Published on in Vol 8, No 3 (2019): March

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/12808, first published .
Identification of Motor Symptoms Related to Parkinson Disease Using Motion-Tracking Sensors at Home (KÄVELI): Protocol for an Observational Case-Control Study

Identification of Motor Symptoms Related to Parkinson Disease Using Motion-Tracking Sensors at Home (KÄVELI): Protocol for an Observational Case-Control Study

Identification of Motor Symptoms Related to Parkinson Disease Using Motion-Tracking Sensors at Home (KÄVELI): Protocol for an Observational Case-Control Study

Journals

  1. 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
  2. Juutinen M, Wang C, Zhu J, Haladjian J, Ruokolainen J, Puustinen J, Vehkaoja A, Dimitriadis S. Parkinson’s disease detection from 20-step walking tests using inertial sensors of a smartphone: Machine learning approach based on an observational case-control study. PLOS ONE 2020;15(7):e0236258 View
  3. Lu R, Xu Y, Li X, Fan Y, Zeng W, Tan Y, Ren K, Chen W, Cao X. Evaluation of Wearable Sensor Devices in Parkinson’s Disease: A Review of Current Status and Future Prospects. Parkinson's Disease 2020;2020:1 View
  4. Jung S, Michaud M, Oudre L, Dorveaux E, Gorintin L, Vayatis N, Ricard D. The Use of Inertial Measurement Units for the Study of Free Living Environment Activity Assessment: A Literature Review. Sensors 2020;20(19):5625 View
  5. Ruokolainen J, Nätti S, Juutinen M, Puustinen J, Holm A, Vehkaoja A, Nieminen H. Digital healthcare platform ecosystem design: A case study of an ecosystem for Parkinson's disease patients. Technovation 2023;120:102551 View
  6. Ileșan R, Cordoș C, Mihăilă L, Fleșar R, Popescu A, Perju-Dumbravă L, Faragó P. Proof of Concept in Artificial-Intelligence-Based Wearable Gait Monitoring for Parkinson’s Disease Management Optimization. Biosensors 2022;12(4):189 View
  7. Etumusei J, Martinez J, McClean S, Fu H. A Novel Martingale Based Model Using a Smartphone to Detect Gait Bout in Human Activity Recognition. Journal of Sensors 2022;2022:1 View
  8. Krokidis M, Dimitrakopoulos G, Vrahatis A, Tzouvelekis C, Drakoulis D, Papavassileiou F, Exarchos T, Vlamos P. A Sensor-Based Perspective in Early-Stage Parkinson’s Disease: Current State and the Need for Machine Learning Processes. Sensors 2022;22(2):409 View
  9. Soundararajan R, Prabu A, Routray S, Malla P, Ray A, Palai G, Faragallah O, Baz M, Abualnaja M, Eid M, Rashed A. Deeply Trained Real-Time Body Sensor Networks for Analyzing the Symptoms of Parkinson’s Disease. IEEE Access 2022;10:63403 View
  10. Zhuparris A, Maleki G, Koopmans I, Doll R, Voet N, Kraaij W, Cohen A, van Brummelen E, De Maeyer J, Groeneveld G. Smartphone and Wearable Sensors for the Estimation of Facioscapulohumeral Muscular Dystrophy Disease Severity: Cross-sectional Study. JMIR Formative Research 2023;7:e41178 View
  11. Li Z, Cui Y, Gu Y, Wang G, Yang J, Chen K, Cao H. Temperature Drift Compensation for Four-Mass Vibration MEMS Gyroscope Based on EMD and Hybrid Filtering Fusion Method. Micromachines 2023;14(5):971 View
  12. Dang X, Li W, Zou J, Cong B, Guan Y. Assessing the impact of body location on the accuracy of detecting daily activities with accelerometer data. iScience 2024;27(2):108626 View