Evidence map›Paper›PMID 37736618›Full record

Article...IEEE...International Conference on Connected Health: Applications, Systems and Engineering Technologies. IEEE International Conference on Connected Health: Applications, Systems and Engineering Technologies2023

FoG-Finder: Real-time Freezing of Gait Detection and Treatment.

Kenneth Koltermann, Woosub Jung, GinaMari Blackwell, Abbott Pinney, Matthew Chen, Leslie Cloud, Ingrid Pretzer-Aboff, Gang Zhou

Open access · goldAbstract read
In one paragraph

Article in ...IEEE...International Conference on Connected Health: Applications, Systems and Engineering Technologies. IEEE International Conference on Connected Health: Applications, Systems and Engineering Technologies, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed, 1 pooled it
8.0field-weighted citation impact, top 2% 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

8 citing papers in PubMed, 1 synthesis or guideline pooled it, 11 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Gait-Guard: Turn-aware Freezing of Gait Detection for Non-intrusive Intervention Systems....IEEE...International Conference on Connected Health: Applications, Systems and Engineering Technologies. IEEE International Conference on Connected Health: Applications, Systems and Engineering Technologies · 2024
    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 at 2 institutions in 1 country.

Kenneth KoltermannDepartment of Computer Science, William & Mary.
Woosub JungDepartment of Computer Science, William & Mary.
GinaMari BlackwellSchool of Nursing, Virginia Commonwealth University.
Abbott PinneyDepartment of Computer Science, William & Mary.
Matthew ChenDepartment of Computer Science, William & Mary.
Leslie CloudDepartment of Neurology, Virginia Commonwealth University.
Ingrid Pretzer-AboffSchool of Nursing, Virginia Commonwealth University.
Gang ZhouDepartment of Computer Science, William & Mary.
William & Mary · USVirginia Commonwealth University · US

Funding

SCH: Context-Aware Freezing of Gait Mitigation in Real-World SettingR01NS120560 · NINDS · VIRGINIA COMMONWEALTH UNIVERSITY · PI CLOUD, LESLIE J, PRETZER-ABOFF, INGRID A · 2021 to 2024
$1.2M
NINDS NIH HHS R01 NS120560
6 · The paper itself

Abstract

Freezing of gait is a serious symptom of Parkinson's disease that increases the risk of injury through falling, and reduces quality of life. Current clinical freezing of gait treatments fail to adequately address the fall risk posed by freezing of gait symptoms, and current real-time treatment systems have high false positive rates. To address this problem, we designed a closed-loop, non-intrusive, and real-time freezing of gait detection and treatment system, FoG-Finder, that automatically detects and treats freezing of gait. To evaluate FoG-Finder, we first collected 716 freezing of gait events from 11 patients. We then compared FoG-Finder against other real-time systems with our dataset. Our system was able to achieve a 13.4% higher F1 score and a 10.7% higher overall accuracy while achieving a reduction of 85.8% in the false positive treatment rate compared with other validated real-time freezing of gait detection and treatment systems. Additionally, FoG-Finder achieved an average treatment latency of 427ms and 615ms for subject-dependent and leave-one-subject-out settings, respectively, making it a viable system to treat freezing of gait in the real-world.

Indexed as

freezing of gaitneural networkswearable devices

Identifiers

PMID37736618
PMCPMC10513482
OpenAlexW4386944545

What OpenQuestion holds

Textmetadata
LicenceTDM
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.