Deep Learning-Based Compressed Sensing for Mobile Device-Derived Sensor Data
Pages 5023 - 5030
Abstract
As the capabilities of smart sensing and mobile technologies continue to evolve and expand, storing diverse sensor data on smartphones and cloud servers becomes increasingly challenging. Effective data compression is crucial to alleviate these storage pressures. Compressed sensing (CS) offers a promising approach, but traditional CS methods often struggle with the unique characteristics of sensor data-like variability, dynamic changes, and different sampling rates-leading to slow processing and poor reconstruction quality. To address these issues, we developed Mob-ISTA-1DNet, an innovative CS framework that integrates deep learning with the iterative shrinkage-thresholding algorithm (ISTA) to adaptively compress and reconstruct smartphone sensor data. This framework is designed to manage the complexities of smartphone sensor data, ensuring high-quality reconstruction across diverse conditions. We developed a mobile application to collect data from 30 volunteers over one month, including accelerometer, gyroscope, barometer, and other sensor measurements. Comparative analysis reveals that Mob-ISTA-1DNet not only enhances reconstruction accuracy but also significantly reduces processing time, consistently outperforming other methods in various scenarios.
References
[1]
Yuequan Bao, James L Beck, and Hui Li. 2011. Compressive sampling for accelerometer signals in structural health monitoring. Structural Health Monitoring, Vol. 10, 3 (2011), 235--246.
[2]
Amir Beck and Marc Teboulle. 2009. A fast iterative shrinkage-thresholding algorithm for linear inverse problems. SIAM journal on imaging sciences, Vol. 2, 1 (2009), 183--202.
[3]
Jeffrey D Blanchard and Jared Tanner. 2015. Performance comparisons of greedy algorithms in compressed sensing. Numerical Linear Algebra with Applications, Vol. 22, 2 (2015), 254--282.
[4]
Mark Borgerding, Philip Schniter, and Sundeep Rangan. 2017. AMP-inspired deep networks for sparse linear inverse problems. IEEE Transactions on Signal Processing, Vol. 65, 16 (2017), 4293--4308.
[5]
Emmanuel J Candès et al. 2006. Compressive sampling. In Proceedings of the international congress of mathematicians, Vol. 3. Madrid, Spain, 1433--1452.
[6]
Si-Xin Chen, Yi-Qing Ni, and Lu Zhou. 2022. A deep learning framework for adaptive compressive sensing of high-speed train vibration responses. Structural Control and Health Monitoring, Vol. 29, 8 (2022), e2979.
[7]
Yunjin Chen, Wei Yu, and Thomas Pock. 2015. On learning optimized reaction diffusion processes for effective image restoration. In Proceedings of the IEEE conference on computer vision and pattern recognition. 5261--5269.
[8]
Wei Dai and Olgica Milenkovic. 2009. Subspace pursuit for compressive sensing signal reconstruction. IEEE transactions on Information Theory, Vol. 55, 5 (2009), 2230--2249.
[9]
Michael B Del Rosario, Stephen J Redmond, and Nigel H Lovell. 2015. Tracking the evolution of smartphone sensing for monitoring human movement. Sensors, Vol. 15, 8 (2015), 18901--18933.
[10]
David L Donoho. 2006. Compressed sensing. IEEE Transactions on information theory, Vol. 52, 4 (2006), 1289--1306.
[11]
Denzil Ferreira, Vassilis Kostakos, and Anind K. Dey. 2015. AWARE: Mobile Context Instrumentation Framework. Frontiers in ICT, Vol. 2 (2015). https://doi.org/10.3389/fict.2015.00006
[12]
Karol Gregor and Yann LeCun. 2010. Learning fast approximations of sparse coding. In Proceedings of the 27th international conference on international conference on machine learning. 399--406.
[13]
Tao Han, Kuangrong Hao, Yongsheng Ding, and Xuesong Tang. 2017. A new multilayer LSTM method of reconstruction for compressed sensing in acquiring human pressure data. In 2017 11th Asian control conference (ASCC). IEEE, 2001--2006.
[14]
Nguyen Quoc Viet Hung, Hoyoung Jeung, and Karl Aberer. 2012. An evaluation of model-based approaches to sensor data compression. IEEE Transactions on Knowledge and Data Engineering, Vol. 25, 11 (2012), 2434--2447.
[15]
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton. 2012. Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems, Vol. 25 (2012).
[16]
Chengbo Li, Wotao Yin, Hong Jiang, and Yin Zhang. 2013. An efficient augmented Lagrangian method with applications to total variation minimization. Computational Optimization and Applications, Vol. 56 (2013), 507--530.
[17]
Shancang Li, Li Da Xu, and Xinheng Wang. 2012. Compressed sensing signal and data acquisition in wireless sensor networks and internet of things. IEEE transactions on industrial informatics, Vol. 9, 4 (2012), 2177--2186.
[18]
Christopher A Metzler, Arian Maleki, and Richard G Baraniuk. 2016. From denoising to compressed sensing. IEEE Transactions on Information Theory, Vol. 62, 9 (2016), 5117--5144.
[19]
Rajarshi Middya, Nabajit Chakravarty, and Mrinal Kanti Naskar. 2017. Compressive sensing in wireless sensor networks--a survey. IETE technical review, Vol. 34, 6 (2017), 642--654.
[20]
Ali Mousavi, Ankit B Patel, and Richard G Baraniuk. 2015. A deep learning approach to structured signal recovery. In 2015 53rd annual allerton conference on communication, control, and computing (Allerton). IEEE, 1336--1343.
[21]
Sungkwang Mun and James E Fowler. 2009. Block compressed sensing of images using directional transforms. In 2009 16th IEEE international conference on image processing (ICIP). IEEE, 3021--3024.
[22]
FuTao Ni, Jian Zhang, and Mohammad N Noori. 2020. Deep learning for data anomaly detection and data compression of a long-span suspension bridge. Computer-Aided Civil and Infrastructure Engineering, Vol. 35, 7 (2020), 685--700.
[23]
Yuuki Nishiyama, Denzil Ferreira, Yusaku Eigen, Wataru Sasaki, Tadashi Okoshi, Jin Nakazawa, Anind K Dey, and Kaoru Sezaki. 2020. iOS Crowd-Sensing Won't Hurt a Bit!: AWARE Framework and Sustainable Study Guideline for iOS Platform. In Distributed, Ambient and Pervasive Interactions (2020-07--10), Norbert Streitz and Shiníchi Konomi (Eds.). Springer International Publishing, Cham, 223--243. https://doi.org/10.1007/978--3-030--50344--4_17
[24]
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al. 2019. Pytorch: An imperative style, high-performance deep learning library. Advances in neural information processing systems, Vol. 32 (2019).
[25]
Meenu Rani, Sanjay B Dhok, and Raghavendra B Deshmukh. 2018. A systematic review of compressive sensing: Concepts, implementations and applications. IEEE access, Vol. 6 (2018), 4875--4894.
[26]
JH Rick Chang, Chun-Liang Li, Barnabas Poczos, BVK Vijaya Kumar, and Aswin C Sankaranarayanan. 2017. One network to solve them all--solving linear inverse problems using deep projection models. In Proceedings of the IEEE International Conference on Computer Vision. 5888--5897.
[27]
Charissa Ann Ronao and Sung-Bae Cho. 2016. Human activity recognition with smartphone sensors using deep learning neural networks. Expert systems with applications, Vol. 59 (2016), 235--244.
[28]
Uwe Schmidt and Stefan Roth. 2014. Shrinkage fields for effective image restoration. In Proceedings of the IEEE conference on computer vision and pattern recognition. 2774--2781.
[29]
Gang Tang, Wei Hou, Huaqing Wang, Ganggang Luo, and Jianwei Ma. 2015. Compressive sensing of roller bearing faults via harmonic detection from under-sampled vibration signals. Sensors, Vol. 15, 10 (2015), 25648--25662.
[30]
Gang Tang, Qin Yang, Hua-Qing Wang, Gang-gang Luo, and Jian-wei Ma. 2015. Sparse classification of rotating machinery faults based on compressive sensing strategy. Mechatronics, Vol. 31 (2015), 60--67.
[31]
Michael E Tipping. 2001. Sparse Bayesian learning and the relevance vector machine. Journal of machine learning research, Vol. 1, Jun (2001), 211--244.
[32]
Joel A Tropp and Anna C Gilbert. 2007. Signal recovery from random measurements via orthogonal matching pursuit. IEEE Transactions on information theory, Vol. 53, 12 (2007), 4655--4666.
[33]
Qiang Wang, Chen Meng, Weining Ma, Cheng Wang, and Lei Yu. 2019. Compressive sensing reconstruction for vibration signals based on the improved fast iterative shrinkage-thresholding algorithm. Measurement, Vol. 142 (2019), 68--78.
[34]
Jian Zhang and Bernard Ghanem. 2018. ISTA-Net: Interpretable optimization-inspired deep network for image compressive sensing. In Proceedings of the IEEE conference on computer vision and pattern recognition. 1828--1837.
[35]
Kai Zhang, Wangmeng Zuo, Shuhang Gu, and Lei Zhang. 2017. Learning deep CNN denoiser prior for image restoration. In Proceedings of the IEEE conference on computer vision and pattern recognition. 3929--3938.
Index Terms
- Deep Learning-Based Compressed Sensing for Mobile Device-Derived Sensor Data
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Published: 21 October 2024
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