Published on in Vol 3, No 1 (2015): Jan-Mar

A Mobile Cloud-Based Parkinson’s Disease Assessment System for Home-Based Monitoring

A Mobile Cloud-Based Parkinson’s Disease Assessment System for Home-Based Monitoring

A Mobile Cloud-Based Parkinson’s Disease Assessment System for Home-Based Monitoring

Journals

  1. Pasluosta C, Gassner H, Winkler J, Klucken J, Eskofier B. An Emerging Era in the Management of Parkinson's Disease: Wearable Technologies and the Internet of Things. IEEE Journal of Biomedical and Health Informatics 2015;19(6):1873 View
  2. Memedi M, Nyholm D, Johansson A, Palhagen S, Willows T, Widner H, Linder J, Westin J. Validity and Responsiveness of At-Home Touch Screen Assessments in Advanced Parkinson's Disease. IEEE Journal of Biomedical and Health Informatics 2015;19(6):1829 View
  3. Jeon H, Lee W, Park H, Lee H, Kim S, Kim H, Jeon B, Park K. Automatic Classification of Tremor Severity in Parkinson’s Disease Using a Wearable Device. Sensors 2017;17(9):2067 View
  4. Iakovakis D, Hadjidimitriou S, Charisis V, Bostantjopoulou S, Katsarou Z, Klingelhoefer L, Reichmann H, Dias S, Diniz J, Trivedi D, Chaudhuri K, Hadjileontiadis L. Motor Impairment Estimates via Touchscreen Typing Dynamics Toward Parkinson's Disease Detection From Data Harvested In-the-Wild. Frontiers in ICT 2018;5 View
  5. Son H, Kim H. A Pilot Study to Test the Feasibility of a Home Mobility Monitoring System in Community-Dwelling Older Adults. International Journal of Environmental Research and Public Health 2019;16(9):1512 View
  6. Wang E, Zhou L, Chen S, Hill K, Parmanto B. An mHealth Platform for Supporting Clinical Data Integration into Augmentative and Alternative Communication Service Delivery: User-Centered Design and Usability Evaluation. JMIR Rehabilitation and Assistive Technologies 2018;5(2):e14 View
  7. Jeon H, Lee W, Park H, Lee H, Kim S, Kim H, Jeon B, Park K. High-accuracy automatic classification of Parkinsonian tremor severity using machine learning method. Physiological Measurement 2017;38(11):1980 View
  8. Rovini E, Maremmani C, Cavallo F. Automated Systems Based on Wearable Sensors for the Management of Parkinson's Disease at Home: A Systematic Review. Telemedicine and e-Health 2019;25(3):167 View
  9. Mitsi G, Mendoza E, Wissel B, Barbopoulou E, Dwivedi A, Tsoulos I, Stavrakoudis A, Espay A, Papapetropoulos S. Biometric Digital Health Technology for Measuring Motor Function in Parkinson’s Disease: Results from a Feasibility and Patient Satisfaction Study. Frontiers in Neurology 2017;8 View
  10. Miocinovic S, Shoeb A, Wang S, Byrd E, Swann N, Pathak A, Ostrem J. Clinical Tremor Severity Estimation Using an Instrumented Eating Utensil. Journal of Parkinson's Disease 2017;7(4):755 View
  11. LEMOYNE R, MASTROIANNI T. IMPLEMENTATION OF A SMARTPHONE AS A WIRELESS ACCELEROMETER PLATFORM FOR QUANTIFYING HEMIPLEGIC GAIT DISPARITY IN A FUNCTIONALLY AUTONOMOUS CONTEXT. Journal of Mechanics in Medicine and Biology 2018;18(02):1850005 View
  12. Linares-del Rey M, Vela-Desojo L, Cano-de la Cuerda R. Mobile phone applications in Parkinson's disease: a systematic review. Neurología (English Edition) 2019;34(1):38 View
  13. Wannheden C, Revenäs Å. How People with Parkinson's Disease and Health Care Professionals Wish to Partner in Care Using eHealth: Co-Design Study. Journal of Medical Internet Research 2020;22(9):e19195 View
  14. Linares-del Rey M, Vela-Desojo L, Cano-de la Cuerda R. Aplicaciones móviles en la enfermedad de Parkinson: una revisión sistemática. Neurología 2019;34(1):38 View
  15. Roh E, Lee H, Kim D, Lee N. A Solution‐Processable, Omnidirectionally Stretchable, and High‐Pressure‐Sensitive Piezoresistive Device. Advanced Materials 2017;29(42) View
  16. Shah N, Aleong R, So I. Novel Use of a Smartphone to Measure Standing Balance. JMIR Rehabilitation and Assistive Technologies 2016;3(1):e4 View
  17. Wiederhold B. mHealth VR Can Transform Mental Health. Cyberpsychology, Behavior, and Social Networking 2015;18(7):365 View
  18. Son H, Park W, Kim H. Mobility monitoring using smart technologies for Parkinson’s disease in free-living environment. Collegian 2018;25(5):549 View
  19. Reinertsen E, Clifford G. A review of physiological and behavioral monitoring with digital sensors for neuropsychiatric illnesses. Physiological Measurement 2018;39(5):05TR01 View
  20. Belić M, Bobić V, Badža M, Šolaja N, Đurić-Jovičić M, Kostić V. Artificial intelligence for assisting diagnostics and assessment of Parkinson’s disease—A review. Clinical Neurology and Neurosurgery 2019;184:105442 View
  21. Rashidisabet H, Thomas P, Ajilore O, Zulueta J, Moore R, Leow A. A systems biology approach to the digital behaviorome. Current Opinion in Systems Biology 2020;20:8 View
  22. Devarajan M, Ravi L. Intelligent cyber-physical system for an efficient detection of Parkinson disease using fog computing. Multimedia Tools and Applications 2019;78(23):32695 View
  23. Mamoshina P, Ojomoko L, Yanovich Y, Ostrovski A, Botezatu A, Prikhodko P, Izumchenko E, Aliper A, Romantsov K, Zhebrak A, Ogu I, Zhavoronkov A. Converging blockchain and next-generation artificial intelligence technologies to decentralize and accelerate biomedical research and healthcare. Oncotarget 2018;9(5):5665 View
  24. Sardana P, Kalra M, Sardana A. Design, Fabrication, and Testing of an Internet Connected Intravenous Drip Monitoring Device. Journal of Sensor and Actuator Networks 2018;8(1):2 View
  25. Ornelas-Vences C, Sánchez-Fernández L, Sánchez-Pérez L, Martínez-Hernández J. Computer model for leg agility quantification and assessment for Parkinson’s disease patients. Medical & Biological Engineering & Computing 2019;57(2):463 View
  26. Ornelas-Vences C, Sanchez-Fernandez L, Sanchez-Perez L, Garza-Rodriguez A, Villegas-Bastida A. Fuzzy inference model evaluating turn for Parkinson’s disease patients. Computers in Biology and Medicine 2017;89:379 View
  27. Schneider R, Biglan K. The promise of telemedicine for chronic neurological disorders: the example of Parkinson's disease. The Lancet Neurology 2017;16(7):541 View
  28. Lee C, Kang S, Hong S, Ma H, Lee U, Kim Y, Lebedev M. A Validation Study of a Smartphone-Based Finger Tapping Application for Quantitative Assessment of Bradykinesia in Parkinson’s Disease. PLOS ONE 2016;11(7):e0158852 View
  29. Stamate C, Magoulas G, Kueppers S, Nomikou E, Daskalopoulos I, Jha A, Pons J, Rothwell J, Luchini M, Moussouri T, Iannone M, Roussos G. The cloudUPDRS app: A medical device for the clinical assessment of Parkinson’s Disease. Pervasive and Mobile Computing 2018;43:146 View
  30. Wang G, Zhou S, Rezaei S, Liu X, Huang A. An Ambulatory Blood Pressure Monitor Mobile Health System for Early Warning for Stroke Risk: Longitudinal Observational Study. JMIR mHealth and uHealth 2019;7(10):e14926 View
  31. Petrizzo D, Popolo P. Smartphone Use in Clinical Voice Recording and Acoustic Analysis: A Literature Review. Journal of Voice 2021;35(3):499.e23 View
  32. Chib A, Lin S. Theoretical Advancements in mHealth: A Systematic Review of Mobile Apps. Journal of Health Communication 2018;23(10-11):909 View
  33. Gowda G, Manjunatha N, Kulkarni K, Bagewadi V, Shyam R, Basavaraju V, Ramesh M, Nagabhushana S, Kumar C, Kulkarni G, Math S. A Collaborative Tele-Neurology Outpatient Consulation Service in Karnataka: Seven Years of Experience From a Tele-Medicine Center. Neurology India 2020;68(2):358 View
  34. Hssayeni M, Jimenez-Shahed J, Burack M, Ghoraani B. Wearable Sensors for Estimation of Parkinsonian Tremor Severity during Free Body Movements. Sensors 2019;19(19):4215 View
  35. Anderson K, Emmerton L. Contribution of mobile health applications to self-management by consumers: review of published evidence. Australian Health Review 2016;40(5):591 View
  36. Battista L, Romaniello A. A novel device for continuous monitoring of tremor and other motor symptoms. Neurological Sciences 2018;39(8):1333 View
  37. Yang X, Fan D, Ren A, Zhao N, Zhang Z, Haider D, Khan M, Tian J. Non-Contact Early Warning of Shaking Palsy. IEEE Journal of Translational Engineering in Health and Medicine 2019;7:1 View
  38. Lazarou I, Stavropoulos T, Meditskos G, Andreadis S, Kompatsiaris I, Tsolaki M. Long-Term Impact of Intelligent Monitoring Technology on People with Cognitive Impairment: An Observational Study. Journal of Alzheimer's Disease 2019;70(3):757 View
  39. Almogren A. RETRACTED ARTICLE: An automated and intelligent Parkinson disease monitoring system using wearable computing and cloud technology. Cluster Computing 2019;22(S1):2309 View
  40. Jauhiainen M, Puustinen J, Mehrang S, Ruokolainen J, Holm A, Vehkaoja A, Nieminen H. Identification of Motor Symptoms Related to Parkinson Disease Using Motion-Tracking Sensors at Home (KÄVELI): Protocol for an Observational Case-Control Study. JMIR Research Protocols 2019;8(3):e12808 View
  41. Collazo C, González Santos J, González Bernal J, Cubo E. Estado sobre la situación del uso y utilidades potenciales de las nuevas tecnologías para medir actividad física. Revisión sistemática de la literatura. Atención Primaria Práctica 2020;2(6):100064 View
  42. Abramavičius S, Venslauskas M, Vaitkus A, Gudžiūnas V, Laucius O, Stankevičius E. Local Vibrational Therapy for Essential Tremor Reduction: A Clinical Study. Medicina 2020;56(10):552 View
  43. Channa A, Ifrim R, Popescu D, Popescu N. A-WEAR Bracelet for Detection of Hand Tremor and Bradykinesia in Parkinson’s Patients. Sensors 2021;21(3):981 View
  44. Pandey B, Kumar Pandey D, Pratap Mishra B, Rhmann W. A comprehensive survey of deep learning in the field of medical imaging and medical natural language processing: Challenges and research directions. Journal of King Saud University - Computer and Information Sciences 2022;34(8):5083 View
  45. Abrami A, Gunzler S, Kilbane C, Ostrand R, Ho B, Cecchi G. Automated Computer Vision Assessment of Hypomimia in Parkinson Disease: Proof-of-Principle Pilot Study. Journal of Medical Internet Research 2021;23(2):e21037 View
  46. Mascheroni A, Choe E, Luo Y, Marazza M, Ferlito C, Caverzasio S, Mezzanotte F, Kaelin-Lang A, Faraci F, Puiatti A, Ratti P. The SleepFit Tablet Application for Home-Based Clinical Data Collection in Parkinson Disease: User-Centric Development and Usability Study. JMIR mHealth and uHealth 2021;9(6):e16304 View
  47. Hssayeni M, Jimenez-Shahed J, Burack M, Ghoraani B. Ensemble deep model for continuous estimation of Unified Parkinson’s Disease Rating Scale III. BioMedical Engineering OnLine 2021;20(1) View
  48. Fuchs C, Nobile M, Zamora G, Degeneffe A, Kubben P, Kaymak U. Tremor assessment using smartphone sensor data and fuzzy reasoning. BMC Bioinformatics 2021;22(S2) View
  49. Sá K, Souza G, Callegari B, Belgamo A, Cabral A, Gorla J, Silva A. Use of Filters to Smooth Out Signals Collected through Mobile Devices in the Static and Dynamic Balance Assessment: A Systematic Review. Applied Sciences 2022;12(13):6579 View
  50. Gopal A, Hsu W, Allen D, Bove R. Remote Assessments of Hand Function in Neurological Disorders: Systematic Review. JMIR Rehabilitation and Assistive Technologies 2022;9(1):e33157 View
  51. Morinan G, Dushin Y, Sarapata G, Rupprechter S, Peng Y, Girges C, Salazar M, Milabo C, Sibley K, Foltynie T, Cociasu I, Ricciardi L, Baig F, Morgante F, Leyland L, Weil R, Gilron R, O’Keeffe J. Computer vision quantification of whole-body Parkinsonian bradykinesia using a large multi-site population. npj Parkinson's Disease 2023;9(1) View
  52. Park Y, Kim S, So H, Jo S, Lee S, Hwang Y, Kim M, Chung S. Effect of mobile health intervention for self-management on self-efficacy, motor and non-motor symptoms, self-management, and quality of life in people with Parkinson's disease: Randomized controlled trial. Geriatric Nursing 2022;46:90 View
  53. Lee J, Yeom I, Chung M, Kim Y, Yoo S, Kim E. Use of Mobile Apps for Self-care in People With Parkinson Disease: Systematic Review. JMIR mHealth and uHealth 2022;10(1):e33944 View
  54. Tripathi S, Malhotra A, Qazi M, Chou J, Wang F, Barkan S, Hellmers N, Henchcliffe C, Sarva H. Clinical Review of Smartphone Applications in Parkinson’s Disease. The Neurologist 2022;27(4):183 View
  55. Grosjean S, Ciocca J, Gauthier-Beaupré A, Poitras E, Grimes D, Mestre T. Co-designing a digital companion with people living with Parkinson's to support self-care in a personalized way: The eCARE-PD Study. DIGITAL HEALTH 2022;8:205520762210816 View
  56. Lai Y, Lien C, Huang C, Lin W, Chen Y, Yu C, Cheng B, Kung C, Kung C, Chiang Y, Hung Y, Chang H, Lu C. Clinical Disease Severity Mediates the Relationship between Stride Length and Speed and the Risk of Falling in Parkinson’s Disease. Journal of Personalized Medicine 2022;12(2):192 View
  57. Morinan G, Peng Y, Rupprechter S, Weil R, Leyland L, Foltynie T, Sibley K, Baig F, Morgante F, Gilron R, Wilt R, Starr P, O'Keeffe J. Computer-vision based method for quantifying rising from chair in Parkinson's disease patients. Intelligence-Based Medicine 2022;6:100046 View
  58. Gauthier-Lafreniere E, Aljassar M, Rymar V, Milton J, Sadikot A. A standardized accelerometry method for characterizing tremor: Application and validation in an ageing population with postural and action tremor. Frontiers in Neuroinformatics 2022;16 View
  59. Palumbo A, Ielpo N, Calabrese B, Corchiola D, Garropoli R, Gramigna V, Perri G. SIMpLE: A Mobile Cloud-Based System for Health Monitoring of People with ALS. Sensors 2021;21(21):7239 View
  60. Anbalagan B, Karnam Anantha S, Kalpana R. Novel Approach to Prognosis Parkinson’s Disease with Wireless Technology Using Resting Tremors. Wireless Personal Communications 2022;125(4):2985 View
  61. Vanitha K, Talasila V. Machine Learning Techniques for Automated Tremor Detection in the Presence of External Stressors. International Journal of Circuits, Systems and Signal Processing 2022;16:551 View
  62. Koyama T, Matsui R, Yamamoto A, Yamada E, Norose M, Ibara T, Kaburagi H, Nimura A, Sugiura Y, Saito H, Okawa A, Fujita K. High-Dimensional Analysis of Finger Motion and Screening of Cervical Myelopathy With a Noncontact Sensor: Diagnostic Case-Control Study. JMIR Biomedical Engineering 2022;7(2):e41327 View
  63. Moreta-de-Esteban P, Martín-Casas P, Ortiz-Gutiérrez R, Straudi S, Cano-de-la-Cuerda R. Mobile Applications for Resting Tremor Assessment in Parkinson’s Disease: A Systematic Review. Journal of Clinical Medicine 2023;12(6):2334 View
  64. Zou Y, Lai Y, Chiu W, Lien C, Huang C, Cheng B, Lin W, Chen Y, Yu C, Chiang Y, Kung C, Kung C, Lu C. Clinical Utility of Plantar Pressure Measurements as Screening in Patients With Parkinson Disease With and Without Freezing of Gait History. Archives of Physical Medicine and Rehabilitation 2023;104(7):1091 View
  65. Newaz N, Hanada E. The Methods of Fall Detection: A Literature Review. Sensors 2023;23(11):5212 View
  66. Ferreira V, Metting E, Schauble J, Seddighi H, Beumeler L, Gallo V. eHealth tools to assess the neurological function for research, in absence of the neurologist – a systematic review, part I (software). Journal of Neurology 2023 View
  67. Paredes-Acuna N, Utpadel-Fischler D, Ding K, Thakor N, Cheng G. Upper limb intention tremor assessment: opportunities and challenges in wearable technology. Journal of NeuroEngineering and Rehabilitation 2024;21(1) View
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Books/Policy Documents

  1. LeMoyne R, Mastroianni T. Wireless MEMS Networks and Applications. View
  2. Senders J, Maher N, Hulsbergen A, Lamba N, Bredenoord A, Broekman M. Ethics of Innovation in Neurosurgery. View
  3. Madeira R, Pereira C, Clipei S, Macedo P. Pervasive Computing Paradigms for Mental Health. View
  4. Gard P, Lalanne L, Ambourg A, Rousseau D, Lesueur F, Frindel C. Internet of Things (IoT) Technologies for HealthCare. View
  5. LeMoyne R, Mastroianni T. Wearable and Wireless Systems for Healthcare I. View
  6. Zamora G, Fuchs C, Degeneffe A, Kubben P, Kaymak U. Computational Intelligence Methods for Bioinformatics and Biostatistics. View
  7. Intas G, Platis C, Stergiannis P. Handbook of Computational Neurodegeneration. View
  8. Torres-Ruiz M, Guzmán G, Moreno-Ibarra M, Acosta-Arenas A. Artificial Intelligence and Big Data Analytics for Smart Healthcare. View
  9. Mieronkoski R, Azimi I, Sequeira L, Peltonen L. Smart Home Technologies and Services for Geriatric Rehabilitation. View
  10. Channa A, Popescu N. Deep Learning in Smart eHealth Systems. View
  11. Channa A, Popescu N. Deep Learning in Smart eHealth Systems. View
  12. Intas G, Platis C, Stergiannis P. Handbook of Computational Neurodegeneration. View
  13. LeMoyne R, Mastroianni T. Wearable and Wireless Systems for Healthcare I. View