There is emerging data that patterns of motor activity early in neonatal life can predict impairments in neuromotor development. However, current techniques to monitor infant movement mainly rely on observer scoring, a technique limited by skill, fatigue, and inter-rater reliability. Consequently, we tested the use of a lightweight, wireless, accelerometer system that measures movement and can be worn by premature babies without interfering with routine care. We hypothesized that this system would be useful in assessing motor activity, in identifying abnormal movement, and in reducing the amount of video that a clinician would need to review for abnormal movements. Ten preterm infants in the NICU were monitored for 1 h using both the accelerometer system and video. A physical therapist trained to recognize cramped-synchronized general movements scored all of the video data by labeling each abnormal movement observed. The parameters of three different computer models were then optimized based on correlating features computed from accelerometer data and the observer’s annotations. The annotations were compared to the model’s prediction on unseen data. The trained observer identified cramped-synchronized general movements in 6 of the 10 infants. The computer models attained between 70% and 90% accuracy when predicting the same observer label for each data point. Our study suggests that mini-accelerometers may prove useful as a clinical tool assessing patterns of movement in preterm infants.