A New Paradigm in Parkinson's Disease Evaluation With Wearable Medical Devices: A Review of STAT-ON™

被引:32
作者
Rodriguez-Martin, Daniel [1 ]
Cabestany, Joan [2 ]
Perez-Lopez, Carlos [3 ]
Pie, Marti [1 ]
Calvet, Joan [1 ]
Sama, Albert [1 ]
Capra, Chiara [1 ]
Catala, Andreu [2 ]
Rodriguez-Molinero, Alejandro [3 ]
机构
[1] Snse4Care SL, Cornella De Llobregat, Spain
[2] Univ Politecn Cataluna, Tech Res Ctr Dependency Care & Autonomous Living, Barcelona, Spain
[3] Dept Invest, Consorci Sanit Alt Penedes Garraf, Vilanova I La Geltru, Spain
基金
欧盟地平线“2020”;
关键词
wearables; accelerometer; machine learning (ML); Parkinson's disease; medical device; DEEP BRAIN-STIMULATION; DYSKINESIA ASSESSMENT; HOME DIARY; GAIT; LEVODOPA; TREMOR; POSTURE; SENSORS; IDENTIFICATION; BRADYKINESIA;
D O I
10.3389/fneur.2022.912343
中图分类号
R74 [神经病学与精神病学];
学科分类号
摘要
In the past decade, the use of wearable medical devices has been a great breakthrough in clinical practice, trials, and research. In the Parkinson's disease field, clinical evaluation is time limited, and healthcare professionals need to rely on retrospective data collected through patients' self-filled diaries and administered questionnaires. As this often leads to inaccurate evaluations, a more objective system for symptom monitoring in a patient's daily life is claimed. In this regard, the use of wearable medical devices is crucial. This study aims at presenting a review on STAT-ON (TM), a wearable medical device Class IIa, which provides objective information on the distribution and severity of PD motor symptoms in home environments. The sensor analyzes inertial signals, with a set of validated machine learning algorithms running in real time. The device was developed for 12 years, and this review aims at gathering all the results achieved within this time frame. First, a compendium of the complete journey of STAT-ON (TM) since 2009 is presented, encompassing different studies and developments in funded European and Spanish national projects. Subsequently, the methodology of database construction and machine learning algorithms design and development is described. Finally, clinical validation and external studies of STAT-ON (TM) are presented.
引用
收藏
页数:21
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