Biology subjects

Hausdorff, J. M.

Publications and source records attributed to Hausdorff, J. M..

3 recordsLinked to original sources

bioRxiv2025

How do we tread? Differences in stability-related foot placement control between overground and treadmill walking in young adults

Step-by-step foot placement control, accommodating for natural variations in center-of-mass state, ensures stability during steady-state gait. Current understanding of this foot placement mechanisms is primarily based on findings during treadmill walking. However, contextual differences might hamper generalization of these findings towards overground walking, and ultimately daily life gait. This study investigated whether foot placement control manifests itself differently during overground as compared to treadmill walking in healthy young adults. 14 young adults walked at comfortable walking speed, both on the treadmill and overground in a figure-8 path. During overground walking we found a significant relationship between the step width/step length and the center-of-mass state during the swing phase of walking, capturing foot placement control with the same linear model as during treadmill walking. Contrary to what was hypothesized, we found a significant lower foot placement precision during overground walking for the step width model with center-of-mass state at the start of the swing phase as predictor, complemented by a wider average step width. Moreover, during overground walking participants responded less strongly to a deviation in center-of-mass position, yet significantly stronger to deviations in center-of-mass velocity at the end of the step for both the step width/step length models. Exploratory analysis showed a larger relative contribution of velocity feedback during over ground walking as compared to treadmill walking. These differences warrant caution in generalizing foot placement findings during treadmill walking to overground walking and might be promising for the estimation of foot placement control in daily life gait.

physiology↗

bioRxiv2025

Foot placement coordination is impaired in people with Parkinson's Disease

BackgroundGait instability is a common and disabling symptom of Parkinsons disease (PD), contributing to frequent falls and reduced quality of life. While clinical balance tests and spatiotemporal gait measures can predict fall risk, they do not fully explain the underlying control mechanisms. In healthy individuals, foot placement is actively adjusted based on an internal estimate of the center of mass state to maintain gait stability, known as foot placement control. This estimation relies on the integration of multisensory information, which has been shown to be impaired in PD, potentially disrupting the control of gait stability through foot placement. ObjectiveTo investigate whether foot placement coordination during overground walking is impaired in people with PD. MethodsFifty people with PD and 51 healthy older adults walked overground for ten minutes at self-selected, comfortable walking speed. Foot placement errors were quantified as the deviation between the actual foot placement and the predicted placement derived from the center of mass kinematic state during the preceding swing phase. ResultsFoot placement errors were significantly higher in people with PD than in healthy older adults in both mediolateral (p < .05) and anteroposterior directions (p < .0001), at both mid-swing and terminal swing. Relative explained variance in mediolateral direction was significantly higher in people with PD compared to healthy older adults (p < .005). ConclusionWe provide first evidence of impaired coordination between the center of mass and foot placement in PD. Future work should investigate a causal relationship between impaired foot placement control, sensorimotor integration and gait instability.

pathology↗

bioRxiv2022

A novel performance scoring quantification framework for stress test set-ups

Stress tests, e.g., the cardiac stress test, are standard clinical screening tools aimed to unmask clinical pathology. As such stress tests indirectly measure physiological reserves. The term reserve has been developed to account for the dis-junction, often observed, between pathology and clinical manifestation. It describes a physiological capacity that is utilized in demanding situations. However, developing a new and reliable stress test based screening tool is complex, prolonged, and relies extensively on domain knowledge. We propose a novel model-free machine-learning framework, the Stress Test Performance Scoring (STEPS) framework, to model expected performance in a stress test. A performance scoring function is trained with measures taken during the performance in a given task while exploiting information regarding the stress test set-up and subjects medical state. Multiple ways of aggregating performance scores at different stress levels are suggested and are examined with an extensive simulation study. When applied to a real-world data example, an AUC of 84.35[95%CI : 70.68 -95.13] was obtained for the STEPS framework to distinguish subjects with neurodegeneration from controls. In summary, STEPS improved screening by exploiting existing domain knowledge and state-of-the-art clinical measures. The STEPS framework can ease and speed up the production of new stress tests.

bioengineering↗