Determining and detecting risk markers for mental illness remains a labor intensive process, requiring vast amounts of observations by clinical professionals. Motor stereotypies, which are defined as involuntary repetitive motor behaviors, invariant in form, that, to an observer, appear to serve no purpose, are a class of risk markers which are very amenable to video analysis. These behaviors are associated with mental illnesses such as Autism, Rett Syndrome, and other developmental disabilities. This paper investigates the application of innovative automated methods to recognize these subtle motor indicators. To train and test our methods, a dataset of actions resembling motor stereotypies was created by engaging the normally developing children at the University of Minnesota's Shirley G. Moore Laboratory School. Comparison to a publicly available dataset depicting a subset of behaviors is performed as well. This work demonstrates the applicability of various techniques in the behavioral science domain. The results show that these techniques can perform well on a difficult and challenging real-world scenario.