Zhilin just sent me the following:
Hi Igor,
I hope all is well with you!
In recent years, I have been working on signal processing for wearable health monitoring, such as signal processing of vital signs in smart watch and other wearables. Particularly, I've applied compressed sensing to this area, and achieved some successes on heart rate monitoring for fitness tracking and health monitoring. So I think you and your blog's readers may be interested in the following work of my collaborators and me:
Zhilin Zhang, Zhouyue Pi, Benyuan Liu, TROIKA: A General Framework for Heart Rate Monitoring Using Wrist-Type Photoplethysmographic Signals During Intensive Physical Exercise, IEEE Trans. on Biomedical Engineering, vol. 62, no. 2, pp. 522-531, February 2015
(preprint: http://arxiv.org/abs/1409.5181)
Thanks Zhilin ! and yes, I am glad to cover work on how compressive sensing and related techniques can make sense of IoT type of sensors (and work that includes datasets!). Without further ado:
Zhilin Zhang, Photoplethysmography-Based Heart Rate Monitoring in Physical Activities via Joint Sparse Spectrum Reconstruction, IEEE Transactions on Biomedical Engineering, vol. 62, no. 8, pp. 1902-1910, August 2015
(preprint: http://arxiv.org/abs/1503.00688)
In fact, I think the problem of Photoplethysmography-based heart rate monitoring can be well formulated into various kinds of compressed sensing models, such as multiple measurement vector (MMV) model (as shown in my second paper), gridless compressed sensing model (also mentioned in my second paper), and time-varying sparsity model. Since the data are available online (the download link was given in my papers), I hope these data can encourage compressed sensing researchers to join this area, revealing potential values of compressed sensing in these real-life problems.
I will very appreciate if you can introduce my work on your blog.
Thank you!
Best regards,
Zhilin
TROIKA: A General Framework for Heart Rate Monitoring Using Wrist-Type Photoplethysmographic Signals During Intensive Physical Exercise by Zhilin Zhang, Zhouyue Pi, Benyuan Liu
Heart rate monitoring using wrist-type photoplethysmographic (PPG) signals during subjects' intensive exercise is a difficult problem, since the signals are contaminated by extremely strong motion artifacts caused by subjects' hand movements. So far few works have studied this problem. In this work, a general framework, termed TROIKA, is proposed, which consists of signal decomposiTion for denoising, sparse signal RecOnstructIon for high-resolution spectrum estimation, and spectral peaK trAcking with verification. The TROIKA framework has high estimation accuracy and is robust to strong motion artifacts. Many variants can be straightforwardly derived from this framework. Experimental results on datasets recorded from 12 subjects during fast running at the peak speed of 15 km/hour showed that the average absolute error of heart rate estimation was 2.34 beat per minute (BPM), and the Pearson correlation between the estimates and the ground-truth of heart rate was 0.992. This framework is of great values to wearable devices such as smart-watches which use PPG signals to monitor heart rate for fitness.
Photoplethysmography-Based Heart Rate Monitoring in Physical Activities via Joint Sparse Spectrum Reconstruction by Zhilin Zhang
Goal: A new method for heart rate monitoring using photoplethysmography (PPG) during physical activities is proposed. Methods: It jointly estimates spectra of PPG signals and simultaneous acceleration signals, utilizing the multiple measurement vector model in sparse signal recovery. Due to a common sparsity constraint on spectral coefficients, the method can easily identify and remove spectral peaks of motion artifact (MA) in PPG spectra. Thus, it does not need any extra signal processing modular to remove MA as in some other algorithms. Furthermore, seeking spectral peaks associated with heart rate is simplified. Results: Experimental results on 12 PPG datasets sampled at 25 Hz and recorded during subjects' fast running showed that it had high performance. The average absolute estimation error was 1.28 beat per minute and the standard deviation was 2.61 beat per minute. Conclusion and Significance: These results show that the method has great potential to be used for PPG-based heart rate monitoring in wearable devices for fitness tracking and health monitoring.
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