Wearable Technology to Detect Motor Fluctuations in Parkinson’s Disease Patients: Current State and Challenges

Barrachina, Mercedes; Maitín, Ana M.; Ávila, Carmen Sánchez; Romero, Juan Pablo

doi:10.3390/s21124188

Cited by 27 publications

(16 citation statements)

References 39 publications

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“…Nevertheless, there are some other preceding reviews that address this topic, following a similar approach [41][42][43][44][45][46][47]. However, the approach adopted herein is more comprehensive than most of these systematic reviews, as 112 studies are analyzed and considered, compared to 10-48 studies considered in previous approaches.…”

Section: Aim Of the Current Systematic Reviewmentioning

confidence: 99%

Internet of Things Technologies and Machine Learning Methods for Parkinson’s Disease Diagnosis, Monitoring and Management: A Systematic Review

Giannakopoulou

Roussaki

Demestichas

2022

Sensors

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Parkinson’s disease is a chronic neurodegenerative disease that affects a large portion of the population, especially the elderly. It manifests with motor, cognitive and other types of symptoms, decreasing significantly the patients’ quality of life. The recent advances in the Internet of Things and Artificial Intelligence fields, including the subdomains of machine learning and deep learning, can support Parkinson’s disease patients, their caregivers and clinicians at every stage of the disease, maximizing the treatment effectiveness and minimizing the respective healthcare costs at the same time. In this review, the considered studies propose machine learning models, trained on data acquired via smart devices, wearable or non-wearable sensors and other Internet of Things technologies, to provide predictions or estimations regarding Parkinson’s disease aspects. Seven hundred and seventy studies have been retrieved from three dominant academic literature databases. Finally, one hundred and twelve of them have been selected in a systematic way and have been considered in the state-of-the-art systematic review presented in this paper. These studies propose various methods, applied on various sensory data to address different Parkinson’s disease-related problems. The most widely deployed sensors, the most commonly addressed problems and the best performing algorithms are highlighted. Finally, some challenges are summarized along with some future considerations and opportunities that arise.

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Section: Aim Of the Current Systematic Reviewmentioning

confidence: 99%

Internet of Things Technologies and Machine Learning Methods for Parkinson’s Disease Diagnosis, Monitoring and Management: A Systematic Review

Giannakopoulou

Roussaki

Demestichas

2022

Sensors

View full text Add to dashboard Cite

show abstract

“…Furthermore, so far, no clear evidence or article has been published on the algorithm methods used in these last two devices. In the review performed in ( 72 ), different algorithms are also proposed as techniques to be embedded in hardware solutions in order to detect motor fluctuations. Other solutions focus more on gait, which can be also interesting ( 73 – 78 ), but they do not provide continuous monitoring at home with a complete mapping of the different symptoms of PD.…”

Section: State Of the Artmentioning

confidence: 99%

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

et al. 2022

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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-ONTM, 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-ONTM 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-ONTM are presented.

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“…As shown in Table 3, one of the most notable characteristics of the selected articles had to do with the variety of models used. A brief description of these models can be found in Table 1 of [88]. The number of models used exceeded the number of articles selected.…”

Section: Evaluation Of the Models Usedmentioning

confidence: 99%

Survey of Machine Learning Techniques in the Analysis of EEG Signals for Parkinson’s Disease: A Systematic Review

2022

Self Cite

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Background: Parkinson’s disease (PD) affects 7–10 million people worldwide. Its diagnosis is clinical and can be supported by image-based tests, which are expensive and not always accessible. Electroencephalograms (EEG) are non-invasive, widely accessible, low-cost tests. However, the signals obtained are difficult to analyze visually, so advanced techniques, such as Machine Learning (ML), need to be used. In this article, we review those studies that consider ML techniques to study the EEG of patients with PD. Methods: The review process was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, which are used to provide quality standards for the objective evaluation of various studies. All publications before February 2022 were included, and their main characteristics and results were evaluated and documented through three key points associated with the development of ML techniques: dataset quality, data preprocessing, and model evaluation. Results: 59 studies were included. The predominating models were Support Vector Machine (SVM) and Artificial Neural Networks (ANNs). In total, 31 articles diagnosed PD with a mean accuracy of 97.35 ± 3.46%. There was no standard cleaning protocol for EEG and a great heterogeneity in EEG characteristics was shown, although spectral features predominated by 88.37%. Conclusions: Neither the cleaning protocol nor the number of EEG channels influenced the classification results. A baseline value was provided for the PD diagnostic problem, although recent studies focus on the identification of cognitive impairment.

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Wearable Technology to Detect Motor Fluctuations in Parkinson’s Disease Patients: Current State and Challenges

Cited by 27 publications

References 39 publications

Internet of Things Technologies and Machine Learning Methods for Parkinson’s Disease Diagnosis, Monitoring and Management: A Systematic Review

Internet of Things Technologies and Machine Learning Methods for Parkinson’s Disease Diagnosis, Monitoring and Management: A Systematic Review

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

Survey of Machine Learning Techniques in the Analysis of EEG Signals for Parkinson’s Disease: A Systematic Review

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