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

Lower Limb Amputation Clinical Trial

— JCE  

Official title:

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

Clinical Trial Details — Status: Recruiting

Administrative data

• NCT number: NCT04030650
• Other study ID #: CASILLAS 2018-1
• Status: Recruiting
• Start date: September 20, 2019
• Est. completion date: November 2024

Study information

• Verified date: February 2024
• Source: Centre Hospitalier Universitaire Dijon
• Contact: Paul ORNETTI
• Phone: 0380293745
• Email: paul.ornetti[@]chu-dijon.fr
• Is FDA regulated: No
• Study type: Observational

Clinical Trial 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.

Recruitment information / eligibility

• Status: Recruiting
• Enrollment: 70
• Est. completion date: November 2024
• Est. primary completion date: November 2024
• Accepts healthy volunteers: Accepts Healthy Volunteers
• Gender: All
• Age group: 18 Years and older

Eligibility

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

Related Conditions & MeSH terms

• Lower Limb Amputation

Intervention

Other:
• Functional analyses
2-minute walking test 200-metre walking test
• 3D 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
• Functional analyses
400-metre walking test 200-metre walking test

Locations

Country	Name	City	State
France	CHU Dijon Bourgogne	Dijon

Sponsors (1)

Lead Sponsor	Collaborator
Centre Hospitalier Universitaire Dijon

Country where clinical trial is conducted

France, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	The error rate of the algorithm	The error rate of the algorithm for predicting the situation encountered in the next step	2 months