Evidence map›Paper›PMID 38218890›Full record

ReviewJournal of neuroengineering and rehabilitation2024

Upper limb intention tremor assessment: opportunities and challenges in wearable technology.

Natalia Paredes-Acuna, Daniel Utpadel-Fischler, Keqin Ding, Nitish V Thakor, Gordon Cheng

Open access · goldAbstract readReview
In one paragraph

Review in Journal of neuroengineering and rehabilitation, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
5.2field-weighted citation impact, top 4% of its field
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

5 citing papers in PubMed, 14 citations in OpenAlex.

  1. Article
  2. Article
  3. Review
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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

5 authors at 3 institutions in 2 countries.

Natalia Paredes-AcunaInstitute for Cognitive Systems, Technical University of Munich, Arcisstraße 21, 80333, Munich, Germany. natalia.paredes@tum.de.ORCID 0000-0002-8139-6179
Daniel Utpadel-FischlerDepartment of Neurology, School of Medicine, Technical University of Munich, Munich, Germany.ORCID 0000-0003-0809-0242
Keqin DingDepartment of Biomedical Engineering, Johns Hopkins School of Medicine, Baltimore, MD, USA.ORCID 0000-0002-0680-673X
Nitish V ThakorDepartment of Biomedical Engineering, Johns Hopkins School of Medicine, Baltimore, MD, USA.ORCID 0000-0002-9981-9395
Gordon ChengInstitute for Cognitive Systems, Technical University of Munich, Arcisstraße 21, 80333, Munich, Germany.ORCID 0000-0003-0770-8717
Fraunhofer Institute for Cognitive Systems · DEJohns Hopkins University · USTechnical University of Munich · DE

Funding

Institute for Advanced Studies (IAS), Technical University of Munich Hans Fischer Senior Fellowship
6 · The paper itself

Abstract

backgroundTremors are involuntary rhythmic movements commonly present in neurological diseases such as Parkinson's disease, essential tremor, and multiple sclerosis. Intention tremor is a subtype associated with lesions in the cerebellum and its connected pathways, and it is a common symptom in diseases associated with cerebellar pathology. While clinicians traditionally use tests to identify tremor type and severity, recent advancements in wearable technology have provided quantifiable ways to measure movement and tremor using motion capture systems, app-based tasks and tools, and physiology-based measurements. However, quantifying intention tremor remains challenging due to its changing nature. METHODOLOGY &

resultsThis review examines the current state of upper limb tremor assessment technology and discusses potential directions to further develop new and existing algorithms and sensors to better quantify tremor, specifically intention tremor. A comprehensive search using PubMed and Scopus was performed using keywords related to technologies for tremor assessment. Afterward, screened results were filtered for relevance and eligibility and further classified into technology type. A total of 243 publications were selected for this review and classified according to their type: body function level: movement-based, activity level: task and tool-based, and physiology-based. Furthermore, each publication's methods, purpose, and technology are summarized in the appendix table.

conclusionsOur survey suggests a need for more targeted tasks to evaluate intention tremors, including digitized tasks related to intentional movements, neurological and physiological measurements targeting the cerebellum and its pathways, and signal processing techniques that differentiate voluntary from involuntary movement in motion capture systems.

Indexed as

TremorWearable Electronic DevicesEssential TremorHumansMovementParkinson DiseaseUpper ExtremityAtaxiaIntention tremorMultiple sclerosisTremor assessmentWearable sensors

Identifiers

PMID38218890
PMCPMC10787996
OpenAlexW4390837702

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.