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The objective of this study was the development of a remote monitoring system to monitor and detect simple motor seizures. Using accelerometer-based kinematic sensors, data were gathered from subjects undergoing medication titration at the Beth Israel Deaconess Medical Center. Over the course of the study, subjects repeatedly performed a predefined set of instrumental activities of daily living (iADLs). During the monitoring sessions, EEG and video data were also recorded and provided the gold standard for seizure detection. To distinguish seizure events from iADLs, we developed a template matching algorithm. Considering the unique signature of seizure events and the inherent temporal variability of seizure types across subjects, we incorporated a customized mass-spring template into the dynamic time warping algorithm. We then ported this algorithm onto a commercially available internet tablet and developed our body sensor network on the Mercury platform. We designed several policies on this platform to compare the tradeoffs between feature calculation, raw data transmission, and battery lifetime. From a dataset of 21 seizures, the sensitivity for our template matching algorithm was found to be 0.91 and specificity of 0.84. We achieved a battery lifetime of 10.5 h on the Mercury platform.