Although tremor is the most common movement disorder, there are few non-invasive treatment options. One of the obstacles to creating effective tremor suppression devices is our lack of understanding regarding where tremor originates (which muscles), how it propagates through the limb (to which degrees of freedom, DOF), and where it manifests most severely (which DOF). To investigate these questions, we created a simple, linear time-invariant model to simulate tremor, with tremorogenic muscle activity input (in the 15 major superficial muscles from the shoulder to the wrist) and joint displacement output (in the 7 major upper limb DOF). The model included excitation-contraction dynamics, musculoskeletal geometry (muscle moment arms) and the mechanical impedance (inertia, damping, and stiffness) of the limb. From our simulation results, we determined four principles of tremor propagation. First, the distribution of tremor depends strongly on musculoskeletal dynamics. Second, the spreading of tremor is due to inertial coupling (primarily) and musculoskeletal geometry (secondarily). Third, tremor spreads narrowly in the sense that most of the tremor caused by a muscle occurs in a small number of DOF. Lastly, assuming uniform distribution of tremorogenic activity among upper-limb muscles, tremor increases proximal-distally, and the contribution from muscles increases proximal-distally.

This study seeks to understand upper limb tremor in subjects with essential tremor (ET). A thorough understanding of tremor distribution will allow for the more effective development of tremor suppression devices, which offer an alternative to current treatments. Previous studies primarily focused on tremor in the hand only. This study seeks to characterize the distribution of tremor throughout the upper limb.We measured tremor in 25 subjects diagnosed with ET using motion capture, which provided displacement information of the limb during multiple postural and kinetic tasks. Inverse kinematics allowed us to analyze the motion capture data in the 7 major degrees of freedom (DOF) of the upper limb. The power spectral density estimate was used to determine: relative tremor magnitude throughout the DOFs, tremor variation between tasks, variation between subjects, and frequency variations between DOFs. Data analysis revealed that tremor increase is roughly proximal to distal. We also show that tremor magnitude in kinetic tasks is significantly higher than in postural tasks. Although we found some variation in tremor distribution between subjects, the roughly proximal to distal increase in tremor severity holds for several subsets of the study population. Finally, we found that tremor frequency doesnt vary significantly (< 1 Hz) between DOFs, in subjects with severe tremor. Our study shows that tremor distribution is quite stereotyped between subjects with ET. Furthermore, we have shown that tremor is greatest in the distal DOFs. This provides a compelling starting point for the development of future tremor suppression devices.

This study seeks to understand upper limb tremor in subjects with essential tremor (ET). A thorough understanding of tremor distribution will allow for the more effective development of tremor suppression devices, which offer an alternative to current treatments. Previous studies primarily focused on tremor in the hand only. This study seeks to characterize the distribution of tremor throughout the upper limb. We measured tremor in 25 subjects diagnosed with ET using motion capture, which provided 0displacement information of the limb during multiple postural and kinetic tasks. Inverse kinematics allowed us to analyze the motion capture data in the 7 major degrees of freedom (DOF) of the upper limb. The power spectral density estimate was used to determine: relative tremor magnitude throughout the DOFs, tremor variation between tasks, variation between subjects, and frequency variations between DOFs. Data analysis revealed that tremor increase is roughly proximal to distal. We also show that tremor magnitude in kinetic tasks is significantly higher than in postural tasks. Although we found some variation in tremor distribution between subjects, the roughly proximal to distal increase in tremor severity holds for several subsets of the study population. Finally, we found that tremor frequency doesnt vary significantly (<<> 1 Hz) between DOFs, in subjects with severe tremor. Our study shows that tremor distribution is quite stereotyped between subjects with ET. Furthermore, we have shown that tremor is greatest in the distal DOFs. This provides a compelling starting point for the development of future tremor suppression devices.