Parkinson’s disease detection from 20-step walking tests using inertial sensors of a smartphone: Machine learning approach based on an observational case-control study

Juutinen, Milla; Wang, Cassia; Zhu, Justin; Haladjian, Juan; Ruokolainen, Jari; Puustinen, Juha; Vehkaoja, Antti

doi:10.1371/journal.pone.0236258

Cited by 30 publications

(34 citation statements)

References 46 publications

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“…The accuracy found in our study was similar that presented in the results of [ 31 , 32 ]. Despite the metrics calculated in our work being close to the values found in [ 31 , 32 ], it is important to highlight the statement of [ 33 ], where emphasis is placed in their work on the performance comparison of the classifiers among several studies, which must be carried out carefully, due to the differences involved in the calculation of the metrics, such as the parameters of the algorithms and the features used.…”

Section: Discussionsupporting

confidence: 92%

“…The study performed in [ 31 ] also used the inertial sensors of accelerometer and gyroscope, but applied to gait of those with PD alongside healthy individuals, to differentiate the relative parameters for gait between the two groups. The classifiers used were also, among others, the KNN, SVM, RF, and NB, and the parameter used for validating the result was accuracy, the classifier that presented the best general result was the KNN with an accuracy of 84.5%.…”

Section: Discussionmentioning

confidence: 99%

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Discrimination between healthy and patients with Parkinson’s disease from hand resting activity using inertial measurement unit

Peres

Calil

Silva³

et al. 2021

BioMed Eng OnLine

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Background Parkinson’s disease (PD) is a neurological disease that affects the motor system. The associated motor symptoms are muscle rigidity or stiffness, bradykinesia, tremors, and gait disturbances. The correct diagnosis, especially in the initial stages, is fundamental to the life quality of the individual with PD. However, the methods used for diagnosis of PD are still based on subjective criteria. As a result, the objective of this study is the proposal of a method for the discrimination of individuals with PD (in the initial stages of the disease) from healthy groups, based on the inertial sensor recordings. Methods A total of 27 participants were selected, 15 individuals previously diagnosed with PD and 12 healthy individuals. The data collection was performed using inertial sensors (positioned on the back of the hand and on the back of the forearm). Different numbers of features were used to compare the values of sensitivity, specificity, precision, and accuracy of the classifiers. For group classification, 4 classifiers were used and compared, those being [Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Naive Bayes (NB)]. Results When all individuals with PD were analyzed, the best performance for sensitivity and accuracy (0.875 and 0.800, respectively) was found in the SVM classifier, fed with 20% and 10% of the features, respectively, while the best performance for specificity and precision (0.933 and 0.917, respectively) was associated with the RF classifier fed with 20% of all the features. When only individuals with PD and score 1 on the Hoehn and Yahr scale (HY) were analyzed, the best performances for sensitivity, precision and accuracy (0.933, 0.778 and 0.848, respectively) were from the SVM classifier, fed with 40% of all features, and the best result for precision (0.800) was connected to the NB classifier, fed with 20% of all features. Conclusion Through an analysis of all individuals in this study with PD, the best classifier for the detection of PD (sensitivity) was the SVM fed with 20% of the features and the best classifier for ruling out PD (specificity) was the RF classifier fed with 20% of the features. When analyzing individuals with PD and score HY = 1, the SVM classifier was superior across the sensitivity, precision, and accuracy, and the NB classifier was superior in the specificity. The obtained result indicates that objective methods can be applied to help in the evaluation of PD.

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Section: Discussionsupporting

confidence: 92%

Section: Discussionmentioning

confidence: 99%

Discrimination between healthy and patients with Parkinson’s disease from hand resting activity using inertial measurement unit

Peres

Calil

Silva³

et al. 2021

BioMed Eng OnLine

View full text Add to dashboard Cite

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“…Similarly, similar procedures, extracting specific features from available data allowing the development of ML based models for Parkinson's disease (PD). In fact, as reported for AD, several studies highlighted that through ML-based approaches applied to PD [268] is possible to predict the progression of the disorder employing serum cytokines [269], MRI [270], and walking tests [271], to estimate the state of PD, employing longitudinal data [272], to rate the main synthomps (resting tremor and bradykinesia) [273], to produce a correct diagnosis from EEG analysis [274,275] and from voice dataset [276,277], only for reporting some relevant works.…”

Section: Ai Imaging and Ophthalmologymentioning

confidence: 96%

Artificial Intelligence in Translational Medicine

Brogi¹,

Calderone²

2021

Preprint

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The huge advancement of Internet web facilities as well as the progress in computing and algorithm development, along with current innovations regarding high-throughput techniques enables the scientific community to gain access to biological datasets, clinical data, and several databases containing billions of information concerning scientific knowledge. Consequently, during the last decade the system for managing, analyzing, processing and extrapolating information from scientific data has been considerably modified in several fields including the medical one. As a consequence of the mentioned scenario, scientific vocabulary was enriched by novel lexicons such as Machine Learning (ML)/Deep Learning (DL) and overall Artificial Intelligence (AI). Beyond the terminology, these computational techniques are revolutionizing the scientific research in drug discovery pitch, from the preclinical studies to clinical investigation. Interestingly, between preclinical and clinical research, the translational research is benefitting from computer-based approaches, transforming the design and execution of the translational research, resulting in breakthroughs for advancing human health. Accordingly, in this review article, we analyze the most advanced applications of AI in translational medicine, providing an up-to-date outlook regarding this emerging field.

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“…Similarly, comparable procedures, extracting specific features from available data, allowing the development of ML-based models for Parkinson's disease (PD). In fact, as reported for AD, several studies highlighted that through ML-based approaches applied to PD [279], it is possible to predict the progression of the disorder by employing serum cytokines [280], MRI [281], and walking tests [282]; to estimate the state of PD, employing longitudinal data [283]; to rate the main symptoms (resting tremor and bradykinesia) [284]; and to produce a correct diagnosis from EEG analysis [285,286] and from voice datasets [287,288].…”

Section: Ai/ml In Central Nervous System (Cns)-related Disordersmentioning

confidence: 99%

Artificial Intelligence in Translational Medicine

Brogi

Calderone

2021

IJTM

View full text Add to dashboard Cite

The huge advancement in Internet web facilities as well as the progress in computing and algorithm development, along with current innovations regarding high-throughput techniques, enable the scientific community to gain access to biological datasets, clinical data and several databases containing billions of pieces of information concerning scientific knowledge. Consequently, during the last decade the system for managing, analyzing, processing and extrapolating information from scientific data has been considerably modified in several fields, including the medical one. As a consequence of the mentioned scenario, scientific vocabulary was enriched by novel lexicons such as machine learning (ML)/deep learning (DL) and overall artificial intelligence (AI). Beyond the terminology, these computational techniques are revolutionizing the scientific research in drug discovery pitch, from the preclinical studies to clinical investigation. Interestingly, between preclinical and clinical research, translational research is benefitting from computer-based approaches, transforming the design and execution of translational research, resulting in breakthroughs for advancing human health. Accordingly, in this review article, we analyze the most advanced applications of AI in translational medicine, providing an up-to-date outlook regarding this emerging field.

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Parkinson’s disease detection from 20-step walking tests using inertial sensors of a smartphone: Machine learning approach based on an observational case-control study

Cited by 30 publications

References 46 publications

Discrimination between healthy and patients with Parkinson’s disease from hand resting activity using inertial measurement unit

Discrimination between healthy and patients with Parkinson’s disease from hand resting activity using inertial measurement unit

Artificial Intelligence in Translational Medicine

Artificial Intelligence in Translational Medicine

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