Identification of Human Limb Viscoelasticity using Robotics Methods to Support the Diagnosis of Neuromuscular Diseases

In this paper we present an original method to estimate in vivo the joint dynamics of the human limbs. The method is based on a non-invasive and painless technology making use of an optical motion capture system and an associated skeletal model to record the human motion and compute its kinematics and its dynamics. The formalism that is used for the identification is commonly used in robotics. The passive limb joints properties are modeled by enhanced spring-damper systems. The inverse dynamics is sampled along a movement to give an over-determined system. The obtained system is solved by the linear least-squares method. To perform the estimation, we place emphasis on giving indicators and requirements to interpret the obtained results, and on using painless, passive constraint-free movements that are usually performed during the clinical diagnosis of neuromuscular diseases. Finally the method is experimentally applied to two healthy subjects and five patients of neuromuscular diseases in order to estimate the upper-limb viscoelastic properties. The obtained results are discussed.