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Human Data Analysis Papers

  1. Matteo Gadaleta, Enrico Grisan, Andrea Facchinetti, Michele Rossi, Prediction of Adverse Glycemic Events from Continuous Glucose Monitoring Signal, IEEE Journal of Biomedical and Health Informatics, Accepted for publication, 2018.

  2. Matteo Gadaleta, Michele Rossi, IDNet: Smartphone-based Gait Recognition with Convolutional Neural Networks. Elsevier Pattern Recognition, Volume 74, Pages 25–37, February 2018. [arXiv:1606.03238v2]
    Here, we present IDNet, a user authentication framework from smartphone-acquired motion signals. Its goal is to recognize a target user from their way of walking, using the accelerometer and gyroscope (inertial) signals provided by a commercial smartphone worn in the front pocket of the user’s trousers. IDNet features several innovations including: i) a robust and smartphone-orientation-independent walking cycle extrac- tion block, ii) a novel feature extractor based on convolutional neural networks, iii) a one-class support vector machine to classify walking cycles, and the coherent integra- tion of these into iv) a multi-stage authentication technique. IDNet is the first system that exploits a deep learning approach as universal feature extractors for gait recog- nition, and that combines classification results from subsequent walking cycles into a multi-stage decision making framework. Experimental results show the superiority of our approach against state-of-the-art techniques, leading to misclassification rates (either false negatives or positives) smaller than 0.15% with fewer than five walking cycles. Design choices are discussed and motivated throughout, assessing their impact on the user authentication performance.
  3. Mohsen Hooshmand, Davide Zordan, Davide Del Testa, Enrico Grisan, Michele Rossi, Boosting the Battery Life of Wearables for Health Monitoring through the Compression of Biosignals. IEEE IoT Journal, Vol. 4, No. 5, pp. 1647-1662, October 2017.
    Modern wearable IoT devices enable the monitoring of vital parameters such as heart or respiratory rates (RESP), electrocardiography (ECG), photo-plethysmographic (PPG) signals within e-health applications. A common issue of wearable technology is that signal transmission is power-demanding and, as such, devices require frequent battery charges and this poses serious limitations to the continuous monitoring of vitals. To ameliorate this, we advocate the use of lossy signal compression as a means to decrease the data size of the gathered biosignals and, in turn, boost the battery life of wearables and allow for fine-grained and long-term monitoring. Considering one dimensional biosignals such as ECG, RESP and PPG, which are often available from commercial wearable IoT devices, we provide a thorough review of existing biosignal compression algorithms. Besides, we present novel approaches based on online dictionaries, elucidating their operating principles and providing a quantitative assessment of compression, reconstruction and energy consumption performance of all schemes. As we quantify, the most efficient schemes allow reductions in the signal size of up to 100 times, which entail similar reductions in the energy demand, by still keeping the reconstruction error within 4% of the peak-to-peak signal amplitude. Finally, avenues for future research are discussed.
  4. Mohsen Hooshmand, Davide Zordan, Tommaso Melodia, Michele Rossi, SURF: subject-adaptive unsupervised ECG signal compression for wearable fitness monitors. IEEE Access, Vol. 5, pp. 19517 - 19535, 7 September 2017.
    Recent advances in wearable devices allow non-invasive and inexpensive collection of biomedical signals including electrocardiogram (ECG), blood pressure, respiration, among others. Collection and processing of various biomarkers is expected to facilitate preventive healthcare through personalized medical applications. Since wearables are based on size- and resource-constrained hardware, and are battery operated, they need to run lightweight algorithms to efficiently manage energy and memory. To accomplish this goal, this article proposes SURF, a subject-adpative unsupervised signal compressor for wearable fitness monitors. The core idea is to perform a specialized lossy compression algorithm on the ECG signal at the source (wearable device), to decrease the energy consumption required for wireless transmission and thus prolong the battery lifetime. SURF leverages unsupervised learning techniques to build and maintain, at runtime, a {\it subject-adaptive} dictionary without requiring any prior information on the signal. Dictionaries are constructed within a suitable feature space, allowing the addition and removal of codewords according to the signal's dynamics (for given target fidelity and energy consumption objectives). Extensive performance evaluation results, obtained with reference ECG traces and with our own measurements from a commercial wearable wireless monitor, show the superiority of SURF against state-of-the-art techniques, including (i) compression ratios up to 90-times, (ii) reconstruction errors (RMSE) between 2% and 7% of the signal's range (depending on the amount of compression sought), and (iii) reduction in energy consumption of up to two-orders of magnitude with respect to sending the signal uncompressed, while preserving its morphology. SURF, with artifact prone ECG signals, allows for typical compression efficiencies (CE) in the range [40,50], which means that the data rate of 3 kbit/s that would be required to send the uncompressed ECG trace is lowered to 60~bit/s and 75~bit/s for CE=40 and CE=50, respectively.
  5. Valentina Vadori, Enrico Grisan, Michele Rossi, Biomedical Signal Compression with Time- and Subject-adaptive Dictionary for Wearable Devices. IEEE International Workshop on Machine Learning for Signal Processing (MLSP), September 13-16, Vietri sul Mare, Salerno, Italy, 2016.

  6. Matteo Gadaleta, Luca Merelli, Michele Rossi, Human Authentication From Ankle Motion Data Using Convolutional Neural Networks. IEEE Statistical Signal Processing Workshop (SSP), June 26-29, Palma de Mallorca, Spain, 2016.

  7. Davide Del Testa, Michele Rossi, Lightweight Lossy Compression of Biometric Patterns via Denoising Autoencoders. IEEE Signal Processing Letters, Vol. 22, No. 12, September 2015.
    Wearable Internet of Things (IoT) devices permit the massive collection of biosignals (e.g., heart-rate, oxygen level, respiration, blood pressure, photo-plethysmographic signal, etc.) at low cost. These, can be used to help address the individual fitness needs of the users and could be exploited within personalized healthcare plans. In this letter, we are concerned with the design of lightweight and efficient algorithms for the lossy compression of these signals. In fact, we underline that compression is a key functionality to improve the lifetime of IoT devices, which are often energy constrained, allowing the optimization of their internal memory space and the efficient transmission of data over their wireless interface. To this end, we advocate the use of autoencoders as an efficient and computationally lightweight means to compress biometric signals. While the presented techniques can be used with any signal showing a certain degree of periodicity, in this letter we apply them to ECG traces, showing quantitative results in terms of compression ratio, reconstruction error and computational complexity. State of the art solutions are also compared with our approach.
  8. Enrico Grisan, Giorgia Cantisani, Giacomo Tarroni, Seung Keun Yoon, Michele Rossi, A supervised learning approach for robust detection of heart beat in plethysmographic data. IEEE Engineering in Medicine and Biology Society (EMBS), August 25-29, Milan, Italy, 2015.

  9. Roberto Francescon, Mohsen Hooshmand, Matteo Gadaleta, Enrico Grisan, Seung Keun Yoon, Michele Rossi, Toward Lightweight Biometric Signal Processing for Wearable Devices. IEEE Engineering in Medicine and Biology Society (EMBS), August 25-29, Milan, Italy, 2015.