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Citing this Article

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Published on 25.05.17 in Vol 19, No 5 (2017): May

This paper is in the following e-collection/theme issue:

Works citing "Activity Recognition for Persons With Stroke Using Mobile Phone Technology: Toward Improved Performance in a Home Setting"

Megan K O'Brien, Nicholas Shawen, Chaithanya K Mummidisetty, Saninder Kaur, Xiao Bo, Christian Poellabauer, Konrad Kording, Arun Jayaraman

J Med Internet Res 2017 (May 25); 19(5):e184 

According to Crossref, the following articles are citing this article (DOI 10.2196/jmir.7385):

(note that this is only a small subset of citations)

  1. Negrini F, Gasperini G, Guanziroli E, Vitale JA, Banfi G, Molteni F. Using an Accelerometer-Based Step Counter in Post-Stroke Patients: Validation of a Low-Cost Tool. International Journal of Environmental Research and Public Health 2020;17(9):3177
    CrossRef
  2. Pradeepa S, Manjula KR, Vimal S, Khan MS, Chilamkurti N, Luhach AK. DRFS: Detecting Risk Factor of Stroke Disease from Social Media Using Machine Learning Techniques. Neural Processing Letters 2023;55(4):3843
    CrossRef
  3. Tomšič M, Domajnko B, Zajc M. The use of assistive technologies after stroke is debunking the myths about the elderly. Topics in Stroke Rehabilitation 2018;25(1):28
    CrossRef
  4. Shawen N, O’Brien MK, Venkatesan S, Lonini L, Simuni T, Hamilton JL, Ghaffari R, Rogers JA, Jayaraman A. Role of data measurement characteristics in the accurate detection of Parkinson’s disease symptoms using wearable sensors. Journal of NeuroEngineering and Rehabilitation 2020;17(1)
    CrossRef
  5. Reinkensmeyer DJ, Blackstone S, Bodine C, Brabyn J, Brienza D, Caves K, DeRuyter F, Durfee E, Fatone S, Fernie G, Gard S, Karg P, Kuiken TA, Harris GF, Jones M, Li Y, Maisel J, McCue M, Meade MA, Mitchell H, Mitzner TL, Patton JL, Requejo PS, Rimmer JH, Rogers WA, Zev Rymer W, Sanford JA, Schneider L, Sliker L, Sprigle S, Steinfeld A, Steinfeld E, Vanderheiden G, Winstein C, Zhang L, Corfman T. How a diverse research ecosystem has generated new rehabilitation technologies: Review of NIDILRR’s Rehabilitation Engineering Research Centers. Journal of NeuroEngineering and Rehabilitation 2017;14(1)
    CrossRef
  6. Ma Y, Zhang P, Tang Y, Pan C, Li G, Liu N, Hu Y, Tang Z. Artificial intelligence: The dawn of a new era for cutting-edge technology based diagnosis and treatment for stroke. Brain Hemorrhages 2020;1(1):1
    CrossRef
  7. Albert MV, Sugianto A, Nickele K, Zavos P, Sindu P, Ali M, Kwon S. Hidden Markov model-based activity recognition for toddlers. Physiological Measurement 2020;41(2):025003
    CrossRef
  8. Antos SA, Danilovich MK, Eisenstein AR, Gordon KE, Kording KP. Smartwatches Can Detect Walker and Cane Use in Older Adults. Innovation in Aging 2019;3(1)
    CrossRef
  9. Liao Y, Vakanski A, Xian M, Paul D, Baker R. A review of computational approaches for evaluation of rehabilitation exercises. Computers in Biology and Medicine 2020;119:103687
    CrossRef
  10. Zhang Y, Zhou Y, Zhang D, Song W. A Stroke Risk Detection: Improving Hybrid Feature Selection Method. Journal of Medical Internet Research 2019;21(4):e12437
    CrossRef
  11. Lonini L, Gupta A, Deems-Dluhy S, Hoppe-Ludwig S, Kording K, Jayaraman A. Activity Recognition in Individuals Walking With Assistive Devices: The Benefits of Device-Specific Models. JMIR Rehabilitation and Assistive Technologies 2017;4(2):e8
    CrossRef
  12. Shawen N, Lonini L, Mummidisetty CK, Shparii I, Albert MV, Kording K, Jayaraman A. Fall Detection in Individuals With Lower Limb Amputations Using Mobile Phones: Machine Learning Enhances Robustness for Real-World Applications. JMIR mHealth and uHealth 2017;5(10):e151
    CrossRef
  13. Porciuncula F, Roto AV, Kumar D, Davis I, Roy S, Walsh CJ, Awad LN. Wearable Movement Sensors for Rehabilitation: A Focused Review of Technological and Clinical Advances. PM&R 2018;10(9S2)
    CrossRef
  14. Rast FM, Labruyère R. Systematic review on the application of wearable inertial sensors to quantify everyday life motor activity in people with mobility impairments. Journal of NeuroEngineering and Rehabilitation 2020;17(1)
    CrossRef
  15. Luvizutto GJ, Silva GF, Nascimento MR, Sousa Santos KC, Appelt PA, de Moura Neto E, de Souza JT, Wincker FC, Miranda LA, Hamamoto Filho PT, de Souza LAPS, Simões RP, de Oliveira Vidal EI, Bazan R. Use of artificial intelligence as an instrument of evaluation after stroke: a scoping review based on international classification of functioning, disability and health concept. Topics in Stroke Rehabilitation 2022;29(5):331
    CrossRef
  16. Lonini L, Shawen N, Hoppe-Ludwig S, Deems-Dluhy S, Mummidisetty CK, Eisenberg Y, Jayaraman A. Combining Accelerometer and GPS Features to Evaluate Community Mobility in Knee Ankle Foot Orthoses (KAFO) Users. IEEE Transactions on Neural Systems and Rehabilitation Engineering 2021;29:1386
    CrossRef
  17. Veerubhotla A, Krantz A, Ibironke O, Pilkar R. Wearable devices for tracking physical activity in the community after an acquired brain injury: A systematic review. PM&R 2022;14(10):1207
    CrossRef
  18. Li Q, Liu Y, Zhu J, Chen Z, Liu L, Yang S, Zhu G, Zhu B, Li J, Jin R, Tao J, Chen L. Upper-Limb Motion Recognition Based on Hybrid Feature Selection: Algorithm Development and Validation. JMIR mHealth and uHealth 2021;9(9):e24402
    CrossRef
  19. Celik Y, Aslan M, Sabanci K, Stuart S, Woo W, Godfrey A. Making good use of inertial data: Towards better identification of free-living mobility recognition. Gait & Posture 2022;97:S309
    CrossRef
  20. Botonis OK, Harari Y, Embry KR, Mummidisetty CK, Riopelle D, Giffhorn M, Albert MV, Heike V, Jayaraman A. Wearable airbag technology and machine learned models to mitigate falls after stroke. Journal of NeuroEngineering and Rehabilitation 2022;19(1)
    CrossRef
  21. El Marhraoui Y, Amroun H, Boukallel M, Anastassova M, Lamy S, Bouilland S, Ammi M. Foot-to-Ground Phases Detection: A Comparison of Data Representation Formatting Methods with Respect to Adaption of Deep Learning Architectures. Computers 2022;11(5):58
    CrossRef
  22. Pohl J, Ryser A, Veerbeek JM, Verheyden G, Vogt JE, Luft AR, Easthope CA. Accuracy of gait and posture classification using movement sensors in individuals with mobility impairment after stroke. Frontiers in Physiology 2022;13
    CrossRef
  23. Suri A, VanSwearingen J, Dunlap P, Redfern MS, Rosso AL, Sejdić E. Facilitators and barriers to real-life mobility in community-dwelling older adults: a narrative review of accelerometry- and global positioning system-based studies. Aging Clinical and Experimental Research 2022;34(8):1733
    CrossRef
  24. Celik Y, Aslan MF, Sabanci K, Stuart S, Woo WL, Godfrey A. Improving Inertial Sensor-Based Activity Recognition in Neurological Populations. Sensors 2022;22(24):9891
    CrossRef
  25. Kulkarni P, Kirkham R, McNaney R. Opportunities for Smartphone Sensing in E-Health Research: A Narrative Review. Sensors 2022;22(10):3893
    CrossRef
  26. Mathunny JJ, Karthik V, Devaraj A, Jacob J. A scoping review on recent trends in wearable sensors to analyze gait in people with stroke: From sensor placement to validation against gold-standard equipment. Proceedings of the Institution of Mechanical Engineers, Part H: Journal of Engineering in Medicine 2023;237(3):309
    CrossRef
  27. Suprayitno E, Kustiningsih K, Ismail S. Stroke patients' neurorehabilitation. Kontakt 2023;25(2):131
    CrossRef
  28. Stock R, Gaarden AP, Langørgen E. The potential of wearable technology to support stroke survivors’ motivation for home exercise – Focus group discussions with stroke survivors and physiotherapists. Physiotherapy Theory and Practice 2023;:1
    CrossRef
  29. Celik Y, Moore J, Durgun M, Stuart S, Woo WL, Godfrey A. Gait on the Edge: A Proposed Wearable for Continuous Real-Time Monitoring Beyond the Laboratory. IEEE Sensors Journal 2023;23(23):29656
    CrossRef
  30. Rigot SK, Maronati R, Lettenberger A, O'Brien MK, Alamdari K, Hoppe-Ludwig S, McGuire M, Looft JM, Wacek A, Cave J, Sauerbrey M, Jayaraman A. Validation of Proprietary and Novel Step-counting Algorithms for Individuals Ambulating With a Lower Limb Prosthesis. Archives of Physical Medicine and Rehabilitation 2024;105(3):546
    CrossRef
  31. Lanotte F, O’Brien MK, Jayaraman A. AI in Rehabilitation Medicine: Opportunities and Challenges. Annals of Rehabilitation Medicine 2023;47(6):444
    CrossRef
  32. Oh Y, Choi S, Shin Y, Jeong Y, Lim J, Kim S. Investigating Activity Recognition for Hemiparetic Stroke Patients Using Wearable Sensors: A Deep Learning Approach with Data Augmentation. Sensors 2023;24(1):210
    CrossRef
  33. . Data Augmentation Techniques for Accurate Action Classification in Stroke Patients with Hemiparesis. Sensors 2024;24(5):1618
    CrossRef
  34. Sengupta N, Rao AS, Yan B, Palaniswami M. A Survey of Wearable Sensors and Machine Learning Algorithms for Automated Stroke Rehabilitation. IEEE Access 2024;12:36026
    CrossRef
  35. Bremm RP, Pavelka L, Garcia MM, Mombaerts L, Krüger R, Hertel F. Sensor-Based Quantification of MDS-UPDRS III Subitems in Parkinson’s Disease Using Machine Learning. Sensors 2024;24(7):2195
    CrossRef

According to Crossref, the following books are citing this article (DOI 10.2196/jmir.7385):

  1. Vitale JA, Negrini F, Banfi G. Osteosarcopenia: Bone, Muscle and Fat Interactions. 2019. Chapter 15:345
    CrossRef
  2. Xiao T, Albert MV. Artificial Intelligence in Brain and Mental Health: Philosophical, Ethical & Policy Issues. 2021. Chapter 2:11
    CrossRef