Key result
Novel 1-D and 2-D S-EMG compression algorithms outperform existing encoders on objective metrics.
Why the study?
Efficient compression algorithms for one-dimensional and two-dimensional S-EMG signals are needed to improve data encoding and reduce redundancy.
The proposed 1-D and 2-D S-EMG signal compression algorithms demonstrate superior performance compared to existing efficient encoders.
May aid efficient sEMG data handling in research; leaves open clinical validation and adoption.
This paper presents algorithms designed for one-dimensional (1-D) and 2-D surface electromyographic (S-EMG) signal compression. The 1-D approach is a wavelet transform based encoder applied to isometric and dynamic S-EMG signals. An adaptive estimation of the spectral shape is used to carry out dynamic bit allocation for vector quantization of transformed coefficients. Thus, an entropy coding is applied to minimize redundancy in quantized coefficient vector and to pack the data. In the 2-D approach algorithm, the isometric or dynamic S-EMG signal is properly segmented and arranged to build a 2-D representation. The high efficient video codec is used to encode the signal, using 16-bit-depth precision, all possible coding/prediction unit sizes, and all intra-coding modes. The encoders are evaluated with objective metrics, and a real signal data bank is used. Furthermore, performance comparisons are also shown in this paper, where the proposed methods have outperformed other efficient encoders reported in the literature.
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Trabuco et al. (2017) studied this question. 1-D and 2-D S-EMG signal compression algorithms vs. Other efficient encoders reported in the literature was evaluated on Objective metrics of signal compression. The proposed 1-D and 2-D surface electromyographic signal compression algorithms outperformed other efficient encoders reported in the literature based on objective metrics.
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