Evidence map›Paper›PMID 39557507›Full record

ArticleMedical engineering & physics2024

Evaluation of a finite state machine algorithm to measure stepping with ankle accelerometry: Performance across a range of gait speeds, tasks, and individual walking ability.

Benjamin F Cornish, Karen Van Ooteghem, Matthew Wong, Kyle S Weber, Frederico Pieruccini-Faria, Manuel Montero-Odasso, William E McIlroy

Abstract readEvaluation Study
PubMed Full text at PMC Publisher
In one paragraph

Article in Medical engineering & physics, 2024. 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. Review
  2. 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

7 authors.

Benjamin F CornishDepartment of Kinesiology and Health Sciences, University of Waterloo, 200 University Ave West, ON, Canada, N2L 3G1. Electronic address: bfcornis@uwaterloo.ca.
Karen Van OoteghemDepartment of Kinesiology and Health Sciences, University of Waterloo, 200 University Ave West, ON, Canada, N2L 3G1. Electronic address: kvanooteghem@uwaterloo.ca.
Matthew WongDepartment of Kinesiology and Health Sciences, University of Waterloo, 200 University Ave West, ON, Canada, N2L 3G1. Electronic address: matthewwong525@gmail.com.
Kyle S WeberDepartment of Kinesiology and Health Sciences, University of Waterloo, 200 University Ave West, ON, Canada, N2L 3G1. Electronic address: kyle.weber@uwaterloo.ca.
Frederico Pieruccini-FariaDepartment of Medicine and Division of Geriatric Medicine, Schulich School of Medicine and Dentistry, The University of Western Ontario, London, ON, Canada. Electronic address: Frederico.Faria@sjhc.london.on.ca.
Manuel Montero-OdassoDepartment of Medicine and Division of Geriatric Medicine, Schulich School of Medicine and Dentistry, The University of Western Ontario, London, ON, Canada. Electronic address: Manuel.MonteroOdasso@sjhc.london.on.ca.
William E McIlroyDepartment of Kinesiology and Health Sciences, University of Waterloo, 200 University Ave West, ON, Canada, N2L 3G1. Electronic address: wmcilroy@uwaterloo.ca.

Funding

Vascular and Behavioral Determinants of Superior Memory Performance from Continuous Monitoring of Everyday ActivitiesU19AG073153 · NIA · UNIVERSITY OF CHICAGO · PI ROGALSKI, EMILY J · 2021 to 2025
$19.1M
NIA NIH HHS U19 AG073153
6 · The paper itself

Abstract

Wearable sensors, including accelerometers, are a widely accepted tool to assess gait in clinical and free-living environments. Methods to identify phases and subphases of the gait cycle are necessary for comprehensive assessment of pathological gait. The current study evaluated the accuracy of a finite state machine (FSM) algorithm to detect strides by identifying gait cycle subphases from ankle-worn accelerometry. Algorithm performance was challenged across a range of speeds (0.4-2.6 m/s), task conditions (e.g., single- and dual-task walking), and individual characteristics. Specifically, the study included a range of treadmill speeds in young adults and overground walking conditions in older adults with neurological disease. Manually counted and algorithm-derived stride detection from acceleration data were evaluated using error analysis and Bland-Altman plots for visualization. Overall, the algorithm successfully detected strides (>96 % accuracy) across gait speed ranges and tasks, for young and older adults. The accuracy of an FSM algorithm combined with ankle-worn accelerometers, provides an analytical approach with affordable and portable tools that permits comprehensive assessment of gait unbounded by setting and proves to perform well in in walking tasks characterized by variable walking. These algorithm capabilities and advancements are critical for identifying phase dependent gait impairments in clinical and free-living assessment.

Indexed as

AccelerometryAlgorithmsAnkleWalkingAdultAgedFemaleGaitHumansMaleMiddle AgedWalking SpeedYoung AdultAccelerometerFinite state machineGaitGait analysisSignal processingWearable sensors

Identifiers

PMID39557507
PMCPMC13527627

What OpenQuestion holds

Textmetadata
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.