Evidence map›Paper›PMID 34113654›Full record

ArticleFrontiers in robotics and AI2021

DeepFoG: An IMU-Based Detection of Freezing of Gait Episodes in Parkinson's Disease Patients via Deep Learning.

Thomas Bikias, Dimitrios Iakovakis, Stelios Hadjidimitriou, Vasileios Charisis, Leontios J Hadjileontiadis

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in robotics and AI, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
32citing papers in PubMed, 4 pooled it
27.4field-weighted citation impact, top 1% 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

32 citing papers in PubMed, 4 syntheses or guidelines pooled it, 103 citations in OpenAlex.

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  18. 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
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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 1 institution in 2 countries.

Thomas BikiasDepartment of Electrical and Computer Engineering, Aristotle University of Thessaloniki, Thessaloniki, Greece.
Dimitrios IakovakisDepartment of Electrical and Computer Engineering, Aristotle University of Thessaloniki, Thessaloniki, Greece.
Stelios HadjidimitriouDepartment of Electrical and Computer Engineering, Aristotle University of Thessaloniki, Thessaloniki, Greece.
Vasileios CharisisDepartment of Electrical and Computer Engineering, Aristotle University of Thessaloniki, Thessaloniki, Greece.
Leontios J HadjileontiadisDepartment of Electrical and Computer Engineering, Aristotle University of Thessaloniki, Thessaloniki, Greece.
Aristotle University of Thessaloniki · GR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Freezing of Gait (FoG) is a movement disorder that mostly appears in the late stages of Parkinson's Disease (PD). It causes incapability of walking, despite the PD patient's intention, resulting in loss of coordination that increases the risk of falls and injuries and severely affects the PD patient's quality of life. Stress, emotional stimulus, and multitasking have been encountered to be associated with the appearance of FoG episodes, while the patient's functionality and self-confidence are constantly deteriorating. This study suggests a non-invasive method for detecting FoG episodes, by analyzing inertial measurement unit (IMU) data. Specifically, accelerometer and gyroscope data from 11 PD subjects, as captured from a single wrist-worn IMU sensor during continuous walking, are processed via Deep Learning for window-based detection of the FoG events. The proposed approach, namely DeepFoG, was evaluated in a Leave-One-Subject-Out (LOSO) cross-validation (CV) and 10-fold CV fashion schemes against its ability to correctly estimate the existence or not of a FoG episode at each data window. Experimental results have shown that DeepFoG performs satisfactorily, as it achieves 83%/88% and 86%/90% sensitivity/specificity, for LOSO CV and 10-fold CV schemes, respectively. The promising performance of the proposed DeepFoG reveals the potentiality of single-arm IMU-based real-time FoG detection that could guide effective interventions via stimuli, such as rhythmic auditory stimulation (RAS) and hand vibration. In this way, DeepFoG may scaffold the elimination of risk of falls in PD patients, sustaining their quality of life in everyday living activities.

Indexed as

deepFoGdeep learningfreezing of gaitParkinson's diseaserhythmic auditory stimulationsmartwatch

Identifiers

PMID34113654
PMCPMC8185568
OpenAlexW3158621908

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.