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Background: Using machine learning to combine wrist accelerometer (ACM) and electrodermal activity (EDA) has been shown effective to detect primarily and secondarily generalized tonic-clonic seizures, here termed as convulsive seizures (CS). A prospective study was conducted for the FDA clearance of an ACM and EDA-based CS-detection device based on a predefined machine learning algorithm. Here we present its performance on pediatric and adult patients in epilepsy monitoring units (EMUs). Methods: Patients diagnosed with epilepsy participated in a prospective multi-center clinical study. Three board-certified neurologists independently labeled CS from video-EEG. The Detection Algorithm was evaluated in terms of Sensitivity and false alarm rate per 24 h-worn (FAR) on all the data and on only periods of rest. Performance were analyzed also applying the Detection Algorithm offline, with a less sensitive but more specific parameters configuration ("Active mode"). Results: Data from 152 patients (429 days) were used for performance evaluation (85 pediatric aged 6-20 years, and 67 adult aged 21-63 years). Thirty-six patients (18 pediatric) experienced a total of 66 CS (35 pediatric). The Sensitivity (corrected for clustered data) was 0.92, with a 95% confidence interval (CI) of [0.85-1.00] for the pediatric population, not significantly different ( p > 0.05) from the adult population's Sensitivity (0.94, CI: [0.89-1.00]). The FAR on the pediatric population was 1.26 (CI: [0.87-1.73]), higher ( p < 0.001) than in the adult population (0.57, CI: [0.36-0.81]). Using the Active mode, the FAR decreased by 68% while reducing Sensitivity to 0.95 across the population. During rest periods, the FAR's were 0 for all patients, lower than during activity periods ( p < 0.001). Conclusions: Performance complies with FDA's requirements of a lower bound of CI for Sensitivity higher than 0.7 and of a FAR lower than 2, for both age groups. The pediatric FAR was higher than the adult FAR, likely due to higher pediatric activity. The high Sensitivity and precision (having no false alarms) during sleep might help mitigate SUDEP risk by summoning caregiver intervention. The Active mode may be advantageous for some patients, reducing the impact of the FAR on daily life. Future work will examine the performance and usability outside of EMUs., Competing Interests: FO, GR, and CC are shareholders of Empatica Inc., which manufactured two of the devices used in this work and developed the two new algorithms tested in this work. GR is also an employee of Empatica and RP is also a consultant and chairs the board of directors for Empatica. TL is part of pending patent applications to detect and predict seizures and to diagnose epilepsy with devices different from the ones used in this work and has received research support from Empatica to conduct this research. TL, WL, and AB have received sensors from Empatica to perform the reported research. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Onorati, Regalia, Caborni, LaFrance, Blum, Bidwell, De Liso, El Atrache, Loddenkemper, Mohammadpour-Touserkani, Sarkis, Friedman, Jeschke and Picard.)