Modeling Foot Function in Barefoot and Shod Conditions. Jill Halstead

Transcription

1 Modeling Foot Function in Barefoot and Shod Conditions. Jill Halstead Research Co-ordinator & Arthritis Research UK Doctoral Student University of Leeds Institute for Molecular Medicine Leeds NIHR Musculoskeletal Biomedical Research Unit This speaker has no conflicts of interest

2 Modeling Foot Motion Foot pain and deformity often related to difficulties with movement. Affecting activities of daily living: Walking, Standing, Climbing Stairs Foot motion most commonly modeled during walking using rigid body assumptions of motion. The foot is split into multiple segments: Markers - commonly passive reflective Placed upon anatomical landmarks.

3 Multi-Segment Foot Models Various multi-segment kinematic foot models have been reported in the literature. (Deschamps et al. 2011) 15 multi-segment foot models in clinical and academic use. Divide the foot and tibia (including the fibula) into two to nine segments. Multiple Foot Segments Commonly Used: 1 Segment - Typical general gait model 2 Segments - Hindfoot, Forefoot. 3 Segments - Hindfoot, Midfoot, Forefoot. 4 Segments - Hindfoot, Midfoot, Medial Forefoot, Lateral Forefoot. 5+ Segments Variations of above plus Toes. Buczek et al. 2006

4 Common Multi-Segment Foot Models Milwaukee, CAST and Oxford foot models Kidder 1996, Leardini 1999, Carson /5 Segments - Tibia, Hindfoot, Midfoot, Forefoot, Hallux 4 markers for each segment - Plus static markers Milwakee = 9 CAST = 5 clusters Oxford = 11

5 Barefoot Foot Modeling Multi-segment foot studies are limited mainly to barefoot assessments. Shoes may confound reflective markers and segmental assumptions. Shoe mounted marker sets have been utilized.

6 Shod Foot Modeling Shod and Orthoses studies Most opted for sandals Unclear if sandals can maintain the position of orthoses Alternative options are cutting holes in trainers. Shod cluster marker studies, optimum hole size.

7 Shod Foot Modeling ibishop et al. i-fab abstract April 10 th 2012 Multi-segment foot kinematics compared in-shoe versus on-shoe marker sets. On shoe marker placement moderate to excellent. Intra-rater (ICC = ) Inter-rater reliability (ICC = Compared to skin-mounted markers. calibration marker placement on the shoe was < 5 mm

8 Shoe Development and Design AIM: Foot & Reflective Markers (+ Orthoses) Prototype 1 - Slipper Shoe Started total webbed upper Prototype 2 - Canvas Pump Modified canvas and rubber lace-up shoe Canvas panels customized with netting windows Markers protrude through netting Custom slits eliminate fouling of markers

9 Shoe Design Feasibility of gait shoe tested in two stages: 1. Comfort and comparability to a normal shoe. 2. Marker tracking during walking, with and without insoles in-situ. Design finalized replicas made in sizes UK 4-11.

10 Method Shoe Validation Participants 15 patients - mechanical midfoot pain. Participating in a larger orthoses intervention study 15 control volunteers with no foot pain. Data acquisition 3D, Infra-Red, 8 camera Vicon MX T40 System (sample rate 200 Hz) Vicon, Oxford UK Integrated force plate (sample rate 1000 Hz) Bertec Corporation, OH USA Motion files processed with: Nexus (version ) Vicon polygon with Oxford foot model (version ) OMG Plc and Vicon motion Systems Ltd., Oxford UK

11 Method - Clinical Protocol Two Conditions: Barefoot and Shod - Random order. Single study limb = Most painful foot (foot pain group) Dominant limb (normal volunteers) Marker sets Helen Hayes modified Plug in Gait (16 markers 14mm) for both limbs. Oxford foot model (14 markers 9 mm). Static frame reference position (FPI score = 0). Data collection undertaken by a single researcher (JH). 5 minute acclimatisation period wearing the shoes. 8 gait cycles at a self selected walking speed 5 m capture volume in a 12m gait laboratory

12 Method Data Processing Processed using Plug-in-Gait and Oxford Foot Models Data filtered Woltring fifth-order spline-interpolating function to fill trajectory gaps. Gait events - Heel strike to Toe off Identified by force plate and auto-correlated for sequential heel strike. Gait cycle normalized to 51 centiles. Consistency graphs plotted: Most representative single gait cycle chosen for each participant and condition.

13 Results Groups we matched for age, not body mass. Patients with foot pain were heavier Group Number Sex Limb Mean Age Mean BMI Norms F 14 Right Foot Pain F 8 Right Median Gait Speed Normal Group Foot Pain Group Barefoot 1.25 m/s Shod 1.28 m/s Barefoot 1.05 m/s Shod 1.08 m/s Walking shod caused slight increase in step length. Between group Differences 0.2 m/s Remain

14 Hindfoot Kinematics OFM Barefoot Difference 7.62 Sagittal Shod Difference 8.46 Barefoot Difference 1.27 Frontal Shod Difference % stance Normalised Gait Cycle Percentage 50% stance Normalised Gait Cycle Percentage

15 Forefoot Kinematics OFM Barefoot Difference Sagittal Shod Difference Barefoot Difference Frontal Shod Difference % stance Normalised Gait Cycle Percentage 50% stance Normalised Gait Cycle Percentage

16 Discussion Shod solutions for skin markers require testing for hole size in multiple shoe sizes to minimize marker drop-out. Using the novel gait shoe, subtle differences between symptomatic patients and control group remained for: Temporal and Spatial Parameters Knee and Rearfoot kinematics Forefoot kinematic measures continue to demonstrate high between-patient variability in both barefoot and more so in shod conditions. High variability of forefoot motions requires cautious interpretation, whether barefoot or shod.

17 Conclusion Our gait shoe offers novel and practical solution for passive infra-red marker systems. Measure rearfoot function in-shoe and importantly, response to in-shoe therapy.

18 Acknowledgements FASTER Team: Anthony Redmond Anne-Maree Keenan Philip Conaghan Dennis McGonagle LTHT Foot Health Department MSK Podiatry Team: Bob Longworth Lee Short Brian Welsh Carl Ferguson Thank You