Evidence map›Paper›PMID 38487777›Full record

ArticleWearable technologies2023

Closing the Wearable Gap: Foot-ankle kinematic modeling via deep learning models based on a smart sock wearable.

Samaneh Davarzani, David Saucier, Purva Talegaonkar, Erin Parker, Alana Turner, Carver Middleton, Will Carroll, John E Ball, Ali Gurbuz, Harish Chander and 4 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Wearable technologies, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Technological advances in lower-limb tele-rehabilitation: A review of literature.Journal of rehabilitation and assistive technologies engineering
    Review
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

14 authors.

Samaneh DavarzaniDepartment of Industrial and Systems Engineering, Mississippi State University, Mississippi State, MS, USA.
David SaucierHuman Factors and Athlete Engineering, Center for Advanced Vehicular Systems, Mississippi State University, Mississippi State, MS, USA.ORCID https://orcid.org/0000-0002-0555-5246
Purva TalegaonkarDepartment of Industrial and Systems Engineering, Mississippi State University, Mississippi State, MS, USA.
Erin ParkerDepartment of Industrial and Systems Engineering, Mississippi State University, Mississippi State, MS, USA.
Alana TurnerHuman Factors and Athlete Engineering, Center for Advanced Vehicular Systems, Mississippi State University, Mississippi State, MS, USA.
Carver MiddletonHuman Factors and Athlete Engineering, Center for Advanced Vehicular Systems, Mississippi State University, Mississippi State, MS, USA.
Will CarrollHuman Factors and Athlete Engineering, Center for Advanced Vehicular Systems, Mississippi State University, Mississippi State, MS, USA.
John E BallHuman Factors and Athlete Engineering, Center for Advanced Vehicular Systems, Mississippi State University, Mississippi State, MS, USA.
Ali GurbuzDepartment of Electrical and Computer Engineering, Mississippi State University, Mississippi State, MS, USA.
Harish ChanderHuman Factors and Athlete Engineering, Center for Advanced Vehicular Systems, Mississippi State University, Mississippi State, MS, USA.
Reuben F BurchDepartment of Industrial and Systems Engineering, Mississippi State University, Mississippi State, MS, USA.
Brian K SmithDepartment of Industrial and Systems Engineering, Mississippi State University, Mississippi State, MS, USA.
Adam KnightHuman Factors and Athlete Engineering, Center for Advanced Vehicular Systems, Mississippi State University, Mississippi State, MS, USA.
Charles FreemanHuman Factors and Athlete Engineering, Center for Advanced Vehicular Systems, Mississippi State University, Mississippi State, MS, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The development of wearable technology, which enables motion tracking analysis for human movement outside the laboratory, can improve awareness of personal health and performance. This study used a wearable smart sock prototype to track foot-ankle kinematics during gait movement. Multivariable linear regression and two deep learning models, including long short-term memory (LSTM) and convolutional neural networks, were trained to estimate the joint angles in sagittal and frontal planes measured by an optical motion capture system. Participant-specific models were established for ten healthy subjects walking on a treadmill. The prototype was tested at various walking speeds to assess its ability to track movements for multiple speeds and generalize models for estimating joint angles in sagittal and frontal planes. LSTM outperformed other models with lower mean absolute error (MAE), lower root mean squared error, and higher

Indexed as

biomechanicsembedded electronicsperformance characterizationsensorssoft wearable robotics

Identifiers

PMID38487777
PMCPMC10936318

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.