{"doi":"10.1101/2021.12.01.470263","title":"Rethinking Margin of Stability: Incorporating Step-To-Step Regulation to Resolve the Paradox","abstract":"ABSTRACT Derived from inverted pendulum dynamics, mediolateral Margin of Stability ( MoS ML ) is a mechanically-grounded measure of instantaneous stability. However, average MoS ML measures yield paradoxical results. Gait pathologies or perturbations often induce larger (supposedly “more stable”) average MoS ML , despite clearly de stabilizing factors. However, people do not walk “on average” – they walk (and sometimes lose balance) one step at a time. We assert the paradox arises because averaging discards step-to-step dynamics. We present a framework unifying the inverted pendulum with Goal-Equivalent Manifold (GEM) analyses. We identify in the pendulum’s center-of-mass dynamics constant- MoS ML manifolds, including one candidate “stability GEM” signifying the goal to maintain some constant . We used this framework to assess step-to-step MoS ML dynamics of humans walking in destabilizing environments. While goal-relevant deviations were readily corrected, humans did not exploit equifinality by allowing deviations to persist along this GEM. Thus, maintaining a constant is inconsistent with observed step-to-step fluctuations in center-of-mass states. Conversely, the extent to which participants regulated fluctuations in foot placements strongly predicted regulation of center-of-mass fluctuations. Thus, center-of-mass dynamics may arise in directly as a consequence of regulating mediolateral foot placements. To resolve the paradox caused by averaging MoS ML , we present a new statistic, Probability of Instability ( PoI L ), to predict instability likelihood. Participants exhibited increased PoI L when de stabilized (p = 9.45×10 −34 ), despite exhibiting larger (“more stable”) average MoS ML (p = 1.70×10 −15 ). Thus, PoI L correctly captured people’s increased risk of losing lateral balance, whereas average MoS ML did not. PoI L also explains why peoples’ average MoS ML increased in destabilizing contexts.","journal":"bioRxiv (Cold Spring Harbor Laboratory)","year":2021,"id":218056,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":3,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9505,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2021-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":334659,"name":"Joseph P. Cusumano","orcid":"0000-0001-5451-6721","position":1,"is_corresponding":false},{"id":334660,"name":"Jonathan B. Dingwell","orcid":"0000-0001-6990-4153","position":2,"is_corresponding":false},{"id":334658,"name":"Meghan E. Kazanski","orcid":"0000-0002-0675-9160","position":0,"is_corresponding":true}],"reference_count":54,"raw_metadata":null,"created_at":"2026-07-18T23:53:24.683898Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}