General movements (GMs) are the spontaneous gross motor movements involving the whole body. GMs progressively develop as an infant ages. Several recent research studies involving the qualitative assessment of the GMs in infants have validated that GMs, or the lack of, are an accurate way diagnosing a neurological dysfunction in the early stages of infancy. One study has shown that definitely abnormal movements occurring between 10-20 weeks post-term accurately predicted cerebral palsy in infants with an accuracy of 85 to 98 percent [1].

The qualitative method of assessing an infant's GMs is an accurate way of predicting a neurological dysfunction, however, requires the review of hours of video footage by a trained physician. This process is not only time consuming and costly but is subjective in the sense that the results cannot be easily transferred among different institutions. It is also difficult to conduct longitudinal studies without first reviewing the entire history of video footage of the infant's GMs. Improvements can be made to the qualitative GMs assessment method by utilizing recent advances in technology that "can make data collection and analysis more efficient, without compromising competency" [2]. In particular, preliminary research has shown that data collected from"wired" micro-electrical-mechanical systems (MEMS) accelerometers attached to the wrist and ankles of an infant is a feasible way of collecting and characterizing the motion patterns that infants display during GMs [3].

The work presented in this thesis is directed towards improving the past research that used "wired" accelerometers to acquire acceleration signals from the limbs of infants. This thesis describes the process of transitioning the "wired" accelerometers to the wireless level, designing a user-friendly interface to graphically interpret the acceleration data, and assessing the designed system through clinical trials on normal and at-risk infants using the design system.