Indoor human localization has gained significant importance because of its wide range of applications, including smart environments, health monitoring and security systems. However, still one of the main challenges is having an accurate indoor positioning system due to complex environments, signal interference, and the demand for real-time processing. While traditional approaches like Recurrent Neural Network (RNNs) are often used for time-series sensor data, they can suffer from high resource consumption and difficulty in modeling complex relational patterns. To address this challenges, this thesis presents a solution using Capsule Network (CapsNet) with capacitive sensors for indoor localization and the motivation of using CapsNets lies in their inherent ability to model hierarchical part-whole relationships which allows to better interpret complex and overlapping temporal patterns from sensors compared to conventional architectures so it helps to achieve high accuracy by preserving the rich capacity of Capsule Network comes at the cost of high computational and memory requirements, making them so difficult to deploy on real-time, resource-constrained edge devices like FPGAs.