Towards Infant Kick Quality Detection to Support Physical Therapy and Early Detection of Cerebral Palsy: A Pilot Study

The kicking patterns of infants can provide markers that may predict the trajectory of their future development. Atypical kicking patterns may predict the possibility of developmental disorders like Cerebral Palsy (CP). Early intervention and physical therapy that encourages the practice of proper kicking motions can help to improve the outcomes in these scenarios. The kicking motions of an infant are usually evaluated by a trained health professional and subsequent physical therapy is also conducted by a licensed professional. The automation of the evaluation of kicking motions and the administration of physical therapy is desirable for standardizing these processes. In this work, we attempt to develop a method to quantify metrics that can provide insight into the quality of baby kicking actions. We utilize a computer vision system to analyze infant kicking stimulated by parent-infant play and a robotic infant mobile. We utilize statistical techniques to estimate kick type (synchronous and non-synchronous), kick amplitude, kick frequency, and kick deviation. These parameters can prove helpful in determining an infant's kick quality and also measure improvements in physical therapy over time. In this paper, we detail the design of the system and discuss the statistical results.