Next generation human body sensing

Alex Casson

@a_casson

alex.casson@manchester.ac.uk

www.eee.manchester.ac.uk/sisp

Smart sensors

2

Smart sensors in Manchester

3

Me

Online signal processing

4

Reduce system power.

[Chen et al., Wearable sensors, 2014]

Case study: Compression

1cm

5

Compression on an Texas Instruments MSP430

[Imtiaz et al., IEEE T-BME, 2014]

Improving battery lifetime

Wireless transmitter Local memory

6

[Imtiaz et al., IEEE T-BME, 2014]

Compression on an Texas Instruments MSP430

Improving battery size

7

[Chen et al., Wearable sensors, 2014]

Smart sensors

8[Chen et al., Wearable sensors, 2014]

Reduce system power.

Increase functionality.

Better quality recordings.

Minimise system latency.

Reduce amount of data to analyse.

Reliable operation over unreliable wireless.

Enable closed loop: recording – stimulation.

Data redaction for privacy.

Design strategy

9[Chen et al., Wearable sensors, 2014]

ASICs Discrete components

Low power CWT

420 µm

10[Casson et al., IEEE JSSC, 2011]

[Casson et al., EL, 2014]

Power: 60 pW – 1 nW

ECG CWT

11[Casson et al., EL, 2014]

Power: 1.3 nW

11

Low power DWT

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Power: 114 nW

12

120 µm

[Casson, Sensors, 2015]

State of the art

13

[Casson, EUSIPCO, 2015]

State of the artRef. Features Classifier Algorithm

performance

Power

performance

[Zhang,

2011]

Frequency information

(bandpass filter)

- - 3 µW

[Chen,

2011]

Signal agnostic compressive

sensing

10 dB SNR 2 µW

[Sridhara,

2011]

Frequency information

(FFT)

Threshold - 1 µW

[Lee,

2013]

Frequency information

(IIR filter)

SVM - 273 µJ /

classification

[Yoo,

2013]

Frequency information

(FIR filter)

SVM 83% detection rate

5% false rate

2 µJ /

classification

[Chen et al., Wearable sensors, 2014] 14

TRADITIONAL ALGORITHMS:

– Two way trade-off: correct detections and false positives.

WEARABLE ALGORITHMS:

– Three way trade-off: correct detections, false positives, and power.

Wearable algorithms

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Power delivery

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Energy harvesting

1 μW: Analogue watch.

10 μW: Digital watch.

100 μW: Ultra low power sensor node.

1000 μW: Low power sensor node.

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Energy harvesting

Interesting

bio-signals

More power

More motion interference

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PPG during motion

Chest ECG

Wrist PPG

Foot PPG

[TomTom]

Time / s

PP

G / µ

VP

PG

/ µ

VE

CG

/ µ

V

Signal processing

20

Power consumption

Noise

4.3% increase in performance

Case study: Noise

This can be

used to reduce

the noise

performance

and dynamic

range

requirements of

the hardware

and hence

reduce power

[Casson, HCII, 2013]

[Casson et al., J. Neurosci. Meth., 2011] 21

Example EEG

Time varying performance

[Casson, Front. Neurosci., 2014]

Electrodes

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[Wearable][IMEC][g.tec] [Cognionics] [Enobio] [Mindo]

Transfer tattoos

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ECG electrodes:

• (Left) Conventional gel based Ag/AgCl.

• (Middle) Copper printed capacitive.

• (Right) Silver printed on tattoo paper.

[Batchelor et al., IEEE EMBC, 2015]

Signal-to-Noise Ratio / dB

Mean St. Dev.

Ag/AgCl

Time domain 19.9 0.9

CWT domain 36.2 0.7

Tattoo

Time domain 19.3 3.9

CWT domain 26.2 5.3

Feedback / treatment

25

Sound stimulation.

Transcranial current stimulation.

Aim:

Data driven treatments based upon time and power constrained signal

collection and analysis

Summary

Individual personalised manufacture

Individual data response feedback and action

Individual optimized treatment

@a_casson

alex.casson@manchester.ac.uk

www.eee.manchester.ac.uk/sisp