Simplified markerless stride detection pipeline (sMaSDP) for surface EMG segmentation Article Swipe
Rafael Castro Aguiar
,
Edward Jero
,
Samit Chakrabarty
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2302.04243
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2302.04243
People with mobility impairments are often recommended for gait assessment studies to diagnose their condition and to select appropriate physiotherapy to improve their mobility. These studies are often conducted in clinical or lab settings, where subjects are assessed in a foreign environment, which may influence their motivation, coordination and overall mobility. Alternatively, if the subject's gait could be assessed in their daily-lives, in unconstrained settings, a more naturalistic gait assessment could be performed. Kinematic analysis of a gait pattern on its own may not be sufficient to characterise a subject's mobility. To better diagnose gait deficiencies, analysis of the patient's muscle activity should be conducted as well. To do so, gait studies should collect, synchronously, Electromyography (EMG) and kinematic data. This method introduces a simplified markerless gait event detection pipeline for the segmentation of EMG signals, via synchronously recorded Inertial Measurement Unit (IMU) data. In an unconstrained walking experiment, healthy subjects walk through a designed course with their kinematic and EMG data recorded. This course comprises 5 different walking modalities (level walking, ramp up/down, staircase up/down), mimicking everyday walking. Through timepoint matching, segmentation and filtering, we generate an algorithm that detects heel-strike (HS) events using a single IMU, and isolates EMG activity of gait cycles, in the different walking modalities. This gait event detection algorithm can be adapted to different datasets, and was tested in both healthy and Parkinson's Disease (PD) gait. Results demonstrate the extracted muscle activity levels in a healthy subject's level ground walking, and the extracted HS events of a PD patient. Adjustments to algorithm parameters are possible (e.g., expected velocity, cadence) and can further increase the detection accuracy.
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Metadata
- Type
- preprint
- Language
- en
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4319793995
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4319793995Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2302.04243Digital Object Identifier
- Title
-
Simplified markerless stride detection pipeline (sMaSDP) for surface EMG segmentationWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-02-08Full publication date if available
- Authors
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Rafael Castro Aguiar, Edward Jero, Samit ChakrabartyList of authors in order
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2302.04243Direct OA link when available
- Concepts
-
Gait, Inertial measurement unit, Physical medicine and rehabilitation, STRIDE, Segmentation, Kinematics, Computer science, Gait analysis, Electromyography, Artificial intelligence, Medicine, Classical mechanics, PhysicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.walking. | 178 |
| abstract_inverted_index.Kinematic | 73 |
| abstract_inverted_index.accuracy. | 271 |
| abstract_inverted_index.algorithm | 188, 214, 257 |
| abstract_inverted_index.comprises | 165 |
| abstract_inverted_index.condition | 14 |
| abstract_inverted_index.conducted | 28, 104 |
| abstract_inverted_index.datasets, | 220 |
| abstract_inverted_index.detection | 128, 213, 270 |
| abstract_inverted_index.different | 167, 207, 219 |
| abstract_inverted_index.extracted | 235, 248 |
| abstract_inverted_index.influence | 44 |
| abstract_inverted_index.kinematic | 118, 158 |
| abstract_inverted_index.matching, | 181 |
| abstract_inverted_index.mimicking | 176 |
| abstract_inverted_index.mobility. | 23, 50, 90 |
| abstract_inverted_index.patient's | 99 |
| abstract_inverted_index.recorded. | 162 |
| abstract_inverted_index.settings, | 33, 64 |
| abstract_inverted_index.staircase | 174 |
| abstract_inverted_index.subject's | 54, 89, 242 |
| abstract_inverted_index.timepoint | 180 |
| abstract_inverted_index.up/down), | 175 |
| abstract_inverted_index.velocity, | 263 |
| abstract_inverted_index.assessment | 9, 69 |
| abstract_inverted_index.filtering, | 184 |
| abstract_inverted_index.introduces | 122 |
| abstract_inverted_index.markerless | 125 |
| abstract_inverted_index.modalities | 169 |
| abstract_inverted_index.parameters | 258 |
| abstract_inverted_index.performed. | 72 |
| abstract_inverted_index.simplified | 124 |
| abstract_inverted_index.sufficient | 85 |
| abstract_inverted_index.Adjustments | 255 |
| abstract_inverted_index.Measurement | 140 |
| abstract_inverted_index.Parkinson's | 228 |
| abstract_inverted_index.appropriate | 18 |
| abstract_inverted_index.demonstrate | 233 |
| abstract_inverted_index.experiment, | 148 |
| abstract_inverted_index.heel-strike | 191 |
| abstract_inverted_index.impairments | 3 |
| abstract_inverted_index.modalities. | 209 |
| abstract_inverted_index.motivation, | 46 |
| abstract_inverted_index.recommended | 6 |
| abstract_inverted_index.characterise | 87 |
| abstract_inverted_index.coordination | 47 |
| abstract_inverted_index.daily-lives, | 61 |
| abstract_inverted_index.environment, | 41 |
| abstract_inverted_index.naturalistic | 67 |
| abstract_inverted_index.segmentation | 132, 182 |
| abstract_inverted_index.deficiencies, | 95 |
| abstract_inverted_index.physiotherapy | 19 |
| abstract_inverted_index.synchronously | 137 |
| abstract_inverted_index.unconstrained | 63, 146 |
| abstract_inverted_index.Alternatively, | 51 |
| abstract_inverted_index.synchronously, | 114 |
| abstract_inverted_index.Electromyography | 115 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 3 |
| citation_normalized_percentile |