Onderzoek naar ambulante meet techniek, en procedures mechanical loading of the shoulder joint
Conditions
Interventions
None listed
Sponsors
Revalidatiecentrum Het Roessingh
Eligibility
Age
18 Years to 99 Years
Inclusion criteria
Inclusion criteria: For part 1 and 2 of the study: Healthy male right handed over 50 years of age no history of shoulder complaints active in ADL ability to understand and follow instructions ability to complete measurement session;For part 3 of the study: Healthy male right handed over 50 years of age with a shoulder endoprotheses (right shoulder) at least 6 month after completion of the rehabilitation process (concerning implant) active in ADL ability to understand and follow instructions ability to complete measurement session
Exclusion criteria
Exclusion criteria: For part 3: Co-morbidity of disorders affecting use of the upper extremity, like rheuma.
Design outcomes
Primary
| Measure | Time frame |
|---|---|
| Part 1: Accuracy of the NN method compared to output of a musculoskeletal model, over subjects. Part 2: Feasibility of the longterm measurement of upper extremity kinematics using IMMS and surface EMG under daily conditions, the application of the NN method on large datasets, descriptives of a first impression of ambulatory obtained shoulder joint load profile of healthy subjects. Part 3: Feasibility of the NN-method on subjects with a shoulder endoprothesis under daily conditions, descriptives of the first impression of ambulatory obtained shoulder joint load profile of subjects with a shoulder endoprothesis. | — |
Secondary
| Measure | Time frame |
|---|---|
| Part 1: The minimization of activities needed to generate `sufficient` training data for the Neural Network. `Sufficient` means here without degrading the initial performance of the Neural Network performance. This minimized set of activities will be used in part 2 and 3 of the experiment to train the NN under daily conditions. | — |
Countries
Netherlands
Outcome results
None listed