Method to collect ground truth data for walking speed in real-world environments: description and validation Article Swipe
Gerhard Aigner
,
Bernd Grimm
,
Christian Lederer
,
Martin Däumer
·
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.7287/peerj.preprints.27558
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.7287/peerj.preprints.27558
Background. Physical activity (PA) is increasingly being recognized as a major factor related to the development or prevention of many diseases, as an intervention to cure or delay disease and for patient assessment in diagnostics, as a clinical outcome measure or clinical trial endpoint. Thus, wearable sensors and signal algorithms to monitor PA in the free-living environment (real-world) are becoming popular in medicine and clinical research. This is especially true for walking speed, a parameter of PA behaviour with increasing evidence to serve as a patient outcome and clinical trial endpoint in many diseases. The development and validation of sensor signal algorithms for PA classification, in particular walking, and deriving specific PA parameters, such as real world walking speed depends on the availability of large reference data sets with ground truth values. In this study a novel, reliable, scalable (high throughput), user-friendly device and method to generate such ground truth data for real world walking speed, other physical activity types and further gait-related parameters in a real-world environment is described and validated. Methods. A surveyor’s wheel was instrumented with a rotating 3D accelerometer (actibelt). A signal processing algorithm is described to derive distance and speed values. In addition, a high-resolution camera was attached via an active gimbal to video record context and detail. Validation was performed in the following main parts: 1) walking distance measurement is compared to the wheel’s built-in mechanical counter, 2) walking speed measurement is analysed on a treadmill at various speed settings, 3) speed measurement accuracy is analysed by an independent certified calibration laboratory - accreditation by DAkkS applying standardised test procedures. Results: The mean relative error for distance measurements between our method and the built-in counter was 0.12%. Comparison of the speed values algorithmically extracted from accelerometry data and true treadmill speed revealed a mean adjusted absolute error of 0.01 m/s (relative error: 0.71 %). The calibration laboratory found a mean relative error between values algorithmically extracted from accelerometry data and laboratory gold standard of 0.36% (0.17-0.64 min/max), which is below the resolution of the laboratory. An official certificate was issued. Discussion. Error values were a magnitude smaller than the any clinically important difference for walking speed. Conclusion. Besides the high accuracy, the presented method can be deployed in a real world setting and allows to be integrated into the digital data flow.
Related Topics
Concepts
Ground truth
Context (archaeology)
Preferred walking speed
Computer science
Accelerometer
Inertial measurement unit
Scalability
Gait
Wearable computer
Artificial intelligence
Simulation
Real-time computing
Physical medicine and rehabilitation
Medicine
Embedded system
Database
Operating system
Biology
Paleontology
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.7287/peerj.preprints.27558
- OA Status
- gold
- Cited By
- 4
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4242864590
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4242864590Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.7287/peerj.preprints.27558Digital Object Identifier
- Title
-
Method to collect ground truth data for walking speed in real-world environments: description and validationWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2019Year of publication
- Publication date
-
2019-02-28Full publication date if available
- Authors
-
Gerhard Aigner, Bernd Grimm, Christian Lederer, Martin DäumerList of authors in order
- Landing page
-
https://doi.org/10.7287/peerj.preprints.27558Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.7287/peerj.preprints.27558Direct OA link when available
- Concepts
-
Ground truth, Context (archaeology), Preferred walking speed, Computer science, Accelerometer, Inertial measurement unit, Scalability, Gait, Wearable computer, Artificial intelligence, Simulation, Real-time computing, Physical medicine and rehabilitation, Medicine, Embedded system, Database, Operating system, Biology, PaleontologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
4Total citation count in OpenAlex
- Citations by year (recent)
-
2021: 3, 2019: 1Per-year citation counts (last 5 years)
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.(PA) | 3 |
| abstract_inverted_index.0.01 | 304 |
| abstract_inverted_index.0.71 | 308 |
| abstract_inverted_index.This | 66 |
| abstract_inverted_index.cure | 25 |
| abstract_inverted_index.data | 126, 150, 292, 324, 386 |
| abstract_inverted_index.from | 290, 322 |
| abstract_inverted_index.gold | 327 |
| abstract_inverted_index.high | 365 |
| abstract_inverted_index.into | 383 |
| abstract_inverted_index.main | 219 |
| abstract_inverted_index.many | 19, 92 |
| abstract_inverted_index.mean | 268, 299, 315 |
| abstract_inverted_index.real | 115, 152, 375 |
| abstract_inverted_index.sets | 127 |
| abstract_inverted_index.such | 113, 147 |
| abstract_inverted_index.test | 264 |
| abstract_inverted_index.than | 353 |
| abstract_inverted_index.this | 133 |
| abstract_inverted_index.true | 69, 294 |
| abstract_inverted_index.were | 349 |
| abstract_inverted_index.with | 78, 128, 178 |
| abstract_inverted_index.(high | 139 |
| abstract_inverted_index.0.36% | 330 |
| abstract_inverted_index.DAkkS | 261 |
| abstract_inverted_index.Error | 347 |
| abstract_inverted_index.Thus, | 44 |
| abstract_inverted_index.being | 6 |
| abstract_inverted_index.below | 335 |
| abstract_inverted_index.delay | 27 |
| abstract_inverted_index.error | 270, 302, 317 |
| abstract_inverted_index.flow. | 387 |
| abstract_inverted_index.found | 313 |
| abstract_inverted_index.large | 124 |
| abstract_inverted_index.major | 10 |
| abstract_inverted_index.other | 156 |
| abstract_inverted_index.serve | 82 |
| abstract_inverted_index.speed | 118, 194, 235, 244, 247, 286, 296 |
| abstract_inverted_index.study | 134 |
| abstract_inverted_index.trial | 42, 89 |
| abstract_inverted_index.truth | 130, 149 |
| abstract_inverted_index.types | 159 |
| abstract_inverted_index.video | 208 |
| abstract_inverted_index.wheel | 175 |
| abstract_inverted_index.which | 333 |
| abstract_inverted_index.world | 116, 153, 376 |
| abstract_inverted_index.0.12%. | 282 |
| abstract_inverted_index.active | 205 |
| abstract_inverted_index.allows | 379 |
| abstract_inverted_index.camera | 200 |
| abstract_inverted_index.derive | 191 |
| abstract_inverted_index.device | 142 |
| abstract_inverted_index.error: | 307 |
| abstract_inverted_index.factor | 11 |
| abstract_inverted_index.gimbal | 206 |
| abstract_inverted_index.ground | 129, 148 |
| abstract_inverted_index.method | 144, 276, 369 |
| abstract_inverted_index.novel, | 136 |
| abstract_inverted_index.parts: | 220 |
| abstract_inverted_index.record | 209 |
| abstract_inverted_index.sensor | 99 |
| abstract_inverted_index.signal | 48, 100, 185 |
| abstract_inverted_index.speed, | 72, 155 |
| abstract_inverted_index.speed. | 361 |
| abstract_inverted_index.values | 287, 319, 348 |
| abstract_inverted_index.Besides | 363 |
| abstract_inverted_index.between | 274, 318 |
| abstract_inverted_index.context | 210 |
| abstract_inverted_index.counter | 280 |
| abstract_inverted_index.depends | 119 |
| abstract_inverted_index.detail. | 212 |
| abstract_inverted_index.digital | 385 |
| abstract_inverted_index.disease | 28 |
| abstract_inverted_index.further | 161 |
| abstract_inverted_index.issued. | 345 |
| abstract_inverted_index.measure | 39 |
| abstract_inverted_index.monitor | 51 |
| abstract_inverted_index.outcome | 38, 86 |
| abstract_inverted_index.patient | 31, 85 |
| abstract_inverted_index.popular | 60 |
| abstract_inverted_index.related | 12 |
| abstract_inverted_index.sensors | 46 |
| abstract_inverted_index.setting | 377 |
| abstract_inverted_index.smaller | 352 |
| abstract_inverted_index.values. | 131, 195 |
| abstract_inverted_index.various | 243 |
| abstract_inverted_index.walking | 71, 117, 154, 222, 234, 360 |
| abstract_inverted_index.Methods. | 172 |
| abstract_inverted_index.Physical | 1 |
| abstract_inverted_index.Results: | 266 |
| abstract_inverted_index.absolute | 301 |
| abstract_inverted_index.accuracy | 249 |
| abstract_inverted_index.activity | 2, 158 |
| abstract_inverted_index.adjusted | 300 |
| abstract_inverted_index.analysed | 238, 251 |
| abstract_inverted_index.applying | 262 |
| abstract_inverted_index.attached | 202 |
| abstract_inverted_index.becoming | 59 |
| abstract_inverted_index.built-in | 230, 279 |
| abstract_inverted_index.clinical | 37, 41, 64, 88 |
| abstract_inverted_index.compared | 226 |
| abstract_inverted_index.counter, | 232 |
| abstract_inverted_index.deployed | 372 |
| abstract_inverted_index.deriving | 109 |
| abstract_inverted_index.distance | 192, 223, 272 |
| abstract_inverted_index.endpoint | 90 |
| abstract_inverted_index.evidence | 80 |
| abstract_inverted_index.generate | 146 |
| abstract_inverted_index.medicine | 62 |
| abstract_inverted_index.official | 342 |
| abstract_inverted_index.physical | 157 |
| abstract_inverted_index.relative | 269, 316 |
| abstract_inverted_index.revealed | 297 |
| abstract_inverted_index.rotating | 180 |
| abstract_inverted_index.scalable | 138 |
| abstract_inverted_index.specific | 110 |
| abstract_inverted_index.standard | 328 |
| abstract_inverted_index.walking, | 107 |
| abstract_inverted_index.wearable | 45 |
| abstract_inverted_index.(relative | 306 |
| abstract_inverted_index.accuracy, | 366 |
| abstract_inverted_index.addition, | 197 |
| abstract_inverted_index.algorithm | 187 |
| abstract_inverted_index.behaviour | 77 |
| abstract_inverted_index.certified | 255 |
| abstract_inverted_index.described | 169, 189 |
| abstract_inverted_index.diseases, | 20 |
| abstract_inverted_index.diseases. | 93 |
| abstract_inverted_index.endpoint. | 43 |
| abstract_inverted_index.extracted | 289, 321 |
| abstract_inverted_index.following | 218 |
| abstract_inverted_index.important | 357 |
| abstract_inverted_index.magnitude | 351 |
| abstract_inverted_index.min/max), | 332 |
| abstract_inverted_index.parameter | 74 |
| abstract_inverted_index.performed | 215 |
| abstract_inverted_index.presented | 368 |
| abstract_inverted_index.reference | 125 |
| abstract_inverted_index.reliable, | 137 |
| abstract_inverted_index.research. | 65 |
| abstract_inverted_index.settings, | 245 |
| abstract_inverted_index.treadmill | 241, 295 |
| abstract_inverted_index.wheel’s | 229 |
| abstract_inverted_index.(0.17-0.64 | 331 |
| abstract_inverted_index.Comparison | 283 |
| abstract_inverted_index.Validation | 213 |
| abstract_inverted_index.algorithms | 49, 101 |
| abstract_inverted_index.assessment | 32 |
| abstract_inverted_index.clinically | 356 |
| abstract_inverted_index.difference | 358 |
| abstract_inverted_index.especially | 68 |
| abstract_inverted_index.increasing | 79 |
| abstract_inverted_index.integrated | 382 |
| abstract_inverted_index.laboratory | 257, 312, 326 |
| abstract_inverted_index.mechanical | 231 |
| abstract_inverted_index.parameters | 163 |
| abstract_inverted_index.particular | 106 |
| abstract_inverted_index.prevention | 17 |
| abstract_inverted_index.processing | 186 |
| abstract_inverted_index.real-world | 166 |
| abstract_inverted_index.recognized | 7 |
| abstract_inverted_index.resolution | 337 |
| abstract_inverted_index.validated. | 171 |
| abstract_inverted_index.validation | 97 |
| abstract_inverted_index.(actibelt). | 183 |
| abstract_inverted_index.Background. | 0 |
| abstract_inverted_index.Conclusion. | 362 |
| abstract_inverted_index.Discussion. | 346 |
| abstract_inverted_index.calibration | 256, 311 |
| abstract_inverted_index.certificate | 343 |
| abstract_inverted_index.development | 15, 95 |
| abstract_inverted_index.environment | 56, 167 |
| abstract_inverted_index.free-living | 55 |
| abstract_inverted_index.independent | 254 |
| abstract_inverted_index.laboratory. | 340 |
| abstract_inverted_index.measurement | 224, 236, 248 |
| abstract_inverted_index.parameters, | 112 |
| abstract_inverted_index.procedures. | 265 |
| abstract_inverted_index.(real-world) | 57 |
| abstract_inverted_index.availability | 122 |
| abstract_inverted_index.diagnostics, | 34 |
| abstract_inverted_index.gait-related | 162 |
| abstract_inverted_index.increasingly | 5 |
| abstract_inverted_index.instrumented | 177 |
| abstract_inverted_index.intervention | 23 |
| abstract_inverted_index.measurements | 273 |
| abstract_inverted_index.standardised | 263 |
| abstract_inverted_index.surveyor’s | 174 |
| abstract_inverted_index.throughput), | 140 |
| abstract_inverted_index.accelerometer | 182 |
| abstract_inverted_index.accelerometry | 291, 323 |
| abstract_inverted_index.accreditation | 259 |
| abstract_inverted_index.user-friendly | 141 |
| abstract_inverted_index.algorithmically | 288, 320 |
| abstract_inverted_index.classification, | 104 |
| abstract_inverted_index.high-resolution | 199 |
| cited_by_percentile_year.max | 97 |
| cited_by_percentile_year.min | 90 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 4 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/11 |
| sustainable_development_goals[0].score | 0.5 |
| sustainable_development_goals[0].display_name | Sustainable cities and communities |
| citation_normalized_percentile.value | 0.60762956 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |