- Slides: 16
Wearable EMG System with Signal Compression and Decompression Related reading: Effective Low-Power Wearable Wireless Surface EMG Sensor Design Based on Analog-Compressed Sensing, Balouchestani & Krishnan (2014). Sensors 14: 2430524328. W. Rose 201704011
Background “s. EMG signals exhibit good level of sparsity in the time and frequency domains. ” “Conventional data acquisition approaches rely on the Shannon sampling theorem, which says a signal must be sampled at least twice its bandwidth in order to be represented without error. ” Sparse signal = a signal with most values zero or lacking information
Drawbacks of conventional approach Generates huge intolerable number of samples for many applications with a large bandwidth. Even for low signal bandwidths, including some biomedical signals, this produces a large number of redundant digital samples.
Cure Use compressive sampling to reduce the number of acquired samples by utilizing sparsity. Specifically, use analog compressive sampling before the analog-to-digital conversion step
Balouchestani & Krishnan (2014) 1. No compression 2. Digitize, then compress (more common than #3) 3. Compress, then digitize
Question for the Authors Balouchestani & Krishnan (2014) say they apply compressive sampling to the analog signal before Ato-D conversion. (See bottom part of figure in previous slide. ) But they implement their compression algorithm in computer code (C, h. Spice, or Matlab). And their sample signals are from online databases. The fact that they implement in code (not with a circuit) and that their test data is already digitized means the ADC has already happened. How do they account for this discrepancy? How did they get it published?
BSBL Block-sparse Bayesian Learning
Balouchestani & Krishnan (2014) Transmitter and Receiver Design
Step 7 of Table 2 in Balouchestani & Krishnan (2014) says sparsity level is given by Sp = (N/N-K) which is an unclear or wrong choice of parentheses, and K is undefined, and it is inconsistent with later figures, which show that Sparsity is a percentage that is 100% or less – which will not be true if computed with the equation above.
Fig. 10 does not make sense. It indicates that the accuracy is highest when sampling rate is lowest, just 10% of the Nyquist rate.