Assessing the clinical utility of inertial sensors for home monitoring in Parkinson's disease: a comprehensive review.
Journal
NPJ Parkinson's disease
ISSN: 2373-8057
Titre abrégé: NPJ Parkinsons Dis
Pays: United States
ID NLM: 101675390
Informations de publication
Date de publication:
20 Aug 2024
20 Aug 2024
Historique:
received:
27
11
2023
accepted:
24
07
2024
medline:
21
8
2024
pubmed:
21
8
2024
entrez:
20
8
2024
Statut:
epublish
Résumé
This review screened 296 articles on wearable sensors for home monitoring of people with Parkinson's Disease within the PubMed Database, from January 2017 to May 2023. A three-level maturity framework was applied for classifying the aims of 59 studies included: demonstrating technical efficacy, diagnostic sensitivity, or clinical utility. As secondary analysis, user experience (usability and patient adherence) was evaluated. The evidences provided by the studies were categorized and stratified according to the level of maturity. Our results indicate that approximately 75% of articles investigated diagnostic sensitivity, i.e. correlation of sensor-data with clinical parameters. Evidence of clinical utility, defined as improvement on health outcomes or clinical decisions after the use of the wearables, was found only in nine papers. A third of the articles included reported evidence of user experience. Future research should focus more on clinical utility, to facilitate the translation of research results within the management of Parkinson's Disease.
Identifiants
pubmed: 39164257
doi: 10.1038/s41531-024-00755-6
pii: 10.1038/s41531-024-00755-6
doi:
Types de publication
Journal Article
Langues
eng
Pagination
161Subventions
Organisme : Fonds National de la Recherche Luxembourg (National Research Fund)
ID : 14146272
Organisme : Fonds National de la Recherche Luxembourg (National Research Fund)
ID : 14146272
Organisme : Fonds National de la Recherche Luxembourg (National Research Fund)
ID : 14146272
Organisme : Fonds National de la Recherche Luxembourg (National Research Fund)
ID : 17981757
Organisme : Fonds National de la Recherche Luxembourg (National Research Fund)
ID : 14146272
Organisme : Fonds National de la Recherche Luxembourg (National Research Fund)
ID : 14146272
Informations de copyright
© 2024. The Author(s).
Références
Del Din, S., Godfrey, A., Mazzà, C., Lord, S. & Rochester, L. Free-living monitoring of Parkinson’s disease: lessons from the field. Mov. Disord. 31, 1293–1313 (2016).
pubmed: 27452964
doi: 10.1002/mds.26718
Monje, M. H. G., Foffani, G., Obeso, J. & Sánchez-Ferro, Á. New sensor and wearable technologies to aid in the diagnosis and treatment monitoring of Parkinson’s disease. Annu. Rev. Biomed. Eng. 21, 111–143 (2019).
pubmed: 31167102
doi: 10.1146/annurev-bioeng-062117-121036
Adam, H. et al. An update on pathogenesis and clinical scenario for Parkinson’s disease: diagnosis and treatment. 3 Biotech 13, 142 (2023).
pubmed: 37124989
pmcid: 10134733
doi: 10.1007/s13205-023-03553-8
Zafar, S. & Yaddanapudi, S. S. Parkinson Disease, in StatPearls (Treasure Island (FL), 2024).
DeMaagd, G. & Philip, A. Parkinson’s disease and its management: part 1: disease entity, risk factors. Pathophysiol. Clin. Present. Diagnosis. P t 40, 504–532 (2015).
Bloem, B. R., Okun, M. S. & Klein, C. Parkinson’s disease. Lancet 397, 2284–2303 (2021).
pubmed: 33848468
doi: 10.1016/S0140-6736(21)00218-X
Hauser, R. A. et al. A home diary to assess functional status in patients with parkinson’s disease with motor fluctuations and dyskinesia. Clin. Neuropharmacol. 23, 75–81 (2000).
pubmed: 10803796
doi: 10.1097/00002826-200003000-00003
Erb, M. K. et al.Health and wearable technology should replace motor diaries to track motor fluctuations in Parkinson’s disease. npj Digital Med. 3, 6 (2020).
doi: 10.1038/s41746-019-0214-x
Sigcha, L. et al. Deep learning and wearable sensors for the diagnosis and monitoring of Parkinson’s disease: a systematic review. Expert Syst. Appl. 229, 120541 (2023).
doi: 10.1016/j.eswa.2023.120541
Antonini, A. et al. Toward objective monitoring of Parkinson’s disease motor symptoms using a wearable device: wearability and performance evaluation of PDMonitor(®). Front. Neurol. 14, 1080752 (2023).
pubmed: 37260606
pmcid: 10228366
doi: 10.3389/fneur.2023.1080752
Asci, F. et al. Wearable electrochemical sensors in Parkinson’s disease. Sens. (Basel) 22, 951 (2022).
doi: 10.3390/s22030951
Luis-Martínez, R., Monje, M. H. G., Antonini, A., Sánchez-Ferro, Á. & Mestre, T. A. Technology-enabled care: integrating multidisciplinary care in Parkinson’s disease through digital technology. Front. Neurol. 11, 575975 (2020).
pubmed: 33250846
pmcid: 7673441
doi: 10.3389/fneur.2020.575975
Moreau, C. et al. Overview on wearable sensors for the management of Parkinson’s disease. NJP Parkinson’s Dis. 9, 153 (2023).
doi: 10.1038/s41531-023-00585-y
Xu, S., Kim, J., Walter, J. R., Ghaffari, R. & Rogers, J. A. Translational gaps and opportunities for medical wearables in digital health. Sci. Transl. Med. 14, eabn6036 (2022).
Fryback, D. G. & Thornbury, J. R. The efficacy of diagnostic imaging. Med Decis. Mak. 11, 88–94 (1991).
doi: 10.1177/0272989X9101100203
Goldsack, J. C. et al. Verification, analytical validation, and clinical validation (V3): the foundation of determining fit-for-purpose for biometric monitoring technologies (BioMeTs). NJP Digit. Med. 3, 55 (2020).
doi: 10.1038/s41746-020-0260-4
Page, M. J. et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. Bmj 372, n71 (2021).
pubmed: 33782057
pmcid: 8005924
doi: 10.1136/bmj.n71
Botros, A. et al. Long-term home-monitoring sensor technology in patients with Parkinson’s disease-acceptance and adherence. Sensors (Basel) 19, 5169 (2019).
Cohen, S. et al. Characterizing patient compliance over six months in remote digital trials of Parkinson’s and Huntington disease. BMC Med Inf. Decis. Mak. 18, 138 (2018).
doi: 10.1186/s12911-018-0714-7
Silva de Lima, A. L. et al. Feasibility of large-scale deployment of multiple wearable sensors in Parkinson’s disease. PLoS One 12, e0189161 (2017).
Abrami, A. et al. Using an unbiased symbolic movement representation to characterize Parkinson’s disease states. Sci. Rep. 10, 7377 (2020).
pubmed: 32355166
pmcid: 7193555
doi: 10.1038/s41598-020-64181-3
Ravichandran, V. et al. iTex gloves: design and in-home evaluation of an e-textile glove system for tele-assessment of Parkinson’s disease. Sensors (Basel) 23, 2877 (2023).
Raykov, Y. P. et al. Probabilistic modelling of gait for robust passive monitoring in daily life. IEEE J. Biomed. Health Inf. 25, 2293–2304 (2021).
doi: 10.1109/JBHI.2020.3037857
Nouriani, A. et al. Real world validation of activity recognition algorithm and development of novel behavioral biomarkers of falls in aged control and movement disorder patients. Front. Aging Neurosci. 15, 1117802 (2023).
pubmed: 36909945
pmcid: 9995757
doi: 10.3389/fnagi.2023.1117802
Powers, R. et al. Smartwatch inertial sensors continuously monitor real-world motor fluctuations in Parkinson’s disease. Sci Transl. Med. 13, eabd7865 (2021).
Adams, J. L. et al. Multiple wearable sensors in Parkinson and Huntington disease individuals: a pilot study in clinic and at home. Digit Biomark. 1, 52–63 (2017).
pubmed: 32095745
pmcid: 7015372
doi: 10.1159/000479018
Boroojerdi, B. et al. Clinical feasibility of a wearable, conformable sensor patch to monitor motor symptoms in Parkinson’s disease. Parkinsonism Relat. Disord. 61, 70–76 (2019).
pubmed: 30635244
doi: 10.1016/j.parkreldis.2018.11.024
Ullrich, M. et al. Detection of unsupervised standardized gait tests from real-world inertial sensor data in Parkinson’s disease. IEEE Trans. Neural Syst. Rehabil. Eng. 29, 2103–2111 (2021).
pubmed: 34633932
doi: 10.1109/TNSRE.2021.3119390
Brand, Y. E. et al. Gait detection from a wrist-worn sensor using machine learning methods: a daily living study in older adults and people with Parkinson disease. Sensors 22, 7094 (2022).
Evers, L. J. et al. Real-life gait performance as a digital biomarker for motor fluctuations: the Parkinson@home validation study. J. Med. Internet Res. 22, e19068 (2020).
pubmed: 33034562
pmcid: 7584982
doi: 10.2196/19068
Shah, V. V. et al. Laboratory versus daily life gait characteristics in patients with multiple sclerosis, Parkinson’s disease, and matched controls. J. Neuroeng. Rehabil. 17, 159 (2020).
pubmed: 33261625
pmcid: 7708140
doi: 10.1186/s12984-020-00781-4
Shah, V. V. et al. Digital biomarkers of mobility in Parkinson’s disease during daily living. J. Parkinsons Dis. 10, 1099–1111 (2020).
pubmed: 32417795
pmcid: 8128134
doi: 10.3233/JPD-201914
Silva de Lima, A. L. et al. Home-based monitoring of falls using wearable sensors in Parkinson’s disease. Mov. Disord. 35, 109–115 (2020).
pubmed: 31449705
doi: 10.1002/mds.27830
Srulijes, K. et al. Fall risk in relation to individual physical activity exposure in patients with different neurodegenerative diseases: a pilot study. Cerebellum 18, 340–348 (2019).
pubmed: 30617629
doi: 10.1007/s12311-018-1002-x
Corrà, M. F. et al. Comparison of laboratory and daily-life gait speed assessment during on and off states in Parkinson’s disease. Sensors (Basel) 21, 3974 (2021).
Silva de Lima, A. L. et al. Impact of motor fluctuations on real-life gait in Parkinson’s patients. Gait Posture 62, 388–394 (2018).
pubmed: 29627498
doi: 10.1016/j.gaitpost.2018.03.045
Lipsmeier, F. et al. Reliability and validity of the Roche PD mobile application for remote monitoring of early Parkinson’s disease. Sci. Rep. 12, 12081 (2022).
pubmed: 35840753
pmcid: 9287320
doi: 10.1038/s41598-022-15874-4
Rodríguez-Molinero, A. et al. Analysis of correlation between an accelerometer-based algorithm for detecting Parkinsonian gait and UPDRS subscales. Front Neurol. 8, 431 (2017).
pubmed: 28919877
pmcid: 5585138
doi: 10.3389/fneur.2017.00431
Safarpour, D. et al. Surrogates for rigidity and PIGD MDS-UPDRS subscores using wearable sensors. Gait Posture 91, 186–191 (2022).
pubmed: 34736096
doi: 10.1016/j.gaitpost.2021.10.029
Burq, M. et al. Virtual exam for Parkinson’s disease enables frequent and reliable remote measurements of motor function. NJP Digit. Med., 2022 5, 65 (2022).
Gaßner, H. et al. Clinical relevance of standardized mobile gait tests. Reliability analysis between gait recordings at hospital and home in Parkinson’s disease: a pilot study. J. Parkinsons Dis. 10, 1763–1773 (2020).
pubmed: 32925099
doi: 10.3233/JPD-202129
Oyama, G. et al. Analytical and clinical validity of wearable, multi-sensor technology for assessment of motor function in patients with Parkinson’s disease in Japan. Sci. Rep. 13, 3600 (2023).
pubmed: 36918552
pmcid: 10015076
doi: 10.1038/s41598-023-29382-6
Marano, M. et al. Remote smartphone gait monitoring and fall prediction in Parkinson’s disease during the COVID-19 lockdown. Neurol. Sci. 42, 3089–3092 (2021).
pubmed: 34046795
pmcid: 8159018
doi: 10.1007/s10072-021-05351-7
Atrsaei, A. et al. Effect of fear of falling on mobility measured during lab and daily activity assessments in Parkinson’s disease. Front Aging Neurosci. 13, 722830 (2021).
pubmed: 34916920
pmcid: 8669821
doi: 10.3389/fnagi.2021.722830
Mancini, M. et al. Measuring freezing of gait during daily-life: an open-source, wearable sensors approach. J. Neuroeng. Rehabil. 18, 1 (2021).
pubmed: 33397401
pmcid: 7784003
doi: 10.1186/s12984-020-00774-3
Mancini, M., Weiss, A., Herman, T. & Hausdorff, J. M. Turn around freezing: community-living turning behavior in people with Parkinson’s disease. Front. Neurol. 9, 18 (2018).
pubmed: 29434567
pmcid: 5790768
doi: 10.3389/fneur.2018.00018
Rodríguez-Molinero, A. et al. A kinematic sensor and algorithm to detect motor fluctuations in Parkinson disease: validation swtudy under real conditions of use. JMIR Rehabil. Assist Technol. 5, e8 (2018).
pubmed: 29695377
pmcid: 5943625
doi: 10.2196/rehab.8335
Zhu, L. et al. Comparing GPS-based community mobility measures with self-report assessments in older adults with Parkinson’s disease. J. Gerontol. A Biol. Sci. Med Sci. 75, 2361–2370 (2020).
pubmed: 31957792
pmcid: 7662184
doi: 10.1093/gerona/glaa012
Tsakanikas, V. et al. Evaluating gait impairment in Parkinson’s disease from instrumented insole and IMU sensor data. Sensors (Basel) 23, 3902 (2023).
Adams, J. L. et al. Using a smartwatch and smartphone to assess early Parkinson’s disease in the WATCH-PD study. NPJ Parkinsons Dis. 9, 64 (2023).
pubmed: 37069193
pmcid: 10108794
doi: 10.1038/s41531-023-00497-x
Kanellos, F. S. et al. Clinical evaluation in Parkinson’s disease: is the golden standard shiny enough? Sensors (Basel) 23, 3807 (2023).
Heldman, D. A. et al. Telehealth management of Parkinson’s disease using wearable sensors: an exploratory study. Digit Biomark. 1, 43–51 (2017).
pubmed: 29725667
pmcid: 5927622
doi: 10.1159/000475801
Hadley, A. J., Riley, D. E. & Heldman, D. A. Real-world evidence for a smartwatch-based Parkinson’s motor assessment app for patients undergoing therapy changes. Digit Biomark. 5, 206–215 (2021).
pubmed: 34703975
pmcid: 8460946
doi: 10.1159/000518571
Isaacson, S. H. et al. Effect of using a wearable device on clinical decision-making and motor symptoms in patients with Parkinson’s disease starting transdermal rotigotine patch: a pilot study. Parkinsonism Relat. Disord. 64, 132–137 (2019).
pubmed: 30948242
doi: 10.1016/j.parkreldis.2019.01.025
Santiago, A. et al. Qualitative evaluation of the personal KinetiGraphTM movement recording system in a Parkinson’s clinic. J. Parkinsons Dis. 9, 207–219 (2019).
pubmed: 30412506
pmcid: 6398558
doi: 10.3233/JPD-181373
Farzanehfar, P., Woodrow, H. & Horne, M. Assessment of wearing off in Parkinson’s disease using objective measurement. J. Neurol. 268, 914–922 (2021).
pubmed: 32935159
doi: 10.1007/s00415-020-10222-w
Cochen De Cock, V. et al. BeatWalk: Personalized music-based gait rehabilitation in Parkinson’s disease. Front Psychol. 12, 655121 (2021).
pubmed: 33981279
pmcid: 8109247
doi: 10.3389/fpsyg.2021.655121
Chomiak, T., Watts, A., Meyer, N., Pereira, F. V. & Hu, B. A. A training approach to improve stepping automaticity while dual-tasking in Parkinson’s disease: a prospective pilot study. Medicine 96, e5934 (2017).
pubmed: 28151878
pmcid: 5293441
doi: 10.1097/MD.0000000000005934
Gaßner, H. et al. The effects of an individualized smartphone-based exercise program on self-defined motor tasks in Parkinson disease: pilot interventional study. JMIR Rehabil. Assist Technol. 9, e38994 (2022).
pubmed: 36378510
pmcid: 9709672
doi: 10.2196/38994
Gatsios, D. et al. Feasibility and utility of mhealth for the remote monitoring of Parkinson disease: ancillary study of the PD_manager randomized controlled trial. JMIR Mhealth Uhealth 8, e16414 (2020).
pubmed: 32442154
pmcid: 7367523
doi: 10.2196/16414
Heijmans, M. et al. Monitoring Parkinson’s disease symptoms during daily life: a feasibility study. NPJ Parkinsons Dis., 2019 5, 21 (2019).
doi: 10.1038/s41531-019-0093-5
Bouça-Machado, R. et al. Feasibility of a mobile-based system for unsupervised monitoring in Parkinson’s disease. Sensors (Basel) 21, 4972 (2021).
Flynn, A., Allen, N. E., Dennis, S., Canning, C. G. & Preston, E. Home-based prescribed exercise improves balance-related activities in people with Parkinson’s disease and has benefits similar to centre-based exercise: a systematic review. J. Physiother. 65, 189–199 (2019).
pubmed: 31521554
doi: 10.1016/j.jphys.2019.08.003
Aarsland, D. et al. Parkinson disease-associated cognitive impairment. Nat. Rev. Dis. Prim. 7, 47 (2021).
pubmed: 34210995
doi: 10.1038/s41572-021-00280-3
Klucken, J., Krüger, R., Schmidt, P. & Bloem, B. R. Management of Parkinson’s disease 20 years from now: towards digital health pathways. J. Parkinsons Dis. 8, S85–s94 (2018).
pubmed: 30584171
pmcid: 6311358
doi: 10.3233/JPD-181519
Sharma, Y., Cheung, L., Patterson, K. K. & Iaboni, A. Factors influencing the clinical adoption of quantitative gait analysis technologies for adult patient populations with a focus on clinical efficacy and clinician perspectives: protocol for a scoping review. JMIR Res. Protoc. 12, e39767 (2023).
pubmed: 36947120
pmcid: 10131694
doi: 10.2196/39767
Rodríguez-Molinero, A. et al. Estimating dyskinesia severity in Parkinson’s disease by using a waist-worn sensor: concurrent validity study. Sci. Rep. 9, 13434 (2019).
pubmed: 31530855
pmcid: 6748910
doi: 10.1038/s41598-019-49798-3
Hssayeni, M. D., Jimenez-Shahed, J., Burack, M. A. & Ghoraani, B. Ensemble deep model for continuous estimation of unified Parkinson’s disease rating scale III. Biomed. Eng. Online 20, 32 (2021).
pubmed: 33789666
pmcid: 8010504
doi: 10.1186/s12938-021-00872-w
Keogh, A., Argent, R., Anderson, A. & Johnston, W. Assessing the usability of wearable devices to measure gait and physical activity in chronic conditions: a systematic review. J. Neuroeng. Rehabil. 18, 138 (2021).
pubmed: 34526053
pmcid: 8444467
doi: 10.1186/s12984-021-00931-2
Huhn, S. et al. The impact of wearable technologies in health research: scoping review. JMIR Mhealth Uhealth 10, e34384 (2022).
pubmed: 35076409
pmcid: 8826148
doi: 10.2196/34384
Nomeikaite, A. et al. Exploring reasons for usage discontinuation in an internet-delivered stress recovery intervention: a qualitative study. Internet Inter. 34, 100686 (2023).
doi: 10.1016/j.invent.2023.100686
Mumtaz, H. et al. Current challenges and potential solutions to the use of digital health technologies in evidence generation: a narrative review. Front. Digit Health 5, 1203945 (2023).
pubmed: 37840685
pmcid: 10568450
doi: 10.3389/fdgth.2023.1203945
Madanian, S., Nakarada-Kordic, I., Reay, S. & Chetty, T. Patients’ perspectives on digital health tools. PEC Innov. 2, 100171 (2023).
pubmed: 37384154
pmcid: 10294099
doi: 10.1016/j.pecinn.2023.100171
Bally, E. L. S. et al. Value-based methodology for person-centred, integrated care supported by information and communication technologies’ (ValueCare) for older people in Europe: study protocol for a pre-post controlled trial. BMC Geriatr. 22, 680 (2022).
pubmed: 35978306
pmcid: 9386998
doi: 10.1186/s12877-022-03333-8
Bombard, Y. et al. Engaging patients to improve quality of care: a systematic review. Implement. Sci. 13, 98 (2018).
pubmed: 30045735
pmcid: 6060529
doi: 10.1186/s13012-018-0784-z
Byrne, A. L., Baldwin, A. & Harvey, C. Whose centre is it anyway? defining person-centred care in nursing: an integrative review. PLoS One 15, e0229923 (2020).
pubmed: 32155182
pmcid: 7064187
doi: 10.1371/journal.pone.0229923
Schwaninger, I., Carros, F., Weiss, A., Wulf, V. & Fitzpatrick, G. Video connecting families and social robots: from ideas to practices putting technology to work. Univ. Access Inf. Soc. 22, 931–943 (2023).
doi: 10.1007/s10209-022-00901-y
Bloem, B. R. et al. ParkinsonNet: A low-cost health care innovation with a systems approach from the Netherlands. Health Aff. (Millwood) 36, 1987–1996 (2017).
pubmed: 29137501
doi: 10.1377/hlthaff.2017.0832
van Leeuwen, K. G., Schalekamp, S., Rutten, M. J. C. M., van Ginneken, B. & de Rooij, M. Artificial intelligence in radiology: 100 commercially available products and their scientific evidence. Eur. Radiol. 31, 3797–3804 (2021).
pubmed: 33856519
pmcid: 8128724
doi: 10.1007/s00330-021-07892-z
Downes, M. J., Brennan, M. L., Williams, H. C. & Dean, R. S. Development of a critical appraisal tool to assess the quality of cross-sectional studies (AXIS). BMJ Open 6, e011458 (2016).
pubmed: 27932337
pmcid: 5168618
doi: 10.1136/bmjopen-2016-011458
Sica, M. et al. Continuous home monitoring of Parkinson’s disease using inertial sensors: a systematic review. PLoS One 16, e0246528 (2021).
pubmed: 33539481
pmcid: 7861548
doi: 10.1371/journal.pone.0246528
Burkhart, P. V. & Sabaté, E. Adherence to long-term therapies: evidence for action. J. Nurs. Scholarsh. 35, 207 (2003).
pubmed: 14562485
doi: 10.1111/j.1547-5069.2003.tb00001.x
Handbook of Research on Digital Libraries. Design, development and impact. Program 43, 342–343 (2009).
doi: 10.1108/00330330910978626
Bhidayasiri, R. et al. Rotigotine for nocturnal hypokinesia in Parkinson’s disease: quantitative analysis of efficacy from a randomized, placebo-controlled trial using an axial inertial sensor. Parkinsonism Relat. Disord. 44, 124–128 (2017).
pubmed: 28818560
doi: 10.1016/j.parkreldis.2017.08.010
Iijima, M., Mitoma, H., Uchiyama, S. & Kitagawa, K. Long-term monitoring gait analysis using a wearable device in daily lives of patients with Parkinson’s disease: the efficacy of selegiline hydrochloride for gait disturbance. Front. Neurol. 8, 542 (2017).
pubmed: 29114238
pmcid: 5660685
doi: 10.3389/fneur.2017.00542
Khodakarami, H., et al. Prediction of the levodopa challenge test in Parkinson’s disease using data from a wrist-worn sensor. Sensors (Basel) 19, 5153 (2019).
Bouça-Machado, R. et al. Kinematic and clinical outcomes to evaluate the efficacy of a multidisciplinary intervention on functional mobility in Parkinson’s disease. Front Neurol. 12, 637620 (2021).
pubmed: 33833729
pmcid: 8021905
doi: 10.3389/fneur.2021.637620
Caballol, N., Bayés, À., Prats, A., Martín-Baranera, M. & Quispe, P. Feasibility of a wearable inertial sensor to assess motor complications and treatment in Parkinson’s disease. PLoS One https://doi.org/10.1371/journal.pone.0279910 (2023).
Thomas, I. et al. Sensor-based algorithmic dosing suggestions for oral administration of levodopa/carbidopa microtablets for Parkinson’s disease: a first experience. J. Neurol. 266, 651–658 (2019).
pubmed: 30659356
pmcid: 6394802
doi: 10.1007/s00415-019-09183-6
Haertner, L. et al. Effect of fear of falling on turning performance in Parkinson’s disease in the lab and at home. Front. Aging Neurosci. 10, 78 (2018).
Kyritsis, K. et al. Assessment of real life eating difficulties in Parkinson’s disease patients by measuring plate to mouth movement elongation with inertial sensors. Sci. Rep. 11, 1632 (2021).
pubmed: 33452324
pmcid: 7810687
doi: 10.1038/s41598-020-80394-y
Mirelman, A. et al. Tossing and turning in bed: nocturnal movements in Parkinson’s disease. Mov. Disord. 35, 959–968 (2020).
pubmed: 32080891
doi: 10.1002/mds.28006
Knudson, M., Thomsen, T. H. & Kjaer, T. W. Comparing objective and subjective measures of Parkinson’s disease using the Parkinson’s KinetiGraph. Front Neurol. 11, 570833 (2020).
pubmed: 33250843
pmcid: 7674832
doi: 10.3389/fneur.2020.570833
Papadopoulos, A. et al. Detecting parkinsonian tremor from IMU data collected in-the-wild using deep multiple-instance learning. IEEE J. Biomed. Health Inf. 24, 2559–2569 (2020).
doi: 10.1109/JBHI.2019.2961748
San-Segundo, R., et al. Parkinson’s disease tremor detection in the wild using wearable accelerometers. Sensors 20, 5817 (2020).
Habets, J. G. V., et al. Rapid dynamic naturalistic monitoring of bradykinesia in Parkinson’s disease using a wrist-worn accelerometer. Sensors (Basel) 21, 7876 (2021).