Fig 1 Accuracy histogram for 9 subjects. 24Ghz radar data with
sampling rate of 1000Hz.
The results of the first experiment with a high sampling rate in Table 1
show that the PAMDF is more accurate than the traditional FFT and AMDF
methods. It yielded almost the same total accuracy as the ML method,
with a difference of less than 0.5%. To our knowledge, there is
currently no method that can provide more accurate results.
Specifically, Figure 2 shows a histogram of the accuracy of the four
methods in the first experiment for data from nine subjects with
different quality. The PAMDF always performs better than the AMDF, with
either large or minor advantages. On the other hand, it has almost
identical accuracy to the ML for all subjects, which demonstrates that
the PAMDF is a close approximation of ML estimation.
In the second experiment with a lower sampling rate, the PAMDF method
maintained similar accuracy with the help of rounding interpolation, and
outperformed other methods. In terms of complexity, the interpolated
PAMDF only slightly increased compared to AMDF. For each window with 200
complex samples in the second experiment, the PAMDF is calculated by
adding only ~ 1200 real additions and ~
110 real multiplications to the AMDF, which needs ~
40,000 real additions but no multiplications. Comparatively, the ML
estimation may also gain performance improvements with additional signal
interpolation. However, applying the simplest signal rounding in would
require ~ 50,000 real additions and ~
4,000 real multiplications, and may only achieve performance similar to
the PAMDF.
Conclusion: A novel PAMDF period estimation method is proposed
for remote heart rate monitoring. The algorithm is implemented by
advancing the classical AMDF and has low complexity. Its estimation
accuracy is almost the same as the maximum likelihood estimation, which
is the best of the known methods. Future studies may investigate the
potential application of the PAMDF in other scenarios.
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