Evidence map›Paper›PMID 31396072›Full record

ArticleFrontiers in neurorobotics2019

Stance and Swing Detection Based on the Angular Velocity of Lower Limb Segments During Walking.

Martin Grimmer, Kai Schmidt, Jaime E Duarte, Lukas Neuner, Gleb Koginov, Robert Riener

Abstract read
In one paragraph

Article in Frontiers in neurorobotics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
24citing papers in PubMed, 1 pooled it
–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

24 citing papers in PubMed, 1 synthesis or guideline pooled it.

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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

6 authors.

Martin GrimmerLauflabor Locomotion Laboratory, Department of Human Sciences, Institute of Sports Science, Technische Universität Darmstadt, Darmstadt, Germany.
Kai SchmidtSensory-Motor Systems (SMS) Lab, Department of Health Sciences and Technology, Institute of Robotics and Intelligent Systems (IRIS), ETH Zurich, Zurich, Switzerland.
Jaime E DuarteSensory-Motor Systems (SMS) Lab, Department of Health Sciences and Technology, Institute of Robotics and Intelligent Systems (IRIS), ETH Zurich, Zurich, Switzerland.
Lukas NeunerSensory-Motor Systems (SMS) Lab, Department of Health Sciences and Technology, Institute of Robotics and Intelligent Systems (IRIS), ETH Zurich, Zurich, Switzerland.
Gleb KoginovSensory-Motor Systems (SMS) Lab, Department of Health Sciences and Technology, Institute of Robotics and Intelligent Systems (IRIS), ETH Zurich, Zurich, Switzerland.
Robert RienerSensory-Motor Systems (SMS) Lab, Department of Health Sciences and Technology, Institute of Robotics and Intelligent Systems (IRIS), ETH Zurich, Zurich, Switzerland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Lower limb exoskeletons require the correct support magnitude and timing to achieve user assistance. This study evaluated whether the sign of the angular velocity of lower limb segments can be used to determine the timing of the stance and the swing phase during walking. We assumed that stance phase is characterized by a positive, swing phase by a negative angular velocity. Thus, the transitions can be used to also identify heel-strike and toe-off. Thirteen subjects without gait impairments walked on a treadmill at speeds between 0.5 and 2.1 m/s on level ground and inclinations between -10 and +10°. Kinematic and kinetic data was measured simultaneously from an optical motion capture system, force plates, and five inertial measurement units (IMUs). These recordings were used to compute the angular velocities of four lower limb segments: two biological (thigh, shank) and two virtual that were geometrical projections of the biological segments (virtual leg, virtual extended leg). We analyzed the reliability (two sign changes of the angular velocity per stride) and the accuracy (offset in timing between sign change and ground reaction force based timing) of the virtual and biological segments for detecting the gait phases stance and swing. The motion capture data revealed that virtual limb segments seem superior to the biological limb segments in the reliability of stance and swing detection. However, increased signal noise when using the IMUs required additional rule sets for reliable stance and swing detection. With IMUs, the biological shank segment had the least variability in accuracy. The IMU-based heel-strike events of the shank and both virtual segment were slightly early (3.3-4.8% of the gait cycle) compared to the ground reaction force-based timing. Toe-off event timing showed more variability (9.0% too early to 7.3% too late) between the segments and changed with walking speed. The results show that the detection of the heel-strike, and thus stance phase, based on IMU angular velocity is possible for different segments when additional rule sets are included. Further work is required to improve the timing accuracy for the toe-off detection (swing).

Indexed as

controldetectionexoskeletongait phaseinertial measurement unitstanceswingwalking

Identifiers

PMID31396072
PMCPMC6667673

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

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.