Investigating the on-board data processing for IMU-based sensors in motion tracking for rehabilitation

This paper investigates the effects of shifting computation-intensive workload for motion tracking based on sensor network from a dedicated remote server to an embedded microcontroller. The importance of battery life is exacerbated for devices designed to work in biomedical field as the prolonged operability could make a vital difference in mobile contexts. On-board processing usually drives to less communication, so the balance between the power consumed by more operations and that saved by limiting the radio transmissions has been evaluated. A measurement station has been realized to measure the energy budget needed to let more patients be monitored simultaneously and, at the same time, to increase the number of sensor nodes working together in the network. Finally, functional equivalence of the implementation has been proven.

[1]  Dejan Raskovic,et al.  Dynamic Voltage and Frequency Scaling For On-Demand Performance and Availability of Biomedical Embedded Systems , 2009, IEEE Transactions on Information Technology in Biomedicine.

[2]  Luca Benini,et al.  Low-power processor architecture exploration for online biomedical signal analysis , 2012, IET Circuits Devices Syst..

[3]  K. Dudacek,et al.  Experimental Evaluation of the MSP430 Microcontroller Power Requirements , 2007, EUROCON 2007 - The International Conference on "Computer as a Tool".

[4]  Pasquale Daponte,et al.  Design and validation of a motion-tracking system for ROM measurements in home rehabilitation , 2014 .

[5]  Alan Jay Smith,et al.  Reducing processor power consumption by improving processor time management in a single-user operating system , 1996, MobiCom '96.

[6]  Sebastian Madgwick,et al.  Estimation of IMU and MARG orientation using a gradient descent algorithm , 2011, 2011 IEEE International Conference on Rehabilitation Robotics.

[7]  Konstantin Mikhaylov,et al.  Optimization of microcontroller hardware parameters for Wireless Sensor Network node power consumption and lifetime improvement , 2010, International Congress on Ultra Modern Telecommunications and Control Systems.

[8]  Konstantina S. Nikita,et al.  Handbook of Biomedical Telemetry: Nikita/Handbook of Biomedical Telemetry , 2014 .

[9]  Pasquale Daponte,et al.  A wireless-based home rehabilitation system for monitoring 3D movements , 2013, 2013 IEEE International Symposium on Medical Measurements and Applications (MeMeA).

[10]  P. Daponte,et al.  Electronic measurements in rehabilitation , 2011, 2011 IEEE International Symposium on Medical Measurements and Applications.

[11]  Robert Szewczyk,et al.  System architecture directions for networked sensors , 2000, ASPLOS IX.

[12]  Subhas Chandra Mukhopadhyay,et al.  Wearable and Autonomous Biomedical Devices and Systems for Smart Environment: Issues and Characterization , 2010 .

[13]  Pasquale Daponte,et al.  Validation of a home rehabilitation system for range of motion measurements of limb functions , 2013, 2013 IEEE International Symposium on Medical Measurements and Applications (MeMeA).

[14]  J. Millet,et al.  Adapting power consumption to performance requirements in a MSP430 microcontroller , 2005, Conference on Electron Devices, 2005 Spanish.

[15]  Pasquale Daponte,et al.  Experimental comparison of orientation estimation algorithms in motion tracking for rehabilitation , 2014, 2014 IEEE International Symposium on Medical Measurements and Applications (MeMeA).

[16]  Harrison,et al.  [IEEE 2011 IEEE 12th International Conference on Rehabilitation Robotics: Reaching Users & the Community (ICORR 2011) - Zurich (2011.06.29-2011.07.1)] 2011 IEEE International Conference on Rehabilitation Robotics - Estimation of IMU and MARG orientation using a gradient descent algorithm , 2011 .