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Study of Locomotor Expectations for Ascending/Descending Slope and Stairs in Patients With Limb Amputations

Study of Locomotor Expectations for Ascending/Descending Slope and Stairs in Patients With Limb Amputations

Status
Recruiting
Phases
Unknown
Study type
Observational
Source
ClinicalTrials.gov
Registry ID
NCT04030650
Acronym
JCE
Enrollment
70
Registered
2019-07-24
Start date
2019-09-20
Completion date
2028-05-01
Last updated
2026-03-20

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Lower Limb Amputation

Brief summary

Patients with lower limb amputations are equipped with prostheses that can be mechanical and/or electronic. These prostheses can be mono-articular (only the ankle) or bi-articular (knee and ankle for example). For amputee patients, situations that may seem trivial, such as climbing and descending stairs, become complex. Thus during the descent of stairs, an unamputated person will slow down the descent by contracting the thigh muscles, which are obviously lacking in the amputee patient. Current prostheses, known as "intelligent" (or "microprocessor") prostheses, make it possible to adjust the locomotion only once the first step has been taken and to assist the patient during ascent/descent situations on slopes and stairs. The next technological challenge in the development of lower limb equipment is to be able to anticipate these complex environmental situations, in order to secure and facilitate movement even before the obstacle is crossed or the terrain changed. This project plans to use the locomotor expectations commonly made during walking as a means of regulating the locomotor pattern. We believe that these expectations will depend on the situation, i.e. a particular anticipation when climbing or descending a slope, or when approaching a staircase, etc. To understand and describe these locomotor expectations, we plan to use recent techniques called supervised machine learning. These will make it possible to classify locomotor behaviour when walking on a slope or stairs. In the second phase, we would like to describe precisely the characteristics of the movements of the joints, and of the muscles during these adaptations. The final objective of this work is to create an autonomous sensor system to control the anticipatory behaviour of a lower limb prosthesis.

Interventions

OTHERFunctional analyses

2-minute walking test 200-metre walking test

OTHER3D analysis of walking and balance

Walking analysis Balance analysis Analysis of the strength of the flexor and extensor muscles of the trunk and lower limb

Sponsors

Centre Hospitalier Universitaire Dijon
Lead SponsorOTHER

Study design

Observational model
OTHER
Time perspective
PROSPECTIVE

Eligibility

Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
Yes

Inclusion criteria

Healthy volunteers: * person who has given oral consent * male or female adult * person being able to understand simple orders, locomotion instructions Lower limb amputee patients: * person who has given oral consent * male or female adult * patient with unilateral major lower limb amputation of any origin (traumatic, vascular, infectious, congenital or neoplastic) with definitive equipment used routinely for at least 3 months. * person able to understand simple orders, locomotion instructions

Exclusion criteria

* person not affiliated or not benefiting from a heath insurance system * person subject to a legal protection measure (curatorship, guardianship) * person with a legal guardian * pregnant or breastfeeding woman * adult unable to consent * person with a dislocated hip * subject with conditions or disabilities other than amputation that affect walking

Design outcomes

Primary

MeasureTime frameDescription
The error rate of the algorithm2 monthsThe error rate of the algorithm for predicting the situation encountered in the next step

Countries

France

Contacts

CONTACTPaul ORNETTI
paul.ornetti@chu-dijon.fr0380293745

Outcome results

None listed

Source: ClinicalTrials.gov · Data processed: Mar 21, 2026