Published on in Vol 7, No 2 (2020): Jul-Dec

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/17986, first published .
The Impact of Reducing the Number of Wearable Devices on Measuring Gait in Parkinson Disease: Noninterventional Exploratory Study

The Impact of Reducing the Number of Wearable Devices on Measuring Gait in Parkinson Disease: Noninterventional Exploratory Study

The Impact of Reducing the Number of Wearable Devices on Measuring Gait in Parkinson Disease: Noninterventional Exploratory Study

Journals

  1. Williamson J, Telfer B, Mullany R, Friedl K. Detecting Parkinson’s Disease from Wrist-Worn Accelerometry in the U.K. Biobank. Sensors 2021;21(6):2047 View
  2. Castiglia S, Tatarelli A, Trabassi D, De Icco R, Grillo V, Ranavolo A, Varrecchia T, Magnifica F, Di Lenola D, Coppola G, Ferrari D, Denaro A, Tassorelli C, Serrao M. Ability of a Set of Trunk Inertial Indexes of Gait to Identify Gait Instability and Recurrent Fallers in Parkinson’s Disease. Sensors 2021;21(10):3449 View
  3. Rastegari E, Ali H, Marmelat V. Detection of Parkinson’s Disease Using Wrist Accelerometer Data and Passive Monitoring. Sensors 2022;22(23):9122 View
  4. Sugiyama Y, Uno K, Matsui Y, Lockhart T. Types of anomalies in two-dimensional video-based gait analysis in uncontrolled environments. PLOS Computational Biology 2023;19(1):e1009989 View
  5. GURCHIEK R, BEYNNON B, AGRESTA C, CHOQUETTE R, MCGINNIS R. Wearable sensors for remote patient monitoring in orthopedics. Minerva Orthopedics 2021;72(5) View
  6. Safarpour D, Dale M, Shah V, Talman L, Carlson-Kuhta P, Horak F, Mancini M. Surrogates for rigidity and PIGD MDS-UPDRS subscores using wearable sensors. Gait & Posture 2022;91:186 View
  7. Manta C, Mahadevan N, Bakker J, Ozen Irmak S, Izmailova E, Park S, Poon J, Shevade S, Valentine S, Vandendriessche B, Webster C, Goldsack J. EVIDENCE Publication Checklist for Studies Evaluating Connected Sensor Technologies: Explanation and Elaboration. Digital Biomarkers 2021;5(2):127 View
  8. Trabassi D, Serrao M, Varrecchia T, Ranavolo A, Coppola G, De Icco R, Tassorelli C, Castiglia S. Machine Learning Approach to Support the Detection of Parkinson’s Disease in IMU-Based Gait Analysis. Sensors 2022;22(10):3700 View
  9. Zhang T, Meng D, Lyu D, Fang B. The Efficacy of Wearable Cueing Devices on Gait and Motor Function in Parkinson Disease: A Systematic Review and Meta-analysis of Randomized Controlled Trials. Archives of Physical Medicine and Rehabilitation 2024;105(2):369 View
  10. Ymeri G, Salvi D, Olsson C, Wassenburg M, Tsanas A, Svenningsson P. Quantifying Parkinson’s disease severity using mobile wearable devices and machine learning: the ParkApp pilot study protocol. BMJ Open 2023;13(12):e077766 View
  11. Zampogna A, Borzì L, Rinaldi D, Artusi C, Imbalzano G, Patera M, Lopiano L, Pontieri F, Olmo G, Suppa A. Unveiling the Unpredictable in Parkinson’s Disease: Sensor-Based Monitoring of Dyskinesias and Freezing of Gait in Daily Life. Bioengineering 2024;11(5):440 View

Books/Policy Documents

  1. Seemann J, Loris T, Weber L, Synofzik M, Giese M, Ilg W. Artificial Neural Networks and Machine Learning – ICANN 2023. View