Benchmarking the predictive capability of human gait simulations Article Swipe
Maarten Afschrift
,
Dinant Kistemaker
,
Friedl De Groote
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1371/journal.pcbi.1012713
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1371/journal.pcbi.1012713
Physics-based simulation generate movement patterns based on a neuro-musculoskeletal model without relying on experimental movement data, offering a powerful approach to study how neuro-musculoskeletal properties shape locomotion. Yet, simulated gait patterns and metabolic powers do not always agree with experiments, pointing to modeling errors reflecting gaps in our understanding. Here, we systematically evaluated the predictive capability of simulations based on a 3D musculoskeletal model to predict gait mechanics, muscle activity, and metabolic power across gait conditions. We simulated the effect of adding mass to body segments, variations in walking speed, inclined walking, and crouched walking. We chose tasks that are relatively straightforward to model to limit the contribution of errors in modeling the task to prediction errors. The simulations predicted stride frequency and walking kinematics with reasonable accuracy but underestimated variation in metabolic power across conditions. In particular, simulations underestimated changes in metabolic power with respect to level walking in tasks requiring substantial positive mechanical work, such as incline walking (27% underestimation). We identified two possible errors in simulated metabolic power. First, the phenomenological metabolic power model produced high maximal mechanical efficiency (average 0.58) during concentric contractions, compared to the observed 0.2–0.3 in laboratory experiments. Second, when we multiplied the mechanical work with more realistic estimates of mechanical efficiency (i.e., 0.25), simulations overestimated the metabolic power by 84%. This suggests that positive work by muscle fibers was overestimated in the simulations. This overestimation may be caused by several assumptions and errors in (the parameters of) the musculoskeletal model including its interaction with the environment or in the cost function. This study highlights the need for more accurate models of musculoskeletal mechanics, energetics, passive elastic structures, and neural control (e.g., optimality criteria) to improve the realism of human movement simulations. Validating simulations across a broad range of conditions is important to pinpoint shortcomings in model-based simulations.
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- en
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- https://doi.org/10.1371/journal.pcbi.1012713
- OA Status
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- 51
- OpenAlex ID
- https://openalex.org/W4416286869
All OpenAlex metadata
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https://doi.org/10.1371/journal.pcbi.1012713Digital Object Identifier
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Benchmarking the predictive capability of human gait simulationsWork title
- Type
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articleOpenAlex work type
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enPrimary language
- Publication year
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2025Year of publication
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2025-11-17Full publication date if available
- Authors
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Maarten Afschrift, Dinant Kistemaker, Friedl De GrooteList of authors in order
- Landing page
-
https://doi.org/10.1371/journal.pcbi.1012713Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.1371/journal.pcbi.1012713Direct OA link when available
- Cited by
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0Total citation count in OpenAlex
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51Number of works referenced by this work
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| publication_date | 2025-11-17 |
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| abstract_inverted_index.a | 7, 17, 60, 292 |
| abstract_inverted_index.3D | 61 |
| abstract_inverted_index.In | 136 |
| abstract_inverted_index.We | 76, 95, 162 |
| abstract_inverted_index.as | 157 |
| abstract_inverted_index.be | 234 |
| abstract_inverted_index.by | 216, 223, 236 |
| abstract_inverted_index.do | 34 |
| abstract_inverted_index.in | 46, 87, 110, 131, 141, 149, 167, 192, 228, 241, 255, 302 |
| abstract_inverted_index.is | 297 |
| abstract_inverted_index.of | 56, 80, 108, 206, 268, 285, 295 |
| abstract_inverted_index.on | 6, 12, 59 |
| abstract_inverted_index.or | 254 |
| abstract_inverted_index.to | 20, 41, 64, 83, 102, 104, 114, 146, 188, 281, 299 |
| abstract_inverted_index.we | 50, 197 |
| abstract_inverted_index.The | 117 |
| abstract_inverted_index.and | 31, 70, 92, 122, 239, 275 |
| abstract_inverted_index.are | 99 |
| abstract_inverted_index.but | 128 |
| abstract_inverted_index.for | 264 |
| abstract_inverted_index.how | 22 |
| abstract_inverted_index.its | 249 |
| abstract_inverted_index.may | 233 |
| abstract_inverted_index.not | 35 |
| abstract_inverted_index.of) | 244 |
| abstract_inverted_index.our | 47 |
| abstract_inverted_index.the | 53, 78, 106, 112, 172, 189, 199, 213, 229, 245, 252, 256, 262, 283 |
| abstract_inverted_index.two | 164 |
| abstract_inverted_index.was | 226 |
| abstract_inverted_index.(27% | 160 |
| abstract_inverted_index.(the | 242 |
| abstract_inverted_index.84%. | 217 |
| abstract_inverted_index.This | 218, 231, 259 |
| abstract_inverted_index.Yet, | 27 |
| abstract_inverted_index.body | 84 |
| abstract_inverted_index.cost | 257 |
| abstract_inverted_index.gait | 29, 66, 74 |
| abstract_inverted_index.gaps | 45 |
| abstract_inverted_index.high | 178 |
| abstract_inverted_index.mass | 82 |
| abstract_inverted_index.more | 203, 265 |
| abstract_inverted_index.need | 263 |
| abstract_inverted_index.such | 156 |
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| abstract_inverted_index.that | 98, 220 |
| abstract_inverted_index.when | 196 |
| abstract_inverted_index.with | 38, 125, 144, 202, 251 |
| abstract_inverted_index.work | 201, 222 |
| abstract_inverted_index.0.58) | 183 |
| abstract_inverted_index.Here, | 49 |
| abstract_inverted_index.agree | 37 |
| abstract_inverted_index.based | 5, 58 |
| abstract_inverted_index.broad | 293 |
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| abstract_inverted_index.data, | 15 |
| abstract_inverted_index.human | 286 |
| abstract_inverted_index.level | 147 |
| abstract_inverted_index.limit | 105 |
| abstract_inverted_index.model | 9, 63, 103, 176, 247 |
| abstract_inverted_index.power | 72, 133, 143, 175, 215 |
| abstract_inverted_index.range | 294 |
| abstract_inverted_index.shape | 25 |
| abstract_inverted_index.study | 21, 260 |
| abstract_inverted_index.tasks | 97, 150 |
| abstract_inverted_index.work, | 155 |
| abstract_inverted_index.(e.g., | 278 |
| abstract_inverted_index.(i.e., | 209 |
| abstract_inverted_index.0.25), | 210 |
| abstract_inverted_index.First, | 171 |
| abstract_inverted_index.across | 73, 134, 291 |
| abstract_inverted_index.adding | 81 |
| abstract_inverted_index.always | 36 |
| abstract_inverted_index.caused | 235 |
| abstract_inverted_index.during | 184 |
| abstract_inverted_index.effect | 79 |
| abstract_inverted_index.errors | 43, 109, 166, 240 |
| abstract_inverted_index.fibers | 225 |
| abstract_inverted_index.models | 267 |
| abstract_inverted_index.muscle | 68, 224 |
| abstract_inverted_index.neural | 276 |
| abstract_inverted_index.power. | 170 |
| abstract_inverted_index.powers | 33 |
| abstract_inverted_index.speed, | 89 |
| abstract_inverted_index.stride | 120 |
| abstract_inverted_index.Second, | 195 |
| abstract_inverted_index.changes | 140 |
| abstract_inverted_index.control | 277 |
| abstract_inverted_index.elastic | 273 |
| abstract_inverted_index.errors. | 116 |
| abstract_inverted_index.improve | 282 |
| abstract_inverted_index.incline | 158 |
| abstract_inverted_index.maximal | 179 |
| abstract_inverted_index.passive | 272 |
| abstract_inverted_index.predict | 65 |
| abstract_inverted_index.realism | 284 |
| abstract_inverted_index.relying | 11 |
| abstract_inverted_index.respect | 145 |
| abstract_inverted_index.several | 237 |
| abstract_inverted_index.walking | 88, 123, 148, 159 |
| abstract_inverted_index.without | 10 |
| abstract_inverted_index.(average | 182 |
| abstract_inverted_index.accuracy | 127 |
| abstract_inverted_index.accurate | 266 |
| abstract_inverted_index.approach | 19 |
| abstract_inverted_index.compared | 187 |
| abstract_inverted_index.crouched | 93 |
| abstract_inverted_index.generate | 2 |
| abstract_inverted_index.inclined | 90 |
| abstract_inverted_index.modeling | 42, 111 |
| abstract_inverted_index.movement | 3, 14, 287 |
| abstract_inverted_index.observed | 190 |
| abstract_inverted_index.offering | 16 |
| abstract_inverted_index.patterns | 4, 30 |
| abstract_inverted_index.pinpoint | 300 |
| abstract_inverted_index.pointing | 40 |
| abstract_inverted_index.positive | 153, 221 |
| abstract_inverted_index.possible | 165 |
| abstract_inverted_index.powerful | 18 |
| abstract_inverted_index.produced | 177 |
| abstract_inverted_index.suggests | 219 |
| abstract_inverted_index.walking, | 91 |
| abstract_inverted_index.walking. | 94 |
| abstract_inverted_index.0.2–0.3 | 191 |
| abstract_inverted_index.activity, | 69 |
| abstract_inverted_index.criteria) | 280 |
| abstract_inverted_index.estimates | 205 |
| abstract_inverted_index.evaluated | 52 |
| abstract_inverted_index.frequency | 121 |
| abstract_inverted_index.function. | 258 |
| abstract_inverted_index.important | 298 |
| abstract_inverted_index.including | 248 |
| abstract_inverted_index.metabolic | 32, 71, 132, 142, 169, 174, 214 |
| abstract_inverted_index.predicted | 119 |
| abstract_inverted_index.realistic | 204 |
| abstract_inverted_index.requiring | 151 |
| abstract_inverted_index.segments, | 85 |
| abstract_inverted_index.simulated | 28, 77, 168 |
| abstract_inverted_index.variation | 130 |
| abstract_inverted_index.Validating | 289 |
| abstract_inverted_index.capability | 55 |
| abstract_inverted_index.concentric | 185 |
| abstract_inverted_index.conditions | 296 |
| abstract_inverted_index.efficiency | 181, 208 |
| abstract_inverted_index.highlights | 261 |
| abstract_inverted_index.identified | 163 |
| abstract_inverted_index.kinematics | 124 |
| abstract_inverted_index.laboratory | 193 |
| abstract_inverted_index.mechanical | 154, 180, 200, 207 |
| abstract_inverted_index.mechanics, | 67, 270 |
| abstract_inverted_index.multiplied | 198 |
| abstract_inverted_index.optimality | 279 |
| abstract_inverted_index.parameters | 243 |
| abstract_inverted_index.prediction | 115 |
| abstract_inverted_index.predictive | 54 |
| abstract_inverted_index.properties | 24 |
| abstract_inverted_index.reasonable | 126 |
| abstract_inverted_index.reflecting | 44 |
| abstract_inverted_index.relatively | 100 |
| abstract_inverted_index.simulation | 1 |
| abstract_inverted_index.variations | 86 |
| abstract_inverted_index.assumptions | 238 |
| abstract_inverted_index.conditions. | 75, 135 |
| abstract_inverted_index.energetics, | 271 |
| abstract_inverted_index.environment | 253 |
| abstract_inverted_index.interaction | 250 |
| abstract_inverted_index.locomotion. | 26 |
| abstract_inverted_index.model-based | 303 |
| abstract_inverted_index.particular, | 137 |
| abstract_inverted_index.simulations | 57, 118, 138, 211, 290 |
| abstract_inverted_index.structures, | 274 |
| abstract_inverted_index.substantial | 152 |
| abstract_inverted_index.contribution | 107 |
| abstract_inverted_index.experimental | 13 |
| abstract_inverted_index.experiments, | 39 |
| abstract_inverted_index.experiments. | 194 |
| abstract_inverted_index.shortcomings | 301 |
| abstract_inverted_index.simulations. | 230, 288, 304 |
| abstract_inverted_index.Physics-based | 0 |
| abstract_inverted_index.contractions, | 186 |
| abstract_inverted_index.overestimated | 212, 227 |
| abstract_inverted_index.overestimation | 232 |
| abstract_inverted_index.systematically | 51 |
| abstract_inverted_index.underestimated | 129, 139 |
| abstract_inverted_index.understanding. | 48 |
| abstract_inverted_index.musculoskeletal | 62, 246, 269 |
| abstract_inverted_index.straightforward | 101 |
| abstract_inverted_index.phenomenological | 173 |
| abstract_inverted_index.underestimation). | 161 |
| abstract_inverted_index.neuro-musculoskeletal | 8, 23 |
| cited_by_percentile_year | |
| corresponding_author_ids | https://openalex.org/A5002971798, https://openalex.org/A5000084981, https://openalex.org/A5044782627 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 3 |
| corresponding_institution_ids | https://openalex.org/I865915315, https://openalex.org/I99464096 |
| citation_normalized_percentile |