The unpredictable and random occurrence of seizures is of the most distressful issue affecting patients and their families. Unattended seizures can have serious consequences including injury or death. The objective of this study is to develop a small, portable, wearable device capable of detecting seizures and alerting patients and families on recognition of specific seizures' motor activity. Ictal data were prospectively obtained in consecutive patients admitted to two video-EEG units. This study included patients with a history of motor seizures, clonic or tonic, or tonic-clonic seizures or patients with complex partial seizures with frequent secondary generalization. A "Motion Sensor" unit mounted on a bracelet was attached to one wrist. The "Sensor" contains a three-axis accelerometer and a transmitter. The three-axis movements' data were transmitted to a portable computer. Algorithm specially developed for this purpose analyzed the recorded data. Seizures' alerts were compared with the video-EEG data. Ictal data were acquired in 15 of the 31 recruited patients. The algorithm correctly identified 20 of 22 (91%) captured seizures and generated an alarm within a median period of 17 seconds. All events lasting >30 seconds (i.e., 19 events) were identified. The system failed to identify 2 of 22 seizures (9%). There were eight false alarms during 1,692 hours of monitoring. Preliminary data suggest that this motion detection device/alarm system can identify most motor seizures with high sensitivity and with a low false alarm rate.