skip to main content
10.1145/2502524.2502536acmconferencesArticle/Chapter ViewAbstractPublication PagesConference Proceedingsacm-pubtype
research-article

Low power programmable architecture for periodic activity monitoring

Published: 08 April 2013 Publication History

Abstract

Body sensor networks (BSNs) are considered a great example for cyber-physical systems due to their close coupling with human body. Activity monitoring is one of the numerous applications of BSNs. Continuous and real-time monitoring of human activities has many applications in healthcare and wellness domains. BSNs utilizing light-weight wearable computers and equipped with inertial sensors are highly suitable for real-time activity monitoring. However, power requirement is a major obstacle for miniaturization of these wearable systems, due to the need for sizable batteries, and also limits the life time of the system. In this paper, we propose a low-power programmable signal processing architecture for dynamic and periodic activity monitoring applications which utilizes the properties of the physical world (i.e., human body movements) to reduce the power consumption of the system. The significant power reduction is achieved by performing signal processing in a tiered-fashion and removing the signals that are not of interest as early as possible. Our proposed architecture uses wavelet decomposition and is favorable for the discrimination of periodic activities. The experimental results show that our architecture achieves 75.7% power saving while maintaining 96.9% sensitivity in the detection of target actions, compared with the scenario where the signal processing is not performed in tiered-fashion. This creates opportunities to enable the next generation of self-powered wearable computers.

References

[1]
E. Alpaydin. Introduction to machine learning. The MIT Press, 2004.
[2]
L. Atallah, G. Jones, R. Ali, J. Leong, B. Lo, and G. Yang. Observing recovery from knee-replacement surgery by using wearable sensors. In Body Sensor Networks (BSN), 2011 International Conference on, pages 29--34. IEEE, 2011.
[3]
A. Avci, S. Bosch, M. Marin-Perianu, R. Marin-Perianu, and P. Havinga. Activity recognition using inertial sensing for healthcare, wellbeing and sports applications: A survey. ARCS 2010, 2010.
[4]
B. Calhoun, D. Daly, N. Verma, D. Finchelstein, D. Wentzloff, A. Wang, S. Cho, and A. Chandrakasan. Design considerations for ultra-low energy wireless microsensor nodes. IEEE Transactions on Computers, pages 727--740, 2005.
[5]
P. Cong, N. Chaimanonart, W. Ko, and D. Young. A wireless and batteryless 10-bit implantable blood pressure sensing microsystem with adaptive rf powering for real-time laboratory mice monitoring. Solid-State Circuits, IEEE Journal of, 44(12):3631--3644, 2009.
[6]
I. Daubechies and W. Sweldens. Factoring wavelet transforms into lifting steps. Journal of Fourier analysis and applications, 4(3):247--269, 1998.
[7]
V. Ekanayake, C. Kelly IV, and R. Manohar. Bitsnap: Dynamic significance compression for a low-energy sensor network asynchronous processor. In Asynchronous Circuits and Systems, 2005. ASYNC 2005. Proceedings. 11th IEEE International Symposium on, pages 144--154. IEEE, 2005.
[8]
J. Friedman. Regularized discriminant analysis. Journal of the American statistical association, 84(405):165--175, 1989.
[9]
H. Ghasemzadeh and R. Jafari. An ultra low power granular decision making using cross correlation: Minimizing signal segments for template matching. In Cyber-Physical Systems (ICCPS), ACM/IEEE International Conference on, 2011.
[10]
H. Ghasemzadeh and R. Jafari. Ultra low power granular decision making using cross correlation: Optimizing bit resolution for template matching. In Real-Time and Embedded Technology and Applications Symposium (RTAS), 17th IEEE, pages 137--146, 2011.
[11]
S. Hanson, M. Seok, Y. Lin, Z. Foo, D. Kim, Y. Lee, N. Liu, D. Sylvester, and D. Blaauw. A low-voltage processor for sensing applications with picowatt standby mode. Solid-State Circuits, IEEE Journal of, 44(4):1145--1155, 2009.
[12]
M. Hempstead, D. Brooks, and G. Wei. An accelerator-based wireless sensor network processor in 130 nm cmos. Emerging and Selected Topics in Circuits and Systems, IEEE Journal on, 1(2):193--202, 2011.
[13]
R. Jafari. Tiered low power wake-up modules for lightweight embedded systems. In Body Sensor Networks (BSN), International Conference on, 2011.
[14]
R. Jafari and R. Lotfian. A low power wake-up circuitry based on dynamic time warping for body sensor networks. In Body Sensor Networks (BSN), International Conference on, 2011.
[15]
C. Kelly IV, V. Ekanayake, and R. Manohar. Snap: A sensor-network asynchronous processor. In Asynchronous Circuits and Systems, 2003. Proceedings. Ninth International Symposium on, pages 24--33. IEEE, 2003.
[16]
N. Lovell, N. Wang, E. Ambikairajah, and B. Celler. Accelerometry based classification of walking patterns using time-frequency analysis. In Engineering in Medicine and Biology Society (EMBS), 29th Annual International Conference of the IEEE, pages 4899--4902. IEEE, 2007.
[17]
S. Mitchell, J. Collin, C. De Luca, A. Burrows, and L. Lipsitz. Open-loop and closed-loop postural control mechanisms in Parkinson's disease: increased mediolateral activity during quiet standing. Neuroscience Letters, 197(2):133--136, 1995.
[18]
N. Najafi and A. Ludomirsky. Initial animal studies of a wireless, batteryless, mems implant for cardiovascular applications. Biomedical microdevices, 6(1):61--65, 2004.
[19]
M. Nyan, F. Tay, K. Seah, and Y. Sitoh. Classification of gait patterns in the time-frequency domain. Journal of biomechanics, 39(14):2647--2656, 2006.
[20]
S. Preece, J. Goulermas, L. Kenney, and D. Howard. A comparison of feature extraction methods for the classification of dynamic activities from accelerometer data. Biomedical Engineering, IEEE Transactions on, 56(3):871--879, 2009.
[21]
N. Saito and R. Coifman. Local discriminant bases and their applications. Journal of Mathematical Imaging and Vision, 5(4):337--358, 1995.
[22]
S. Sasaki, T. Seki, and S. Sugiyama. Batteryless accelerometer using power feeding system of rfid. In SICE-ICASE, 2006. International Joint Conference, pages 3567--3570. IEEE, 2006.
[23]
M. Sekine, T. Tamura, M. Akay, T. Fujimoto, T. Togawa, and Y. Fukui. Discrimination of walking patterns using wavelet-based fractal analysis. Neural Systems and Rehabilitation Engineering, IEEE Transactions on, 10(3):188--196, 2002.
[24]
M. Sekine, T. Tamura, T. Togawa, and Y. Fukui. Classification of waist-acceleration signals in a continuous walking record. Medical engineering & physics, 22(4):285--291, 2000.
[25]
R. Yazicioglu, T. Torfs, J. Penders, I. Romero, H. Kim, P. Merken, B. Gyselinckx, H. Yoo, and C. Van Hoof. Ultra-low-power wearable biopotential sensor nodes. In Engineering in Medicine and Biology Society, 2009. EMBC 2009. Annual International Conference of the IEEE, pages 3205--3208. IEEE, 2009.
[26]
B. Zhai, L. Nazhandali, J. Olson, A. Reeves, M. Minuth, R. Helfand, S. Pant, D. Blaauw, and T. Austin. A 2.60 pj/inst subthreshold sensor processor for optimal energy efficiency. In VLSI Circuits, 2006. Digest of Technical Papers. 2006 Symposium on, pages 154--155. IEEE, 2006.

Cited By

View all
  • (2021)Vision and Inertial Sensing Fusion for Human Action Recognition: A ReviewIEEE Sensors Journal10.1109/JSEN.2020.302232621:3(2454-2467)Online publication date: 1-Feb-2021
  • (2019)Orientation Independent Activity/Gesture Recognition Using Wearable Motion SensorsIEEE Internet of Things Journal10.1109/JIOT.2018.28561196:2(1427-1437)Online publication date: Apr-2019
  • (2017)A survey of depth and inertial sensor fusion for human action recognitionMultimedia Tools and Applications10.1007/s11042-015-3177-176:3(4405-4425)Online publication date: 1-Feb-2017
  • Show More Cited By

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences
ICCPS '13: Proceedings of the ACM/IEEE 4th International Conference on Cyber-Physical Systems
April 2013
278 pages
ISBN:9781450319966
DOI:10.1145/2502524
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 08 April 2013

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. activity monitoring
  2. body sensor networks
  3. power optimization
  4. signal processing
  5. wearable computing

Qualifiers

  • Research-article

Funding Sources

Conference

ICCPS '13
Sponsor:

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)1
  • Downloads (Last 6 weeks)0
Reflects downloads up to 20 Jan 2025

Other Metrics

Citations

Cited By

View all
  • (2021)Vision and Inertial Sensing Fusion for Human Action Recognition: A ReviewIEEE Sensors Journal10.1109/JSEN.2020.302232621:3(2454-2467)Online publication date: 1-Feb-2021
  • (2019)Orientation Independent Activity/Gesture Recognition Using Wearable Motion SensorsIEEE Internet of Things Journal10.1109/JIOT.2018.28561196:2(1427-1437)Online publication date: Apr-2019
  • (2017)A survey of depth and inertial sensor fusion for human action recognitionMultimedia Tools and Applications10.1007/s11042-015-3177-176:3(4405-4425)Online publication date: 1-Feb-2017
  • (2016)Inertial Measurement Unit-Based Wearable Computers for Assisted Living Applications: A signal processing perspectiveIEEE Signal Processing Magazine10.1109/MSP.2015.249931433:2(28-35)Online publication date: Mar-2016
  • (2016)MotionSynthesis Toolset (MoST): An Open Source Tool and Data Set for Human Motion Data Synthesis and ValidationIEEE Sensors Journal10.1109/JSEN.2016.256259916:13(5365-5375)Online publication date: Jul-2016
  • (2014)Demonstration abstract: upper body motion capture system using inertial sensorsProceedings of the 13th international symposium on Information processing in sensor networks10.5555/2602339.2602410(351-352)Online publication date: 15-Apr-2014
  • (2014)MotionSynthesis toolset (MoST)Proceedings of the 4th ACM MobiHoc workshop on Pervasive wireless healthcare10.1145/2633651.2637472(25-30)Online publication date: 11-Aug-2014
  • (2014)Fusion of Inertial and Depth Sensor Data for Robust Hand Gesture RecognitionIEEE Sensors Journal10.1109/JSEN.2014.230609414:6(1898-1903)Online publication date: Jun-2014
  • (2014)Demonstration abstract: Upper body motion capture system using inertial sensorsIPSN-14 Proceedings of the 13th International Symposium on Information Processing in Sensor Networks10.1109/IPSN.2014.6846798(351-352)Online publication date: Apr-2014
  • (2014)Cyber physical system: Paper survey2014 International Conference on ICT For Smart Society (ICISS)10.1109/ICTSS.2014.7013187(273-278)Online publication date: Sep-2014
  • Show More Cited By

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Media

Figures

Other

Tables

Share

Share

Share this Publication link

Share on social media