Gait speed estimation via inertial sensors and machine learning

Salvatore Tedesco; Colum Crowe; Marco Sica; Lorna Kenny; Brendan O'Flynn; David Scott Mueller; Suzanne Timmons; John Barton

doi:10.1016/j.gaitpost.2023.07.243

Gait speed estimation via inertial sensors and machine learning

Salvatore Tedesco
, Colum Crowe
, Marco Sica
, Lorna Kenny
, Brendan O'Flynn
, David Scott Mueller
, Suzanne Timmons
, John Barton

AbbVie

Research output: Contribution to journal › Meeting abstract › peer-review

Abstract

Changes in gait are a well-documented symptom for several conditions, such as degenerative diseases, stroke, foot conditions, neurological conditions (i.e., Parkinson’s). Gait speed is thus a well-known indicator for assessing an individual’s functional mobility. Machine learning (ML) shows great potential in estimating gait speed due to its ability to reveal hidden patterns in large datasets and has already shown promising results over standard strap-down integration methods for foot-mounted inertial sensors (IMU)

Original language	English
Pages (from-to)	s201-s203
Journal	Gait and Posture
Volume	106
Issue number	1
DOIs	https://doi.org/10.1016/j.gaitpost.2023.07.243
Publication status	Published - 13 Sept 2023

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

Inertial measurement unit
Gait
Accelerometer
STRIDE
Preferred walking speed
Computer science
Artificial intelligence
Gait analysis
Barefoot
Physical medicine and rehabilitation
Gyroscope
Effect of gait parameters on energetic cost
Medicine
Engineering
Aerospace engineering
Operating system

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10.1016/j.gaitpost.2023.07.243

Cite this

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title = "Gait speed estimation via inertial sensors and machine learning",

abstract = "Changes in gait are a well-documented symptom for several conditions, such as degenerative diseases, stroke, foot conditions, neurological conditions (i.e., Parkinson{\textquoteright}s). Gait speed is thus a well-known indicator for assessing an individual{\textquoteright}s functional mobility. Machine learning (ML) shows great potential in estimating gait speed due to its ability to reveal hidden patterns in large datasets and has already shown promising results over standard strap-down integration methods for foot-mounted inertial sensors (IMU) ",

keywords = "Inertial measurement unit, Gait, Accelerometer, STRIDE, Preferred walking speed, Computer science, Artificial intelligence, Gait analysis, Barefoot, Physical medicine and rehabilitation, Gyroscope, Effect of gait parameters on energetic cost, Medicine, Engineering, Aerospace engineering, Operating system",

author = "Salvatore Tedesco and Colum Crowe and Marco Sica and Lorna Kenny and Brendan O'Flynn and Mueller, \{David Scott\} and Suzanne Timmons and John Barton",

year = "2023",

month = sep,

day = "13",

doi = "10.1016/j.gaitpost.2023.07.243",

language = "English",

volume = "106",

pages = "s201--s203",

journal = "Gait and Posture",

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TY - JOUR

T1 - Gait speed estimation via inertial sensors and machine learning

AU - Tedesco, Salvatore

AU - Crowe, Colum

AU - Sica, Marco

AU - Kenny, Lorna

AU - O'Flynn, Brendan

AU - Mueller, David Scott

AU - Timmons, Suzanne

AU - Barton, John

PY - 2023/9/13

Y1 - 2023/9/13

N2 - Changes in gait are a well-documented symptom for several conditions, such as degenerative diseases, stroke, foot conditions, neurological conditions (i.e., Parkinson’s). Gait speed is thus a well-known indicator for assessing an individual’s functional mobility. Machine learning (ML) shows great potential in estimating gait speed due to its ability to reveal hidden patterns in large datasets and has already shown promising results over standard strap-down integration methods for foot-mounted inertial sensors (IMU)

AB - Changes in gait are a well-documented symptom for several conditions, such as degenerative diseases, stroke, foot conditions, neurological conditions (i.e., Parkinson’s). Gait speed is thus a well-known indicator for assessing an individual’s functional mobility. Machine learning (ML) shows great potential in estimating gait speed due to its ability to reveal hidden patterns in large datasets and has already shown promising results over standard strap-down integration methods for foot-mounted inertial sensors (IMU)

KW - Inertial measurement unit

KW - Gait

KW - Accelerometer

KW - STRIDE

KW - Preferred walking speed

KW - Computer science

KW - Artificial intelligence

KW - Gait analysis

KW - Barefoot

KW - Physical medicine and rehabilitation

KW - Gyroscope

KW - Effect of gait parameters on energetic cost

KW - Medicine

KW - Engineering

KW - Aerospace engineering

KW - Operating system

UR - https://openalex.org/W4386700731

U2 - 10.1016/j.gaitpost.2023.07.243

DO - 10.1016/j.gaitpost.2023.07.243

M3 - Meeting abstract

SN - 0966-6362

VL - 106

SP - s201-s203

JO - Gait and Posture

JF - Gait and Posture

IS - 1

ER -

Gait speed estimation via inertial sensors and machine learning

Abstract

UN SDGs

Keywords

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