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

Focus Groups and Qualitative Research for Human Factors Research (1220) mHealth for Wellness, Behavior Change and Prevention (3315) Home Care (215) Wearable Devices and Sensors (1133) Ubiquitous Health (uHealth) (134) Aging in Place (389)

Published on in Vol 9, No 3 (2022): Jul-Sep

This is a member publication of University of Bristol (Jisc)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/36370, first published .
Acceptability of an In-home Multimodal Sensor Platform for Parkinson Disease: Nonrandomized Qualitative Study

Acceptability of an In-home Multimodal Sensor Platform for Parkinson Disease: Nonrandomized Qualitative Study

Acceptability of an In-home Multimodal Sensor Platform for Parkinson Disease: Nonrandomized Qualitative Study

Authors of this article:

Catherine Morgan1, 2 Author Orcid Image ;   Emma L Tonkin3 Author Orcid Image ;   Ian Craddock3 Author Orcid Image ;   Alan L Whone1, 2 Author Orcid Image
Article Authors Cited by (12) Tweetations (3) Metrics

Journals

  1. Morgan C, Jameson J, Craddock I, Tonkin E, Oikonomou G, Isotalus H, Heidarivincheh F, McConville R, Tourte G, Kinnunen K, Whone A. Understanding how people with Parkinson's disease turn in gait from a real-world in-home dataset. Parkinsonism & Related Disorders 2022;105:114 View
  2. Morgan C, Tonkin E, Masullo A, Jovan F, Sikdar A, Khaire P, Mirmehdi M, McConville R, Tourte G, Whone A, Craddock I. A multimodal dataset of real world mobility activities in Parkinson’s disease. Scientific Data 2023;10(1) View
  3. Morgan C, Masullo A, Mirmehdi M, Isotalus H, Jovan F, McConville R, Tonkin E, Whone A, Craddock I. Automated Real-World Video Analysis of Sit-to-Stand Transitions Predicts Parkinson’s Disease Severity. Digital Biomarkers 2023:92 View
  4. Khoo L, Lim M, Chong C, McNaney R. Machine Learning for Multimodal Mental Health Detection: A Systematic Review of Passive Sensing Approaches. Sensors 2024;24(2):348 View
  5. Popp Z, Low S, Igwe A, Rahman M, Kim M, Khan R, Oh E, Kumar A, De Anda‐Duran I, Ding H, Hwang P, Sunderaraman P, Shih L, Lin H, Kolachalama V, Au R. Shifting From Active to Passive Monitoring of Alzheimer Disease: The State of the Research. Journal of the American Heart Association 2024;13(2) View
  6. Goubault E, Duval C, Martin C, Lebel K. Innovative Detection and Segmentation of Mobility Activities in Patients Living with Parkinson’s Disease Using a Single Ankle-Positioned Smartwatch. Sensors 2024;24(17):5486 View
  7. Stefani A, Antelmi E, Arnaldi D, Arnulf I, During E, Högl B, Hu M, Iranzo A, Luke R, Peever J, Postuma R, Videnovic A, Gan-Or Z. From mechanisms to future therapy: a synopsis of isolated REM sleep behavior disorder as early synuclein-related disease. Molecular Neurodegeneration 2025;20(1) View
  8. Rookes T, Batla A, Armstrong M, Ambler G, Walters K, Schrag A. Patient Acceptability and Technical Reliability of Wearable Devices Used for Monitoring People With Parkinson Disease: Survey Study. JMIR Formative Research 2025;9:e63704 View
  9. Cheng Q, Morgan C, Sikdar A, Masullo A, Whone A, Mirmehdi M. Your turn: At home turning angle estimation for Parkinson’s disease severity assessment. Artificial Intelligence in Medicine 2025;167:103194 View
  10. Farabolini G, Baldini N, Pagano A, Andrenelli E, Pepa L, Morone G, Ceravolo M, Capecci M. Continuous Movement Monitoring at Home Through Wearable Devices: A Systematic Review. Sensors 2025;25(16):4889 View

Books/Policy Documents

  1. . Digital Technologies in Movement Disorders. View

Conference Proceedings

  1. Liu S. 2024 International Conference on Computers, Information Processing and Advanced Education (CIPAE). Enhancing Elderly Safety: A Machine Learning-Driven Acoustic System for In-Home Fall Detection and Alerting View