC, Advocating for a Path to Increase Diversity in Enrollment in Cardiovascular Clinical Trials. JACC: Advances, 1(5), 2022, p.100152.
  • Abdulsalam Almadani, Abhishek Shivdeo, Emmanuel Agu, and Jacques Kpodonu, Deep Video Action Recognition Models for Assessing Cardiac Function from Echocardiograms. In 2022 IEEE International Conference on Big Data (Big Data) (pp. 5189-5199). IEEE

Smartphone Gait Analyses

  • Alcohol Intoxication and Marijuana Impairment from Gait:
    • Ruojun Li, Emmanuel Agu, Atifa Sarwar, Kristin Grimone, Debra Herman, Ana Abrantes and Michael Stein, Fine-Grained Intoxicated Gait Classification using a Bi-Linear CNN, IEEE Sensors Journal, 2023.
    • Allison Borges, Celeste Caviness, Ana M. Abrantes, Debra Herman, Kristin Grimone, Emmanuel Agu, and Michael D. Stein, 2023. User-centered preferences for a gait-informed alcohol intoxication app. Mhealth, 9.
    • Marie Sillice, Michael Stein, Cynthia Battle, Lidia Meshesha, Clifford Lindsay, Emmanuel Agu, and Ana Abrantes, 2022. Exploring factors associated with mobile phone behaviors and attitudes toward technology among adults with alcohol use disorder and implications for mhealth interventions: exploratory study. JMIR formative research, 6(8), p.e32768
    • Ruojun Li, Ganesh Balakrishnan, Jiaming Nie, Yu Li, Emmanuel Agu, Kristine Grimone, Debra Herman, Ana Abrantes, Michael Stein, 2021. Estimation of blood alcohol concentration from smartphone gait data using neural networks. IEEE Access, 9, pp.61237-61255
    • Ruojun Li, Emmanuel Agu, Ganesh Balakrishnan, Debra Herman, Ana Abrantes, Michael Stein, and Jane Metrik, 2019, November. WeedGait: unobtrusive smartphone sensing of marijuana-induced gait impairment by fusing gait cycle segmentation and neural networks. In 2019 IEEE Healthcare Innovations and Point of Care Technologies, (HI-POCT) (pp. 91-94). IEEE
    • Christina Aiello and Emmanuel Agu, Investigating Postural Sway Features, Normalization and Personalization in Detecting Blood Alcohol Levels of Smartphone Users, in Proc Wireless Health Conference 2016, NIH, Bethesda, Maryland
  • Parkinsons:
    • Hamza Abujrida, Emmanuel Agu and Kaveh Pahlavan, 2023. DeepaMed: Deep learning-based medication adherence of Parkinson’s disease using smartphone gait analysis. Smart Health, 30, p.100430.
    • Hamza Abujrida, Emmanuel Agu, Kaveh Pahlavan, Machine Learning-based Motor Assessment of Parkinson’s Disease Using Postural Sway, Gait and Lifestyle Features on Crowdsourced Smartphone Data, Biomedical Physics and Engineering Express Journal 2019.
    • Hamza Abujrida, Emmanuel Agu and Kaveh Pahlavan, 2022, December. DeePaGait: Motor Assessment of Parkinson’s Disease Using a Multi-Layer 1D Convolutional Neural Network on Smartphone Gait Data. In 2022 IEEE International Conference on Big Data (Big Data) (pp. 5153-5162). IEEE
  • Osteoarthritis:
    • Wafaa S. Almuhammadi, Emmanuel Agu, Jean King, Patricia Franklin, OA-Pain-Sense: Machine Learning Prediction of Hip and Knee Osteoarthritis Pain from IMU Data, In Informatics (Vol. 9, No. 4, p. 97), 2022. Multidisciplinary Digital Publishing Institute (MDPI).

DARPA Warfighter Analytics for Smartphone Healthcare:

  • Context Recognition:
    • Wen Ge, Guanyi Mou, Emmanuel Agu and Kyumin Lee, 2024. Deep Heterogeneous Contrastive Hyper-Graph Learning for In-the-Wild Context-Aware Human Activity Recognition. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 7(4), pp.1-23
    • Apiwat Ditthapron, Adam C. Lammert, and Emmanuel O. Agu, 2023. Multi-task Deep Learning Methods for Improving Human Context Recognition from Low Sampling Rate Sensor Data. IEEE Sensors Journal
    • Apiwat Ditthapron, Adam Lammert, Emmanuel Agu. ADL-GAN, 2023. Data Augmentation to Improve In-the-wild ADL Recognition using GANs. IEEE Access
    • Walter Gerych, Luke Buquicchio, Kavin Chandrasekaran, Hamid Mansoor, Emmanuel Agu, Elke Rundensteiner, 2023. Domain Adaptation Methods for Lab-to-Field Human Context Recognition. Sensors, 23(6), p.3081
    • Abdulaziz Alajaji, Walter Gerych, Kavin Chandrasekaran, Luke Buquicchio, Hamid Mansoor, Emmanuel Agu, Elke Rundensteiner, 2022, March. Triplet-based Domain Adaptation (Triple-DARE) for Lab-to-field Human Context Recognition. In 2022 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops) (pp. 155-161). IEEE
  • Health Assessment:
    • Florina Asani, Bhoomi Patel, Srinarayan Srikanthan, Emmanuel Agu, 2023. BioscoreNet: Traumatic Brain Injury (TBI) detection using a multimodal self-attention fusion neural network and a passive bioscore monitoring framework from smartphone sensor data. Smart Health, 27, p.100352
    • Abdulaziz Alajaji, Walter Gerych, Luke Buquicchio, Kavin Chandrasekaran, Hamid Mansoor, Emmanuel Agu, and Elke Rundensteiner, 2021. Smartphone health biomarkers: Positive unlabeled learning of in-the-wild contexts. IEEE Pervasive Computing, 20(1), pp.50-61
    • Bhoomi Kalpesh Patel, Srinarayan Srikanthan, Florina Asani, and Emmanuel Agu, 2021, December. Machine learning prediction of tbi from mobility, gait, and balance patterns. In 2021 IEEE/ACM Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE) (pp. 11-22). IEEE.
    • Srinarayan Srikanthan, Florina Asani, Bhoomi Kalpesh Patel, and Emmanuel Agu, 2021, September. Smartphone TBI Sensing using Deep Embedded Clustering and Extreme Boosted Outlier Detection. In 2021 IEEE International Conference on Digital Health (ICDH) (pp. 122-132). IEEE
    • Shreesha Narasimha Murthy, Florina Asani, Srinarayan Srikanthan, and Emmanuel Agu, 2020, December. Deepseas: Smartphone-based early ailment sensing using coupled lstm autoencoders. In 2020 IEEE International Conference on Big Data (Big Data) (pp. 4911-4918). IEEE.
  • Visual Analytics for Health Sensemaking:
    • Hamid Mansoor, Walter Gerych, Abdulaziz Alajaji, Luke Buquicchio, Kavin Chandrakasekaran, Emmanuel Agu, Elke Rundensteiner, Angela Rodriguez, 2023. 2023. INPHOVIS: Interactive visual analytics for smartphone-based digital phenotyping. Visual Informatics
    • Hamid Mansoor, Walter Gerych, Luke Buquicchio, Kavin Chandrakasekaran, Elke Rundensteiner, Emmanuel Agu, 2021. ARGUS: Interactive visual analysis of disruptions in smartphone-detected Bio-Behavioral Rhythms. Visual Informatics, 5(3), pp.39-53.
    • Hamid Mansoor, Walter Gerych, Luke Buquicchio, Kavin Chandrakasekaran Emmanuel Agu, Elke Rundensteiner, 2021. Visual analytics of smartphone-sensed human behavior and health. IEEE Computer Graphics and Applications, 41(3), pp.96-104
    • Hamid Mansoor, Walter Gerych, Luke Buquicchio, Kavin Chandrasekaran, Abdulaziz Alajaji, Aidan Murphy, Elke Rundensteiner and Emmanuel Agu, 2020, December. INTOSIS: Interactive Observation of Smartphone Inferred Symptoms for In-The-Wild Data. In 2020 IEEE International Conference on Big Data (Big Data) (pp. 4882-4891). IEEE
  • Contact Tracing:
    • Zhuoran Su, Kaveh Pahlavan, Emmanuel Agu and Haowen Wei, 2022. Proximity Detection During Epidemics: Direct UWB TOA Versus Machine Learning Based RSSI, International Journal of Wireless Information Networks, 29(4), pp.480-490.
    • Oleksandr Semenov, Emmanuel Agu, Kaveh Pahlavan and Zhuoran Su, 2022. Covid-19 social distance proximity estimation using machine learning analyses of smartphone sensor data. IEEE Sensors Journal, 22(10), pp.9568-9579
    • Zhuoran Su, Kaveh Pahlavan, Emmanuel Agu, 2021. Performance evaluation of COVID-19 proximity detection using bluetooth LE signal. IEEE access, 9, pp.38891-38906

Infectious Disease Detection from Wearable Data:

  • Atifa Sarwar, Abdulsalam Almadani, and Emmanuel O. Agu, 2024. Few-shot meta-learning for pre-symptomatic detection of Covid-19 from limited health tracker data. Smart Health, p.100459.
  • Atifa Sarwar, Emmanuel Agu, and Abdulsalam Alamadani 2023. CovidRhythm: A Deep Learning Model for Passive Prediction of Covid-19 Using Biobehavioral Rhythms Derived from Wearable Physiological Data. IEEE Open Journal of Engineering in Medicine & Biology, 4, pp.21-30
  • Atifa Sarwar and Emmanuel Agu, September. Passive COVID-19 assessment using machine learning on physiological and activity data from low end wearables. In 2021 IEEE International Conference on Digital Health (ICDH) (pp. 80-90). IEEE. (best student paper award)

Neurolingustic Assessment from Speech:

  • Apiwat Ditthapron, Adam Lammert, Emmanuel Agu, 2022. Continuous TBI Monitoring from Spontaneous Speech Using Parametrized Sinc Filters and a Cascading GRU. IEEE Journal of Biomedical and Health Informatics, 26(7), pp.3517-3528
  • Apiwat Ditthapron, Emmanuel Agu, Adam Lammert, 2021. Privacy-preserving deep speaker separation for smartphone-based passive speech assessment. IEEE Open Journal of Engineering in Medicine and Biology, 2, pp.304-313
  • Apiwat Ditthapron, Adam Lammert, Emmanuel Agu, Masking Kernel for Learning Energy-Efficient Representations for Speaker Recognition and Mobile Health, Proc INTERSPEECH  2023, pages 2843-2847.
  • Apiwat Ditthapron, Emmanuel Agu, Adam Lammert, 2021, December. Learning from Limited Data for Speech-based Traumatic Brain Injury (TBI) Detection. In 2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA) (pp. 1482-1486). IEEE

Other Projects:

  • Breast Cancer from Histopathological Images:
    • David Clement, Emmanuel Agu, Muhammad Suleiman, John Obayemi, Steve Adeshina and Wole Soboyejo, Multi-class breast cancer histopathological image classification using multi-scale pooled image feature representation (mpifr) and one-versus-one support vector machines. Applied Sciences, 13(1), 2022, p.156
    • David Clement, Emmanuel Agu, John Obayemi, Steve Adeshina and Wole Soboyejo, Breast Cancer Tumor Classification using a Deep Bag of Deep Multi-Resolution Convolutional Features (BoDMCF), MDPI Informatics, (Vol. 9, No. 4, p. 91). MDPI
  • Pain Assessment from Wearable Data:
    • Atifa Sarwar, Emmanuel Agu, Justin Polcari, Jack Ciroli, Benjamin Nephew, and Jean King, 2022. PainRhythms: Machine learning prediction of chronic pain from circadian dysregulation using actigraph data—a preliminary study. Smart Health, 26, p.100344.
  • Predicting sedentary behaviors
    • Qian He and Emmanuel Agu, 2022. Context-aware probabilistic models for predicting future sedentary behaviors of smartphone users. Journal of Healthcare Informatics Research, pp.1-41
    • Qian He and Emmanuel Agu, A Rhythm Analysis-Based Model to Predict Sedentary Behaviors, in Proc. IEEE 2nd Int’l Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE) 2017, Philadelphia, PA
  • Suicide Prevention
    • Edwin Boudreaux, Elke Rundensteiner, Feifan Liu, Bo Wang, Celine Larkin, Emmanuel Agu, Samiran Ghosh, Joshua Semeter, Gregory Simon, Rachel Davis-Martin, 2021. Applying machine learning approaches to suicide prediction using healthcare data: overview and future directions. Frontiers in psychiatry, 12, p.707916
    • Ada Dogrucu, Alex Perucic, Anabella Isaro, Damon Ball, Ermal Toto, Elke Rundensteiner, Emmanuel Agu, Rachel Davis-Martin, and Edwin Boudreaux, Moodable: Instantaneous Depression Assessment using Machine Learning on Voice Samples and Retrospectively Harvested Smartphone and Social Media Data, Elsevier Smart Health Journal, vol 17, July 2020
  • Predicting Impact of Pandemics on Cardiovascular Biomarkers
    • Trusting Inekwe, Winnie Mkandawire, Brian Wee, Emmanuel Agu and Andres Colubri, Biomarker Trajectory Prediction and Causal Analysis of the Impact of the Covid-19 Pandemic on CVD Patients using Machine Learning Methods, Proc IEEE CHASE 2024.
    • Maryam Hasan, Elke Rundensteiner, Emmanuel Agu, 2021, December. DeepEmotex: classifying emotion in text messages using deep transfer learning. In 2021 IEEE International Conference on Big Data (Big Data) (pp. 5143-5152). IEEE.
    • Maryam Hasan, Elke Rundensteiner, Xiangnan Kong and Emmanuel Agu, Discover Trends in Public Emotion using Social Sensing, ACM SIGWeb Newsletter. Spring, Article 2 (March 2017), 5 pages
    • Maryam Hasan, Emmanuel Agu, Elke Rundensteiner, Using Hashtags as Labels for Supervised Learning of Emotions in Twitter Messages, in SIGKDD Health Informatics Workshop (HI-KDD), co-located with ACM SIGKDD 2014
    • Maryam Hasan, Elke Rundensteiner and Emmanuel Agu, EMOTEX: Detecting Emotions in Twitter Messages, in Proc ASE/IEEE Int’l Conference on Social Computing (Socialcom) 2014
  • Emotion from Twitter Messages
    • Maryam Hasan, Elke Rundensteiner, Emmanuel Agu, 2021, December. DeepEmotex: classifying emotion in text messages using deep transfer learning. In 2021 IEEE International Conference on Big Data (Big Data) (pp. 5143-5152). IEEE.
    • Maryam Hasan, Elke Rundensteiner, Xiangnan Kong and Emmanuel Agu, Discover Trends in Public Emotion using Social Sensing, ACM SIGWeb Newsletter. Spring, Article 2 (March 2017), 5 pages
    • Maryam Hasan, Emmanuel Agu, Elke Rundensteiner, Using Hashtags as Labels for Supervised Learning of Emotions in Twitter Messages, in SIGKDD Health Informatics Workshop (HI-KDD), co-located with ACM SIGKDD 2014
    • Maryam Hasan, Elke Rundensteiner and Emmanuel Agu, EMOTEX: Detecting Emotions in Twitter Messages, in Proc ASE/IEEE Int’l Conference on Social Computing (Socialcom) 2014