A Gait Data Compression and Reconstruction Framework for Edge Device using Low-Dimensional Attention Model with Autoencoder

This paper presents a gait data compression and reconstruction framework based on a low-dimensional attention model with autoencoder. By reducing the size of the attention filter to match the maximum matrix rank, the dimensionality of the attention filter can be reduced to enhance the compression ratio. Extensive evaluations using MHEALTH dataset demonstrated that the proposed method can achieve compression ratio of 24 with low reconstruction error of Percent Root Mean Square Difference (PRD) of 0.0323, Correlation Coefficient (CC) of 0.9510, and Signal-to-Noise Ratio Loss (SNRL) of 1.51 dB. The proposed compression model is implemented in hardware using microcontroller. Fixed-point quantization and optimized Softmax layer representation are performed to reduce the hardware resources requirement.