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A New Paradigm in Parkinson’s Disease Evaluation With Wearable Medical Devices: A Review of STAT-ONTM

Front Neurol (2022)

doi.org/10.3389/fneur.2022.912343

Comparison of the Results of a Parkinson’s Holter Monitor With Patient Diaries, in Real Conditions of Use: A Sub-analysis of the MoMoPa-EC Clinical Trial.

Front Neurol (2022)

Pérez-López C, Hernández-Vara J, Caballol N, Bayes À, Buongiorno M, Lopez-Ariztegui N, Gironell A, López-Sánchez J, Martínez-Castrillo JC, Sauco M A, et al.

doi.org/10.3389/fneur.2022.835249

Clinical utility of a personalized and long-term monitoring device for Parkinson’s disease in a real clinical practice setting: An expert opinion survey on STAT-ON.

Neurología (2020)

Santos García D, López Ariztegui N, Cubo E, Vinagre Aragón A, García-Ramos R, Borrué C, Fernández-Pajarín G, Caballol N, Cabo I, Barrios-López JM, et al.

doi: 10.1016/j.nrl.2020.10.013

A “HOLTER” for Parkinson’s disease: Validation of the ability to detect on-off states using the REMPARK system.

Gait Posture (2018)

doi: 10.1016/j.gaitpost.2017.09.031

A Kinematic Sensor and Algorithm to Detect Motor Fluctuations in Parkinson Disease: Validation Study Under Real Conditions of Use.

JMIR Rehabil Assist Technol (2018)

doi: 10.2196/rehab.8335

Analysis of correlation between an accelerometer-Based algorithm for Detecting Parkinsonian gait and UPDRS subscales.

Front Neurol (2017)

doi: 10.3389/fneur.2017.00431

Internal – Journals

Treatment of Parkinson’s disease could be regulated by movement sensors: Subcutaneous infusion of varying apomorphine doses according to the intensity of motor activity.

Med Hypotheses (2009)

doi: 10.1016/j.mehy.2008.11.031

Analyzing human gait and posture by combining feature selection and kernel methods

Neurocomputing (2011)

doi: 10.1016/j.neucom.2011.03.028

Gait identification by means of box approximation geometry of reconstructed attractors in latent space.

Neurocomputing (2013)

doi: 10.1016/j.neucom.2012.12.042

SVM-based posture identification with a single waist-located triaxial accelerometer.

Expert Syst Appl (2013)

doi: 10.1016/j.eswa.2013.07.028

A Wearable Inertial Measurement Unit for Long-Term Monitoring in the Dependency Care Area.

Sensors (2013)

doi: 10.3390/s131014079

Remote control of apomorphine infusion rate in Parkinson’s disease: Real-time dose variations according to the patients’ motor state. A proof of concept

Parkinsonism Relat Disord (2015)

doi: 10.1016/j.parkreldis.2015.04.030

Posture transition identification on PD patients through a SVM-based technique and a single waist-worn accelerometer.

Neurocomputing (2015)

doi: 10.1016/j.neucom.2014.09.084

Monitoring Motor Fluctuations in Parkinson’s Disease Using a Waist-Worn Inertial Sensor.

International Work Conference on Artificial Neural Networks. Advances in Computational Intelligence. Lecture Notes on Computer Science. (2015)

doi: 10.1007/978-3-319-19258-1_38

Detecting freezing of gait with a tri-axial accelerometer in Parkinson’s disease patients.

Med Biol Eng Comput (2016)

doi:10.1007/s11517-015-1395-3

Validation of a Portable Device for Mapping Motor and Gait Disturbances in Parkinson’s Disease.

JMIR mHealth uHealth (2015)

Rodríguez-Molinero A et al.

doi: 10.2196/mhealth.3321

Adapted step length estimators for patients with Parkinson’s disease using a lateral belt worn accelerometer.

Technol Heal Care (2015)

Sayeed T, el al.

doi: 10.3233/THC-140882

Transition-Aware Human Activity Recognition Using Smartphones.

Neurocomputing (2016)

Reyes-Ortiz J-L, et al.

doi: 10.1016/j.neucom.2015.07.085

Dopaminergic-induced dyskinesia assessment based on a single belt-worn accelerometer.

Artif Intell Med (2016)

doi: 10.1016/j.artmed.2016.01.001

Assessing Motor Fluctuations in Parkinson’s Disease Patients Based on a Single Inertial Sensor.

Sensors (2016)

doi: 10.3390/s16122132

Home detection of freezing of gait using support vector machines through a single waist-worn triaxial accelerometer.

PLoS One (2017)

doi: 10.1371/journal.pone.0171764

Estimating bradykinesia severity in Parkinson’s disease by analysing gait through a waist-worn sensor.

Comput Biol Med (2017)

doi: 10.1016/j.compbiomed.2017.03.020

A Waist-Worn Inertial Measurement Unit for Long-Term Monitoring of Parkinson’s Disease Patients.

Sensors (2017)

doi: 10.3390/s17040827

Determining the optimal features in freezing of gait detection through a single waist accelerometer in home environments.

Pattern Recognit Lett (2018)

doi: 10.1016/j.patrec.2017.05.009

Analysis of correlation between an accelerometer-Based algorithm for Detecting Parkinsonian gait and UPDRS subscales.

Front Neurol (2017)

doi: 10.3389/fneur.2017.00431

A “HOLTER” for Parkinson’s disease: Validation of the ability to detect on-off states using the REMPARK system.

Gait Posture (2018)

doi: 10.1016/j.gaitpost.2017.09.031

Deep learning for freezing of gait detection in Parkinson’s disease patients in their homes using a waist-worn inertial measurement unit.

Knowledge-Based Syst (2017)

doi: 10.1016/j.knosys.2017.10.017

A Kinematic Sensor and Algorithm to Detect Motor Fluctuations in Parkinson Disease: Validation Study Under Real Conditions of Use.

JMIR Rehabil Assist Technol (2018)

doi: 10.2196/rehab.8335

Posture transition analysis with barometers : contribution to accelerometer-based algorithms.

Neural Comput Appl (2018)

doi: 10.1007/s00521-018-3759-8

Estimating dyskinesia severity in Parkinson’s disease by using a waist-worn sensor: concurrent validity study.

Sci Rep (2019)

doi: 10.1038/s41598-019-49798-3

A new paradigm in Parkinson’s Disease evaluation with wearable medical devices: a review of STAT-ON.

Front Neurol (2022)

doi: 10.3389/fneur.2022.912343

 

doi: 10.1016/j.knosys.2017.10.017

Internal – Conference papers

HELP : Optimizing Treatment of Parkinson ’ s Disease Patients. 3rd International Conference on the Elderly and New Technologies.

3rd International Conference on the Elderly and New Technologies. Castellón de la Plana (2012). p. 1–10

Identification of sit-to-stand and stand-to-sit transitions using a single inertial sensor.

Stud Health Technol Inform (2012) 177:113–117

Link

Dyskinesia and motor state detection in Parkinson’s Disease patients with a single movement sensor.

2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE (2012). p. 1194–1197

doi: 10.1109/EMBC.2012.6346150

A Heterogeneous Database for Movement Knowledge Extraction in Parkinson ’ s Disease.

European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. (2013)

Identification of Postural Transitions Using a Waist-Located Inertial Sensor. International Work Conference on Artificial Neural Networks.

Lecture Notes on Computer Science. Springer-Verlag (2013). p. 142–14

A double closed loop to enhance the quality of life of Parkinson’s Disease patients: REMPARK system.

Stud Health Technol Inform (2014) 207:115–124.

doi: 10.3233/978-1-61499-474-9-115

Posture Detection with waist-worn Accelerometer : An application to improve Freezing of Gait detection in Parkinson ’ s disease patients.

European Conference on Ambient Assisted Living. Eindhoven (2014)

link

Enhancing FoG detection by means of postural context using a waist accelerometer.

First International Freezing of Gait Congress. Ein Bokek, Dead Sea, Israel (2014)

Link

Is “Frequency Distribution” Enough to Detect Tremor in PD Patients Using a Wrist Worn Accelerometer?

Proceedings of the 8th International Conference on Pervasive Computing Technologies for Healthcare. ICST (2014)

doi: 10.4108/icst.pervasivehealth.2014.254928

Human Activity Recognition on Smartphones with Awareness of Basic Activities and Postural Transitions

Lecture Notes in Computer Science p. 177–184 (2014)

doi: 10.1007/978-3-319-11179-7_23

Basketball Activity Recognition using Wearable Inertial Measurement Units.

XVI International Conference on Human Computer Interaction. Vilanova i la Geltrú (2015). p.7

Comparison of Features, Window Sizes and Classifiers in Detecting Freezing of Gait in Patients with Parkinson’s Disease through a Waist-Worn Accelerometer.

19 th International Conference of the Catalan Association for Artificial Intelligence, CCIA 2016. Barcelona (2016). p. 10

Satisfaction survey of a Parkinson’s Holter, a medical device for the monitoring of motor symptoms.

International Congress of Parkinson’s Disease and Movement Disorders. (2021)

Feasibility to detect Parkinson’s Motor Symptoms with a waist-worn Parkinson’s Holter.

International Congress of Parkinson’s Disease and Movement Disorders. (2021)

Registro simultáneo de la actividad motora con sensores inerciales (STAT ONTM) y de potenciales de campo de núcleo subtalámico (PerceptTM) en la enfermedad de Parkinson.

XLIII Reunión Anual Sociedad Andaluza Neurología. (2021)

Posture Transitions Identification Based on a Triaxial Accelerometer and a Barometer Sensor.

Advances in Computational Intelligence. Lecture Notes on Computer Science. Cádiz: Springer-Verlag (2017). p. 333–343

doi: 10.1007/978-3-319-59147-6_29

Deep Learning for Detecting Freezing of Gait Episodes in Parkinson’s Disease Based on Accelerometers.

Advances in Computational Intelligence. Cham: Springer International Publishing (2017). p. 344–355

doi: 10.1007/978-3-319-59147-6_30

STAT-ON: a Wearable inertial system to objectively evaluate motor symptoms in Parkinson’s Disease.

Movement Disorders Society Conference. (2019)

Randomized multicenter single-blind parallel-group trial to compare the efficacy of a Holter for Parkinson symptoms against other clinical follow-up methods.

World Parkinson Congress. Kyoto (2019)

Internal – Others

Posture Detection with waist-worn Accelerometer : An application to improve Freezing of Gait detection in Parkinson ’ s disease patients.

Recent Advances in Ambient Assisted Living – Bridging Assistive Technologies, e-Health and Personalized Health Care. (2015). p. 3–17

doi: 10.3233/978-1-61499-597-5 -3

Parkinson’s Disease Management through ICT: The REMPARK Approach.

River Publishers (2017). 1–250 p.

doi: 10.13052/rp-9788793519459

External – Journals

Clinical utility of a personalized and long-term monitoring device for Parkinson’s disease in a real clinical practice setting: An expert opinion survey on STAT-ON.

Neurología (2020)

Santos García D, López Ariztegui N, Cubo E, Vinagre Aragón A, García-Ramos R, Borrué C, Fernández-Pajarín G, Caballol N, Cabo I, Barrios-López JM, et al.

doi: 10.1016/j.nrl.2020.10.013

Adopting a multidisciplinary telemedicine intervention for fall prevention in Parkinson’s disease. Protocol for a longitudinal, randomized clinical trial.

Cubo E, Garcia-Bustillo A, Arnaiz-Gonzalez A, Ramirez-Sanz JM, Garrido-Labrador JL, Valiñas F, Allende M, Gonzalez-Bernal JJ, Gonzalez-Santos J, Diez-Pastor JF, et al.

PLoS One (2021) 16:e0260889.

doi: 10.1371/journal.pone.026088

Multicentre, randomised, single- blind, parallel group trial to compare the effectiveness of a Holter for Parkinson’s symptoms against other clinical monitoring methods: study protocol.

Rodriguez-Molinero A, Hernández-Vara J, Miñarro A, Martinez-Castrillo JC, Pérez-López C, Bayes À, Pérez-Martínez DA, Group MR.

MJ Open (2021) 11:1–9.

doi: 10.1136/ bmjopen-2020-045272

Evaluación de un sistema de sensores inerciales externos tipo Holter en pacientes con enfermedad de Parkinson en Argentina.

Perrote F, Zeppa G, Coca H, Figueroa S, de Battista JC.

Neurol Argentina (2021)

doi: 10.1016/j.neuarg.2021.05.006

Comparison of the Results of a Parkinson’s Holter Monitor With Patient Diaries, in Real Conditions of Use: A Sub-analysis of the MoMoPa-EC Clinical Trial.

Front Neurol (2022)

Pérez-López C, Hernández-Vara J, Caballol N, Bayes À, Buongiorno M, Lopez-Ariztegui N, Gironell A, López-Sánchez J, Martínez-Castrillo JC, Sauco M A, et al.

doi: 10.3389/fneur.2022.835249

External – Conferences papers & Others

Early detection of Parkinson‘s disease motor fluctuations with a wearable inertial sensor.

Movement Disorders. (2020). p. 35 (suppl. 1)

Caballol N, Prats A, Quispe P, Ranchal M, Alcaine S, Fondevilla F, Bayes A.

Link

Use in Clinical Practice of a Personalised and Long term Monitoring Device for Parkinson’s Disease: STAT ON.

International Congress of Parkinson’s Disease and Movement Disorders. Philadelphia (2020)

Santos-García D, López Ariztegui N, Cubo E, Vinagre Aragón A, García Ramos R, Borrué C, Fernández Pajarín G, Caballol N, Cabo I, Barrios López J, et al.

Utilidad del sensor STAT-ON para la Enfermedad de Parkinson en la práctica clínica diaria.

LXXIII Reunión Anual Sociedad Española de Neurología. (2021)

Caballol Pons N, Ávila A, Planas Ballvé A, Prats A, Quispe P, Pérez Soriano S, Cubo E, García Bustillo Á, Cabo I, López Ariztegui N, et al.

Towards the integration of body-worn sensors in the management of Parkinson’s disease : perspectives on new paradigms of follow-up.

Sorbonne Université (2021).

Fleischman C.

Link

Validation of a real-time monitoring system to detect motor symptoms in patients with Parkinson’s Disease treated with Levodopa Carbidopa Intestinal Gel.

International Congress of Parkinson’s Disease and Movement Disorders. (2021)

Bougea A, Palkopoulou M, Pantinaki S, Antonoglou A, Efthymiopoulou F.

Link

Satisfaction survey of a Parkinson’s Holter, a medical device for the monitoring of motor symptoms.

International Congress of Parkinson’s Disease and Movement Disorders. (2021)

Rodriguez-Martin D, Perez-Lopez C, Pie M, Calvet J, Catala A, Rodriguez-Molinero A, Cabestany J.

Feasibility to detect Parkinson’s Motor Symptoms with a waist-worn Parkinson’s Holter.

International Congress of Parkinson’s Disease and Movement Disorders. (2021)

Rodriguez-Martin D, Perez-Lopez C, Pie M, Calvet J, Catala A, Rodriguez-Molinero A, Cabestany J, Caballol N, Bayes A.

Registro simultáneo de la actividad motora con sensores inerciales (STAT ONTM) y de potenciales de campo de núcleo subtalámico (PerceptTM) en la enfermedad de Parkinson.

XLIII Reunión Anual Sociedad Andaluza Neurología. (2021)

Barrios-López JM, Ruiz Fernandez E, Triguero Cueva L, Madrid Navarro C, Perez Navarro J., Jouma Katati M, Mínguez Castellanos A, Escamilla Sevilla F.

Evaluation of Two Commercial Sensor Systems for Monitoring Parkinsonism and Their Possible Influence on Management of Parkinson’s Disease.

Gothenburg: Institute of Neuroscience and Physiology Sahlgrenska Academy University of Gothenburg (2022). 1–51 p.

Grahn F.

Link

nkLink

Simultaneous recording in Parkinson’s disease with STAT-ON and subthalamic local field potentials (PerceptTM).

8th Congress of the European Academy of Neurology – Europe 2022. (2022)

Barrios López JM, Ruiz Fernández E, Triguero Cueva L, Madrid Navarro C, Pérez Navarro MJ, Jouma Katati M, Mínguez Castellanos A, Escamilla Sevilla F.

Body-Worn Sensors for Parkinson’s disease: A qualitative approach with patients and healthcare professionals.

PLoS One (2022) 17:e0265438.

Virbel-Fleischman C, Rétory Y, Hardy S, Huiban C, Corvol J-C, Grabli D

doi: 10.1371/journal.pone.0265438