Integrating motion sensing and wearable, modular high-density diffuse optical tomography: preliminary results Article Swipe
Sabrina Brigadoi
,
Aman Ganglani
,
Hubin Zhao
,
Robert J. Cooper
·
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.1117/12.2527197
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.1117/12.2527197
The next generation of diffuse optical imaging systems will consist of wearable and fiber-less devices, to exploit the advantages of diffuse optical imaging over other functional neuroimaging techniques and meet the needs of users to acquire data in real-world settings. Recently, research at UCL gave rise to a novel, modular high-density diffuse optical tomography (DOT) system that was validated by reconstructing activation images over the motor cortex of a thumb-tofinger extension task. The real question, however, is whether these fiber-less systems can be employed whilst the subject performs real-world activities, that is, whether they can provide reliable signals during participant motion. Integrating motion sensors into modular wearable electronics is straightforward. In this study we acquired DOT and motion sensor data whilst participants performed different activities involving motion. In one acquisition, only accelerometer data were acquired while in the second acquisition, all 9-axis of data (accelerometer, gyroscope and magnetometer data) were acquired. Results demonstrated that acceleration data from motion sensors is not enough to detect motion artifacts whilst performing active movement (e.g., walking), since the global motion obscures any subtle motion artifact. Conversely, by combining accelerometer and gyroscope data it seems possible to detect motion artifacts even during walking, that is when a global motion is present. However, not all types of motion artifacts (e.g., eyebrow raising) could be detected even with this full data configuration. Further studies are required to shed light on this important research question.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1117/12.2527197
- OA Status
- green
- Cited By
- 5
- References
- 5
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2961735042
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W2961735042Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1117/12.2527197Digital Object Identifier
- Title
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Integrating motion sensing and wearable, modular high-density diffuse optical tomography: preliminary resultsWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2019Year of publication
- Publication date
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2019-07-11Full publication date if available
- Authors
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Sabrina Brigadoi, Aman Ganglani, Hubin Zhao, Robert J. CooperList of authors in order
- Landing page
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https://doi.org/10.1117/12.2527197Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://discovery.ucl.ac.uk/10089736/1/1107405%20%281%29.pdfDirect OA link when available
- Concepts
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Computer vision, Accelerometer, Computer science, Artificial intelligence, Motion capture, Wearable computer, Gyroscope, Artifact (error), Modular design, Match moving, Motion (physics), Optical flow, Engineering, Embedded system, Operating system, Aerospace engineering, Image (mathematics)Top concepts (fields/topics) attached by OpenAlex
- Cited by
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5Total citation count in OpenAlex
- Citations by year (recent)
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2022: 1, 2021: 1, 2020: 3Per-year citation counts (last 5 years)
- References (count)
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5Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.task. | 71 |
| abstract_inverted_index.these | 78 |
| abstract_inverted_index.types | 209 |
| abstract_inverted_index.users | 33 |
| abstract_inverted_index.while | 135 |
| abstract_inverted_index.(e.g., | 170, 213 |
| abstract_inverted_index.9-axis | 141 |
| abstract_inverted_index.active | 168 |
| abstract_inverted_index.cortex | 66 |
| abstract_inverted_index.detect | 163, 192 |
| abstract_inverted_index.during | 98, 196 |
| abstract_inverted_index.enough | 161 |
| abstract_inverted_index.global | 174, 202 |
| abstract_inverted_index.images | 62 |
| abstract_inverted_index.motion | 102, 117, 157, 164, 175, 179, 193, 203, 211 |
| abstract_inverted_index.novel, | 48 |
| abstract_inverted_index.second | 138 |
| abstract_inverted_index.sensor | 118 |
| abstract_inverted_index.subtle | 178 |
| abstract_inverted_index.system | 55 |
| abstract_inverted_index.whilst | 84, 120, 166 |
| abstract_inverted_index.Further | 225 |
| abstract_inverted_index.Results | 151 |
| abstract_inverted_index.acquire | 35 |
| abstract_inverted_index.consist | 9 |
| abstract_inverted_index.diffuse | 4, 20, 51 |
| abstract_inverted_index.exploit | 16 |
| abstract_inverted_index.eyebrow | 214 |
| abstract_inverted_index.imaging | 6, 22 |
| abstract_inverted_index.modular | 49, 105 |
| abstract_inverted_index.motion. | 100, 126 |
| abstract_inverted_index.optical | 5, 21, 52 |
| abstract_inverted_index.provide | 95 |
| abstract_inverted_index.sensors | 103, 158 |
| abstract_inverted_index.signals | 97 |
| abstract_inverted_index.studies | 226 |
| abstract_inverted_index.subject | 86 |
| abstract_inverted_index.systems | 7, 80 |
| abstract_inverted_index.whether | 77, 92 |
| abstract_inverted_index.However, | 206 |
| abstract_inverted_index.acquired | 114, 134 |
| abstract_inverted_index.detected | 218 |
| abstract_inverted_index.devices, | 14 |
| abstract_inverted_index.employed | 83 |
| abstract_inverted_index.however, | 75 |
| abstract_inverted_index.movement | 169 |
| abstract_inverted_index.obscures | 176 |
| abstract_inverted_index.performs | 87 |
| abstract_inverted_index.possible | 190 |
| abstract_inverted_index.present. | 205 |
| abstract_inverted_index.raising) | 215 |
| abstract_inverted_index.reliable | 96 |
| abstract_inverted_index.required | 228 |
| abstract_inverted_index.research | 41, 235 |
| abstract_inverted_index.walking, | 197 |
| abstract_inverted_index.wearable | 11, 106 |
| abstract_inverted_index.Recently, | 40 |
| abstract_inverted_index.acquired. | 150 |
| abstract_inverted_index.artifact. | 180 |
| abstract_inverted_index.artifacts | 165, 194, 212 |
| abstract_inverted_index.combining | 183 |
| abstract_inverted_index.different | 123 |
| abstract_inverted_index.extension | 70 |
| abstract_inverted_index.gyroscope | 145, 186 |
| abstract_inverted_index.important | 234 |
| abstract_inverted_index.involving | 125 |
| abstract_inverted_index.performed | 122 |
| abstract_inverted_index.question, | 74 |
| abstract_inverted_index.question. | 236 |
| abstract_inverted_index.settings. | 39 |
| abstract_inverted_index.validated | 58 |
| abstract_inverted_index.walking), | 171 |
| abstract_inverted_index.activation | 61 |
| abstract_inverted_index.activities | 124 |
| abstract_inverted_index.advantages | 18 |
| abstract_inverted_index.fiber-less | 13, 79 |
| abstract_inverted_index.functional | 25 |
| abstract_inverted_index.generation | 2 |
| abstract_inverted_index.performing | 167 |
| abstract_inverted_index.real-world | 38, 88 |
| abstract_inverted_index.techniques | 27 |
| abstract_inverted_index.tomography | 53 |
| abstract_inverted_index.Conversely, | 181 |
| abstract_inverted_index.Integrating | 101 |
| abstract_inverted_index.activities, | 89 |
| abstract_inverted_index.electronics | 107 |
| abstract_inverted_index.participant | 99 |
| abstract_inverted_index.acceleration | 154 |
| abstract_inverted_index.acquisition, | 129, 139 |
| abstract_inverted_index.demonstrated | 152 |
| abstract_inverted_index.high-density | 50 |
| abstract_inverted_index.magnetometer | 147 |
| abstract_inverted_index.neuroimaging | 26 |
| abstract_inverted_index.participants | 121 |
| abstract_inverted_index.accelerometer | 131, 184 |
| abstract_inverted_index.configuration. | 224 |
| abstract_inverted_index.reconstructing | 60 |
| abstract_inverted_index.thumb-tofinger | 69 |
| abstract_inverted_index.(accelerometer, | 144 |
| abstract_inverted_index.straightforward. | 109 |
| cited_by_percentile_year.max | 97 |
| cited_by_percentile_year.min | 89 |
| countries_distinct_count | 0 |
| institutions_distinct_count | 4 |
| citation_normalized_percentile.value | 0.7487845 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |