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The aim of this study is to propose and validate an inertial sensors-based methodology for the para-rowing stroke cycles segmentation. One non-disabled athlete performed two para-rowing set-ups, simulating PR1 (arms and shoulders-AS) and PR2 (trunk and arms-TA) conditions. Catch and finish events of each stroke cycle were identified on the signals measured by three sensors located on the right forearm (FA), upper arm (UA), and on the trunk (T). Accuracy was quantified by identifying the same events on the 3D trajectory of one right hand-located marker. UA and FA sensors data lead to a more accurate detection of stroke events with respect to the T sensor (average error: 28.8ms, 29.0ms, 56.9ms). The present results open promising scenarios on the application of inertial sensors in para-rowing for real-time performance-related feedback to athletes and coaches.