The AktiMuW project aims to enhance mobility assistance for elderly individuals by developing a smart rollator equipped with advanced posture monitoring. A crucial aspect of this system is the detection and correction of the user's posture of legs. This is based on measuring the distance between the user and the rollator, among other methods. The study evaluates three different distance sensors — HC-SR04, HC-SR04-P, and TFmini-S to determine the most reliable and suitable option. To achieve this, a series of use case centered experiments were conducted, where each sensor's performance was tested. The HC-SR04 demonstrated relatively low measurement error, with Root Mean Square Error (RMSE) values ranging from 0.64 cm to 0.89 cm but required a 5V power supply and additional voltage conversion components, complicating integration to single board computers (SBC). The HC-SR04-P, an updated model, operates reliably at 3.3V — compatible with Raspberry Pi boards — and maintained comparable measurement precision, with RMSE values of 0.57 cm and 0.78 cm. In contrast, the TFmini-S LiDAR sensor exhibited higher RMSE values of 5.42 cm and 2.89 cm, particularly struggling at shorter distances, making it unsuitable for this application. Further gait analysis tests confirmed that the HC-SR04-P could effectively monitor the user's position, despite occasional signal reflections. The study concludes that the HC-SR04-P is the optimal choice for the rollator Machine Learning algorithms due to its balance of accuracy, compatibility, and cost-effectiveness. These findings contribute to the theoretical understanding of sensor-based posture monitoring and hold practical significance for the development of assistive mobility devices and further algorithms.