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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

Motor Activity Based Classification of Depression in Unipolar and Bipolar Patients
Enrique Garcia-Ceja, Michael A. Riegler, Petter Jakobsen, et al.
(2018)
Closed Access | Times Cited: 37

Showing 1-25 of 37 citing articles:

Mental health monitoring with multimodal sensing and machine learning: A survey
Enrique Garcia-Ceja, Michael A. Riegler, Tine Nordgreen, et al.
Pervasive and Mobile Computing (2018) Vol. 51, pp. 1-26
Open Access | Times Cited: 297
Systematic review and meta-analysis of performance of wearable artificial intelligence in detecting and predicting depression
Alaa Abd‐Alrazaq, Rawan AlSaad, Farag Shuweihdi, et al.
npj Digital Medicine (2023) Vol. 6, Iss. 1
Open Access | Times Cited: 46
Digital biomarkers of mood disorders and symptom change
Nicholas C. Jacobson, Hilary Weingarden, Sabine Wilhelm
npj Digital Medicine (2019) Vol. 2, Iss. 1
Open Access | Times Cited: 119
Applying machine learning in motor activity time series of depressed bipolar and unipolar patients compared to healthy controls
Petter Jakobsen, Enrique Garcia-Ceja, Michael A. Riegler, et al.
PLoS ONE (2020) Vol. 15, Iss. 8, pp. e0231995-e0231995
Open Access | Times Cited: 66
Prediction of impending mood episode recurrence using real-time digital phenotypes in major depression and bipolar disorders in South Korea: a prospective nationwide cohort study
Heon‐Jeong Lee, Chul‐Hyun Cho, Taek Lee, et al.
Psychological Medicine (2022) Vol. 53, Iss. 12, pp. 5636-5644
Closed Access | Times Cited: 36
Harnessing Consumer Wearable Digital Biomarkers for Individualized Recognition of Postpartum Depression Using the All of Us Research Program Data Set: Cross-Sectional Study
Eric Hurwitz, Zachary Butzin-Dozier, Hiral Master, et al.
JMIR mhealth and uhealth (2024) Vol. 12, pp. e54622-e54622
Open Access | Times Cited: 5
Applying AI in the Context of the Association Between Device-Based Assessment of Physical Activity and Mental Health: Systematic Review
Simon Woll, Dennis Birkenmaier, Gergely Biri, et al.
JMIR mhealth and uhealth (2025) Vol. 13, pp. e59660-e59660
Open Access
IMU-Based Monitoring for Assistive Diagnosis and Management of IoHT: A Review
Fan Bo, Mustafa Ozkan Yerebakan, Yanning Dai, et al.
Healthcare (2022) Vol. 10, Iss. 7, pp. 1210-1210
Open Access | Times Cited: 22
One-Dimensional Convolutional Neural Networks on Motor Activity Measurements in Detection of Depression
Joakim Ihle Frogner, Farzan Majeed Noori, Pål Halvorsen, et al.
(2019), pp. 9-15
Open Access | Times Cited: 30
Identifying digital biomarkers in actigraph based sequential motor activity data for assessment of depression: a model evaluating SVM in LSTM extracted feature space
Anshika Arora, Pinaki Chakraborty, M. P. S. Bhatia
International Journal of Information Technology (2023) Vol. 15, Iss. 2, pp. 797-802
Closed Access | Times Cited: 10
Comparison of Manual and Automated Feature Engineering for Daily Activity Classification in Mental Disorder Diagnosis
Jakub Adamczyk, Filip Malawski
Computing and Informatics (2021) Vol. 40, Iss. 4, pp. 850-879
Open Access | Times Cited: 18
Depression Level Classification Using Machine Learning Classifiers Based on Actigraphy Data
Junggu Choi, Inhwan Ko, Sanghoon Han
IEEE Access (2021) Vol. 9, pp. 116622-116646
Open Access | Times Cited: 16
Two-Dimensional Convolutional Neural Network for Depression Episodes Detection in Real Time Using Motor Activity Time Series of Depresjon Dataset
Carlos H. Espino-Salinas, Carlos E. Galván-Tejada, Huizilopoztli Luna-García, et al.
Bioengineering (2022) Vol. 9, Iss. 9, pp. 458-458
Open Access | Times Cited: 12
Machine Learning Techniques for Developing Remotely Monitored Central Nervous System Biomarkers Using Wearable Sensors: A Narrative Literature Review
Ahnjili Zhuparris, Annika A. de Goede, Iris E. Yocarini, et al.
Sensors (2023) Vol. 23, Iss. 11, pp. 5243-5243
Open Access | Times Cited: 6
Utilizing deep convolutional neural architecture with attention mechanism for objective diagnosis of schizophrenia using wearable IoMT devices
Muzafar Mehraj Misgar, M. P. S. Bhatia
Multimedia Tools and Applications (2023) Vol. 83, Iss. 13, pp. 39601-39620
Closed Access | Times Cited: 6
Identification of Schizophrenic Individuals Using Activity Records Through Visualization of Recurrent Networks
Rohit Kumar Bondugula, Kaushik Bhargav Sivangi, Siba K. Udgata
Lecture notes in networks and systems (2022), pp. 653-664
Closed Access | Times Cited: 9
A Machine Learning Based Study to Predict Depression with Monitoring Actigraph Watch Data
M. Raihan, Anupam Kumar Bairagi, Shagoto Rahman
2022 13th International Conference on Computing Communication and Networking Technologies (ICCCNT) (2021)
Closed Access | Times Cited: 12
IoT-Enabled Smart Mental Health Assessment Using Deep Hybrid Regression Models Over Actigraph-Based Sequential Motor Activity Data
Anshika Arora
Arabian Journal for Science and Engineering (2024) Vol. 49, Iss. 9, pp. 12493-12507
Closed Access | Times Cited: 1
Hopping-mean: an augmentation method for motor activity data towards real-time depression diagnosis using machine learning
Muzafar Mehraj Misgar, M. P. S. Bhatia
Multimedia Tools and Applications (2024)
Closed Access | Times Cited: 1
IMU-Based Monitoring for Assistive Disease Diagnosis and Management of IoHT: A Review
Bo Fan, Mustafa Ozkan Yerebakan, Yanning Dai, et al.
(2022)
Open Access | Times Cited: 6
Detection of Depression from IoMT Time Series Data using UMAP features
Muzafar Mehraj Misgar, M. P. S. Bhatia
2022 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS) (2022) Vol. 9, pp. 623-628
Closed Access | Times Cited: 5
Nighttime Depression Episodes Classification using a Formal Method: Knowledge Discovery in Databases
Julieta G. Rodríguez-Ruiz, Carlos E. Galván-Tejada, Sodel Vázquez-Reyes, et al.
(2019), pp. 207-212
Closed Access | Times Cited: 7
Applying machine learning in motor activity time series of depressed bipolar and unipolar patients
Petter Jakobsen, Enrique Garcia-Ceja, Michael A. Riegler, et al.
bioRxiv (Cold Spring Harbor Laboratory) (2020)
Open Access | Times Cited: 6
Deep‐SQA: A deep learning model using motor activity data for objective sleep quality assessment assisting digital wellness in healthcare 5.0
Anshika Arora, Pinaki Chakraborty, M. P. S. Bhatia, et al.
Expert Systems (2023) Vol. 41, Iss. 7
Closed Access | Times Cited: 2
Harnessing consumer wearable digital biomarkers for individualized recognition of postpartum depression using theAll of UsResearch Program dataset
Eric Hurwitz, Zachary Butzin-Dozier, Hiral Master, et al.
medRxiv (Cold Spring Harbor Laboratory) (2023)
Open Access | Times Cited: 2
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