Digital archive of theses discussed at the University of Pisa


Thesis etd-06232017-104614

Thesis type
Tesi di dottorato di ricerca
Thesis title
Musculoskeletal Analysis of the Lumbar Spine in daily activities
Academic discipline
Course of study
tutor Prof.ssa Di Puccio, Francesca
  • Low back pain
  • Level walking
  • Hip instrumented implant
  • Hill-type muscle model
  • Motion analysis
  • Musculoskeletal model
  • Spine
  • Vicon system
Graduation session start date
Release date
Low back pain is an economic, social and public heathy problem, affecting approximately 80% of all adults in developed countries at some point of their life. As suggested by some epidemiological studies, high spinal loads can be a leading cause of risk for low back pain.
Different experimental methods have been used to estimate in vivo spinal loads during daily activities (e.g. level walking, upper body flexion and weight lifting). However, invasiveness, complexity and limited number of available subjects have encouraged the development of computational models, based on assumptions and simplifications.
A musculoskeletal model was constructed from the existing GaitFullBody model from the AnyBody Managed Model Repository v. 1.6.2, provided by AnyBody Modeling System v. 6.0.4 (AMS) (AnyBody Technology A/S, Aalborg, Denmark). A previously established lumbar spine model was modified and the force – length – velocity relationships of the Hill-Type model were included. Muscle parameters were identified after an exhaustive literature review. Their values were scaled on the basis of the maximal isometric force for each muscle group. The inputs of the musculoskeletal model were markers trajectories and force data, captured by a 10 cameras capture system and two AMTI force plates, respectively. Twelve asymptomatic males together with five patients with an hip instrumented implant were measured during the most important daily activities with relatively high spinal loads. A customized marker set for the spine was used. In vivo joint reaction forces from instrumented implants were recorded to validate the musculoskeletal model, driven by the motion data of a given patient. Within this PhD thesis, the validation together with analyses of the predicted outcomes were carried out for level walking, during which the spine is exposed to a high number of cycles.