May 1, 2023

A Low-power and Reliable Body Area Network Platform for Rehabilitation Applications

Fabien Massé 1

Shyamal Patel 2,3, Julien Penders 1, Bert Gyselinckx 1, and Paolo Bonato 2,4

1 Holst Centre / imec, Eindhoven, The Netherlands2 Dept. of Physical Medicine and Rehabilitation, Harvard Medical School, Boston MA 3 Dept. of Electrical and Computer Engineering, Northeastern University, Boston MA4 Harvard-MIT Division of Health Sciences and Technology, Cambridge MA

First AMA-IEEE Conference

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Motion

EKG & Respiration

A Low-power and Reliable BAN Platform for Rehabilitation Applications

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Background and Motivation

• Current limitations in rehabilitation monitoring In-patient: limited feedback to the patients on

his/her exercises Lack of tools for long-term quantitative

assessment Out-patient: No effective way to get information

about functional gain in daily life [1]

• Key advantages of Body Area Networks Wearable >> Comfort of use and set-up Low-power >> Longitudinal assessment of

patient’s recovery Reliable >> High data integrity for on-line and

off-line recordings Real-time >> Feedback or close-loop systems

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A Low-power and Reliable BAN Platform for Rehabilitation Applications

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Low-power and reliable BAN platform

• Low-power Imec’s ultra-low-power sensors [2] 3+ days of autonomy while continuously

transmitting the data Lightweight: <20 grams Small form factor: 52 x 32 x 15 mm3

• Reliable communication Optimized wireless communication based on quality

of service rules [3] Reduce data losses while maintaining limited latency

• Main features Multiple nodes: up to 10 in the same network Multiple sensors: ECG, EMG, Respiration,

Acceleration Tunable sampling frequencies: Up to 1KHz Wireless transmission or data logging on the nodes

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A Low-power and Reliable BAN Platform for Rehabilitation Applications

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Platform architecture

• Communication protocol Star network : TDMA-based MAC protocol Application-oriented Quality-of-Service (QoS) layer [2]

Application-oriented retransmission mechanism Efficient balancing of : data integrity, autonomy,

and latency Radio communication reliability

Processing unit (backside)Texas Instrument MSP430F1611• 8MHz• 10KB RAM/48KB ROM

Radio transceiverNordic Semi nRF24L01• 2.4GHz / 2Mbps

Ultra low-power biopotential sensor[1]•ECG, EMG, EEG signals

•Ultra Low Power Dissipation 21 μA @ 3V

Optional AccelerometerAnalog Devices ADXL330 • -/+ 3g

Data storageSD-card support for accurate offline data analysis

Power management

Top | Bottom

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A Low-power and Reliable BAN Platform for Rehabilitation Applications

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Preliminary evaluation for in- and out- patients

• Validation protocol Two ambulatory environments

Office environment > daily-working activities Clinical setting > limited displacements

Sensor setup 6 nodes All nodes : 3D Acceleration (40 Hz) Two nodes : extra EMG (500Hz) Central node : extra ECG (200Hz)

• Qualitative feedback from clinicians Comfortable Easy-to-setup User-friendly GUI Multi-modal sensor network

Real-time application Balenced latency Offline0

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Pac

ket E

rror R

ate

(%)

Office environmentClinical environment

4.95 %

7.97 %

1.29 %

0.80 %

1.35%

0.27%

latency <1000 mslatency <300 ms No latency constraints

© Holst Centre

A Low-power and Reliable BAN Platform for Rehabilitation Applications

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Future work

• Explore ways to cope with bursts of packet losses Local processing Context-aware QoS layer On-node data storage

• Clinical trials On epileptic patients in clinical environment Scheduled for summer 2010

[1] Bonato P., “Wearable sensors/systems and their impact on biomedical engineering”, IEEE EMBS Magazine 2003

[2] Yazicioglu R.F. et al., “A 60 μW 60 nV/√Hz readout front-end for portable biopotential acquisition systems”, IEEE ISSC Conf, 2006

[3] Massé F. and Penders J., “Quality-of-Service in BAN: PER reduction and its trade-offs”, BSN 2010

References

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