  Neuromuscular disorders, such as Spinal Muscular Atrophy (SMA) and Duchenne
Muscular Dystrophy (DMD), cause progressive muscular degeneration and loss of
motor function for 1 in 6,000 children. Traditional upper limb motor function
assessments do not quantitatively measure patient-performed motions, which
makes it difficult to track progress for incremental changes. Assessing motor
function in children with neuromuscular disorders is particularly challenging
because they can be nervous or excited during experiments, or simply be too
young to follow precise instructions. These challenges translate to confounding
factors such as performing different parts of the arm curl slower or faster
(phase variability) which affects the assessed motion quality. This paper uses
curve registration and shape analysis to temporally align trajectories while
simultaneously extracting a mean reference shape. Distances from this mean
shape are used to assess the quality of motion. The proposed metric is
invariant to confounding factors, such as phase variability, while suggesting
several clinically relevant insights. First, there are statistically
significant differences between functional scores for the control and patient
populations (p$=$0.0213$\le$0.05). Next, several patients in the patient cohort
are able to perform motion on par with the healthy cohort and vice versa. Our
metric, which is computed based on wearables, is related to the Brooke's score
((p$=$0.00063$\le$0.05)), as well as motor function assessments based on
dynamometry ((p$=$0.0006$\le$0.05)). These results show promise towards
ubiquitous motion quality assessment in daily life.
