Evidence map›Paper›PMID 39720518›Full record

ArticleiScience2024

Continuous tremor monitoring in Parkinson's disease: A wristwatch-inspired triboelectric sensor approach.

Sirinya Ukasi, Satana Pongampai, Basanta Kumar Panigrahi, Swati Panda, Sugato Hajra, Hoe Joon Kim, Naratip Vittayakorn, Thitirat Charoonsuk

Abstract read
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In one paragraph

Article in iScience, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Review
  2. Digital Technologies for Symptom Monitoring in Parkinson Disease.Current neurology and neuroscience reports · 2026
    Review
  3. Article
  4. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Sirinya UkasiDepartment of Materials Science, Faculty of Science, Srinakharinwirot University, Sukhumvit 23, Watthana, Bangkok 10110, Thailand.
Satana PongampaiDepartment of Physics, Faculty of Science, King Mongkut's University of Technology Thonburi, Bangkok 10140, Thailand.
Basanta Kumar PanigrahiDepartment of Electrical Engineering, Siksha O Anusandhan University, Bhubaneswar 751030, India.
Swati PandaDepartment of Robotics and Mechatronics Engineering, Daegu Gyeongbuk Institute of Science and Technology, Daegu 42988, South Korea.
Sugato HajraDepartment of Robotics and Mechatronics Engineering, Daegu Gyeongbuk Institute of Science and Technology, Daegu 42988, South Korea.
Hoe Joon KimDepartment of Robotics and Mechatronics Engineering, Daegu Gyeongbuk Institute of Science and Technology, Daegu 42988, South Korea.
Naratip VittayakornDepartment of Chemistry, School of Science, King Mongkut's Institute of Technology Ladkrabang, Bangkok 10520, Thailand.
Thitirat CharoonsukDepartment of Materials Science, Faculty of Science, Srinakharinwirot University, Sukhumvit 23, Watthana, Bangkok 10110, Thailand.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Parkinson's disease (PD) prevalence is projected to reach 12 million by 2040. Wearable sensors offer a promising approach for comfortable, continuous tremor monitoring to optimize treatment strategies. Here, we present a wristwatch-like triboelectric sensor (WW-TES) inspired by automatic watches for unobtrusive PD tremor assessment. The WW-TES utilizes a free-standing design with a surface-modified polytetrafluoroethylene (PTFE) film and a stainless-steel rotor within a biocompatible polylactic acid (PLA) package. Electrode distance is optimized to maximize the output signal. We propose and discuss the WW-TES working mechanism. The final design is validated for activities of daily living (ADLs), with varying signal amplitudes corresponding to tremor severity levels ("normal" to "severe") based on MDS-UPDRS tremor frequency. Wavelet packet transform (WPT) is employed for signal analysis during ADLs. The WW-TES demonstrates the potential for continuous tremor monitoring, offering an accurate screening of severity and comfortable, unobtrusive wearability.

Indexed as

Health sciencesMaterials scienceNatural sciences

Identifiers

PMID39720518
PMCPMC11667019

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.