This paper investigates the potential of detecting stroke gaits by Deep Neural Network (DNN) models based on data from different sensor systems. Stroke survivors usually experience partial disability and need long-term rehabilitation. Because correct evaluation of stroke gaits is crucial for clinics to apply suitable medical treatments and rehabilitation strategies, we propose a method to detect stroke gaits by generalized gait data that might be obtained from different measurement systems. In this paper, we measure the subjects’ gait data by two equipment: an IMU system and a motion capture system. Then, we apply the IMU data to develop DNN models, which achieve an accuracy of 99.04% in detecting the stroke gaits by k-fold validation. Last, we applied the data measured by the motion capture system to test the DNN models. The results showed that the model could successfully detect stroke gaits with an average accuracy of 98.68%. In addition, we also applied a public dataset, where the gait data was measured by different IMU systems, to evaluate the model performance. The results show that the DNN models achieve an average accuracy of 98.25% in distinguishing normal gaits. These results confirm the possibility of detecting abnormal gaits based on data measured at different locations by assorted sensor systems.