Reservoir Computing (RC) offers an energy-efficient framework for processing temporal data. This paper presents a two-stage reservoir computing architecture for efficient and accurate classification of temporal sensory data, particularly vibrational signals captured from the Hall sensors. The proposed architecture leverages a physical reservoir consisting of a mechanical system to perform intrinsic dynamic encoding of input signals, followed by a software-based Echo State Network (ESN) that acts as a second-stage reservoir for nonlinear classification. Experiments conducted in two distinct environments - a terrain surface dataset and a machine shop dataset- demonstrate the effectiveness of this cascaded design. Our proposed method achieves a reduction in memory overhead, lower computational complexity, and maintains a competitive accuracy. Principal Component Analysis and confusion matrix evaluations confirm enhanced separability and improved class-specific feature alignment provided by the second-stage ESN, validating its utility for real-time classification tasks such as resource-constrained edge, embedded devices, etc. In contrast to deep learning baselines such as LSTM and 1D-CNN, our cascaded ESN achieves comparable accuracy with two orders of magnitude fewer trainable parameters and drastically reduced runtime. This compactness and efficiency make the proposed architecture particularly well-suited for deployment in low-power edge and embedded systems, where conventional deep learning models are impractical.