Background: Kinematic gait assessment is essential to the gait rehabilitation program after stroke. Portable devices composed of inertial sensors are an alternative for this evaluation. However, knowledge regarding the psychometric properties of these devices is needed to understand their accuracy, especially in evaluation of individuals with movement disorders (e.g., people post stroke). This systematic review aims to analyze the psychometric properties of portable devices that use inertial sensors to assess kinematic gait parameters in people post stroke. We will also investigate which portable device assesses alterations in lower limb angular movements during gait. Methods: We will search for studies in English without publication date restriction, that evaluated psychometric properties of portable devices that use inertial sensors to assess kinematic gait parameters in people after stroke. Searches will be performed in the following electronic databases: Cochrane Central Registry of Controlled Trials (CENTRAL), Medline/PubMed, EMBASE Ovid, CINAHL EBSCO, PsycINFO Ovid, IEEE Xplore Digital Library (IEEE), and Physiotherapy Evidence Database (PEDro). Gray literature will also be searched, including published and unpublished studies (dissertations and theses). The Consensus-based Standards for the Selection of Health Measurement Instruments (COSMIN) risk of bias tool will be used to assess the quality of studies that analyzed reliability and measurement error of devices. Expected results: This will be the first review assessing the risk of bias in studies that analyzed psychometric properties of portable devices that use inertial sensors to assess kinematic gait parameters in people post stroke. Then, we hope to elucidate this topic and help the decision-making of clinicians regarding the feasibility of these devices. Finally, we also hope to provide an overview of the characteristics of portable devices that assessed changes in angular lower limb movements during gait in this population. Registration: The protocol was registered in Open Science Framework on May 11th 2023 (https://doi.org/10.17605/OSF.IO/7M6DA).