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Video-Based Hand Movement Analysis of Parkinson Patients before and after Medication Using High-Frame-Rate Videos and MediaPipe

Güney, Gökhan ; Jansen, Talisa S. ; Dill, Sebastian ; Schulz, Jörg B. ; Dafotakis, Manuel ; Hoog Antink, Christoph ; Braczynski, Anne K. (2022)
Video-Based Hand Movement Analysis of Parkinson Patients before and after Medication Using High-Frame-Rate Videos and MediaPipe.
In: Sensors, 2022, 22 (20)
doi: 10.26083/tuprints-00022843
Article, Secondary publication, Publisher's Version

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Item Type: Article
Type of entry: Secondary publication
Title: Video-Based Hand Movement Analysis of Parkinson Patients before and after Medication Using High-Frame-Rate Videos and MediaPipe
Language: English
Date: 7 November 2022
Place of Publication: Darmstadt
Year of primary publication: 2022
Publisher: MDPI
Journal or Publication Title: Sensors
Volume of the journal: 22
Issue Number: 20
Collation: 15 Seiten
DOI: 10.26083/tuprints-00022843
Corresponding Links:
Origin: Secondary publication DeepGreen
Abstract:

Tremor is one of the common symptoms of Parkinson’s disease (PD). Thanks to the recent evolution of digital technologies, monitoring of PD patients’ hand movements employing contactless methods gained momentum. Objective: We aimed to quantitatively assess hand movements in patients suffering from PD using the artificial intelligence (AI)-based hand-tracking technologies of MediaPipe. Method: High-frame-rate videos and accelerometer data were recorded from 11 PD patients, two of whom showed classical Parkinsonian-type tremor. In the OFF-state and 30 Minutes after taking their standard oral medication (ON-state), video recordings were obtained. First, we investigated the frequency and amplitude relationship between the video and accelerometer data. Then, we focused on quantifying the effect of taking standard oral treatments. Results: The data extracted from the video correlated well with the accelerometer-based measurement system. Our video-based approach identified the tremor frequency with a small error rate (mean absolute error 0.229 (±0.174) Hz) and an amplitude with a high correlation. The frequency and amplitude of the hand movement before and after medication in PD patients undergoing medication differ. PD Patients experienced a decrease in the mean value for frequency from 2.012 (±1.385) Hz to 1.526 (±1.007) Hz and in the mean value for amplitude from 8.167 (±15.687) a.u. to 4.033 (±5.671) a.u. Conclusions: Our work achieved an automatic estimation of the movement frequency, including the tremor frequency with a low error rate, and to the best of our knowledge, this is the first paper that presents automated tremor analysis before/after medication in PD, in particular using high-frame-rate video data.

Uncontrolled Keywords: Parkinson’s disease, tremor, video-based analyses, mediapipe, artificial intelligence
Status: Publisher's Version
URN: urn:nbn:de:tuda-tuprints-228436
Additional Information:

This article belongs to the Special Issue Recent Advancements in Sensor Technologies for Healthcare and Biomedical Applications

Classification DDC: 600 Technology, medicine, applied sciences > 600 Technology
600 Technology, medicine, applied sciences > 610 Medicine and health
Divisions: 18 Department of Electrical Engineering and Information Technology > Artificial Intelligent Systems in Medicine (KISMED)
Date Deposited: 07 Nov 2022 12:31
Last Modified: 14 Nov 2023 19:05
SWORD Depositor: Deep Green
URI: https://tuprints.ulb.tu-darmstadt.de/id/eprint/22843
PPN: 50163892X
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