erpraveen @ iitb.ac.in, pcpandey @ ee.iitb.ac.in 1/25

Indicon2013, Mumbai, 13-15 December 2013, Paper ID 1084 Track 4.1 Signal Processing & VLSI (Biomedical Systems & Signal Processing )Sunday, 15-12-2013, 1540 – 1710

IIT Bombay

Praveen KumarPrem C. Pandey

erpraveen @ iitb.ac.in, pcpandey @ ee.iitb.ac.in

A Wearable Inertial Sensing Device for Fall Detection and Motion Tracking

erpraveen @ iitb.ac.in, pcpandey @ ee.iitb.ac.in 2/25

1.Introduction

2.Hardware Design

3.Data Acquisition & Testing

4.Real-Time Fall Detection

5.Summary & Conclusion

Outline

erpraveen @ iitb.ac.in, pcpandey @ ee.iitb.ac.in 3/25

Posture & Motion Monitoring Aids for assisted living

Fall detection & alarm device to be worn by elderly persons and patients with risk of losing balance.

Monitoring of limb movement for analysis of gait disorders in patients suffering from neuromuscular diseases.

ActigraphyLogging of orientation & movement of limbs and torso for analysis & treatment of sleep disorders.

Techniques▫ Optical ▫ Image based ▫ Acoustic ▫ Magnetic ▫ Inertial sensing

1. INTRODUCTION

erpraveen @ iitb.ac.in, pcpandey @ ee.iitb.ac.in 4/25

MEMS inertial sensors: accelerometer (linear acceleration) & gyroscope (angular velocity)

• Low-cost, compact, & free from interference problems.• No restrictions on the movement space.

Observations based on the literature• Only accelerometer or only gyroscope: good results for restricted movement in specific

directions.

Multiple sensors: recognition of a larger types of activities, better accuracy.• System with sensors on multiple body parts for tracking relative movement of different body

parts.• System for fall detection: head, waist, trunk, and thigh found to be good sensor placement

locations, wrist found to be unsuitable.• Multiple signal fusion & fuzzy inference systems: enhanced accuracy but not well suited for real-

time applications. • Threshold based fall detection: well suited for real-time fall detection but lower accuracy.

erpraveen @ iitb.ac.in, pcpandey @ ee.iitb.ac.in 5/25

ObjectiveDevelopment of a wearable inertial sensing device with wireless connectivity

Real-time fall detection & alarm

Recording for gait analysis

Logging for actigraphy

Hardware: Tri-axial integrated accelerometer & gyroscope, microcontroller, nonvolatile memory, Bluetooth.

Signal processing for fall detection: Multiple decomposition and thresholding of tri-axial accelerometer outputs.

Software: interfacing, recording, signal processing.

erpraveen @ iitb.ac.in, pcpandey @ ee.iitb.ac.in 6/25

2. HARDWARE DESIGNDesign objectiveContinuous acquisition of acceleration & angular velocity data: settable sampling frequency: 100 Hz or higher for gait monitoring and fall detection, < 20 hz for actigtraphy.Processing capacity for real-time fall detection.

Wireless connectivity: operation control, data transfer, fusion of data from multiple devicesInternal memory: data recording Compact & wearable: single supply operation with low power consumption, no switches & connectors.

ComponentsMEMS-based sensor with integrated tri-axial accelerometer & gyroscope; Microcontroller; Flash memory; Serially interfaced Bluetooth module; Regulator

erpraveen @ iitb.ac.in, pcpandey @ ee.iitb.ac.in 7/25

Sensor

MEMS-based sensor with integrated tri-axial accelerometer & gyroscope: InvenSense MPU 6000

Acc. range: ±2 g, ±4 g, ±8 g, ±16 g; Gyro. range: ±250 °/s, ±500 °/s, ±1000 °/s, ±2000 °/s

Sampling frequency: 4 Hz – 8 kHz 16-bit ADCs, clock, temp. sensor, interrupts Digital output: I2C, SPI FIFO: 1024 bytes (85 samples) Vdd: 2.375 – 3.46 V, Idd: 3.9 mA

erpraveen @ iitb.ac.in, pcpandey @ ee.iitb.ac.in 8/25

Microcontroller16-bit microcontroller: Microchip PIC24F64GB004 (44 pin) 35 I/O pins, Two SPI, two I2C, two UART, one USB 64 KB program memory, 8 KB RAM,. Internal clock of 8 MHz FRC with fCY of 4 MHz Vdd: 2 – 3.6 V, Idd: 2.9 mA (at 4 MIPS)

Memory64-Mb serial dual I/O flash memory: Microchip SST25VF064C Nonvolatile memory for recording more than 12 hours of data for actigraphy;

Burst mode data transfer to save processor time for real-time fall detection and data transfer from multiple modules in a time multiplexed manner

Vdd: 2.7 – 3.6 V, Idd: 25 mA

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Bluetooth ModuleSerially interfaced Bluetooth module: Roving Networks RN-42Range: 20 m range Data rate: 240 kbps in slave modeVdd: 3.3 V, Idd: 3 mA (connected) & 30 mA (data transfer)

PowerMCP 1802 LDO regulator: 3.3 V output for 3.5 – 12 V input, with max current of 300 mA.

erpraveen @ iitb.ac.in, pcpandey @ ee.iitb.ac.in 10/25

Block diagram

UART

I2C

SPIMICROCONTROLLER

TRIAXIALACC. & GYRO.

SENSOR

MEMORY

BLUETOOTH MODULEPOWER SUPPLY

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Micro-controller pin connectionsDVDD3V3

VCAP

DISVREG

PGEC1

PGED1

MCLR

AVDDC10 0.1 µ17

DVSS

24FJ64GB004MICROCONTROLLER

16

28

29

40

39

AVSS

VDD

VSS

VDD

VSS1,2,3,4,5,44

23,24,25

U1 (MEMORY)in Fig. 5

U4 (SENSOR)in Fig. 3

0.1 µ

0.1 µ

C6

C7

U5

34,35,36,37U3 (BLUETOOTH )

in Fig. 4

5

4

1

2

3

DVDD3V3

38,41,43

C9 7

6

22

21

18

DEBUG10 µ

R6

100

CON5

DVSS

CN1

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Sensor inter-facing

SCL2 SCL

SDA

INT

FSYNC

VDD

CPOUT

23

24

1225

23

24 8

24FJ64GB004MICROCONTROLLER

SDA2

INT11

DVDD3V3

R3R44.7k 4.7k

U5

/CS

REGOUT

13

10

20

CLKIN

AD0

GND1

9

18

DVDD3V3

0.1 µ

0.1 µ

2.2 n

C12

C4

C11

DVSS MPU-6000SENSOR

U4

DVSS

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Memory inter-facing DVDD3V3RP8 HOLD

CE

WP

SO

SI

SCK

VDD

VSS

7

1

3

2

5

61

2

4

3

5

44

8

4

DVSS

SST25VF064CFLASH MEMORY

RP25

RP23

SDI1

SDO1

SCK1

0.1 µ

C3

U5 U1

24FJ64GB004MICROCONTROLLER

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Serial communication & Bluetooth interface

GND

GNDRP19

RP20

RB5

RB7

RC5

RA9

RA4

Tx

Rx

PIO2

PIO3

PIO4

PIO6

RESET

PIO5

PIO8

14

13

19

20

22

3

534

35

38

43

41

37

36 1

12

11

21

31

LED1 (R) 220

DVSS24F64GB004MICROCONT.

RN-42BLUETOOTH

U5 U3

R5

LED2 (G) 220

R7

DVDD3V3

DVSSCON5

2 41 3 5

SERIALCN2

J1 J2

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Circuit assembly2-layer 36 mm x 29 mm PCB, No switches & connectors

Top View Bottom view

Packaged ISM

Bluetooth Sensor

Flash Memory

Microcontroller

ISM Battery

Acrylic box

Acrylic cover

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3. DATA ACQUISITION & TESTING

Sample-by-sample data acquisitionRead the 6-axis sensor data at each sampling interval; save the data in internal 252 bytes buffer. If internal buffer is full, write 252 byte- data to the memory using page program

Burst mode data acquisitionRead 1024 bytes from FIFO at each interrupt; write to flash using page program; check for IRQ from UART and service it if needed.

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Testing & calibration

PC based GUI for operation control & data transfer through Bluetooth

Test setup: Control Moment Gyroscope Model 750 (Educational Control Products)

Central platform with two outer rings Encoders to record the angles of rotation

using a PC Brakes for fixing angular positions

•Testing Device mounted on central platform Movements of platform or the rings Simultaneous recording of the sensor outputs by the device & encoder outputs using PC

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Results

Accelerometer outputs: Max deviations of 0.06, 0.01, 0.09 g in x, y, z

Gyroscope outputs: Close match to CMG encoder outputs

Example: device output for x-axis (solid), CMG output (broken)

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Accelerometer outputs during simulated falls

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4. REAL-TIME FALL DETECTION Observations from the accelerometer recordings

Fall: Large variation from the mean value for a certain duration and in a certain direction.

Multiple direction decomposition of accelerometer output and thresholding can help in improving sensitivity & specificity of the detection, without using gyroscope outputs.

Real-time fall detection method: Thresholding & duration window on 7 directional components

Components: Three axial components of the acceleration, magnitudes of the acceleration in three orthogonal planes, and the magnitude in the three-dimensional space

v1(n) = x(n), v2(n) = y(n), v3(n) = z(n)

v4(n) = √(x(n)2 + y(n)2), v5(n) = √(y(n)2 + z(n)2), v6(n) = √(x(n)2 + z(n)2)

v7(n) = √(x(n)2 + y(n)2 + z(n)2)

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Variation function for each component

100-point moving avg. mi(n) = mi(n − 1) + [vi(n) − vi(n − 100)]/100

di(n) = │vi(n) − mi(n)│

• Thresholding & duration window on each variation function

If di(n) > θ for duration less than t1., reset.

If di(n) > θ for duration greater than t1 but less than t2, declare fall.

If di(n) > θ for duration greater than t2, wait for di(n) < θ and then reset.

erpraveen @ iitb.ac.in, pcpandey @ ee.iitb.ac.in 22/25

Tests with falls & activities of daily life (ADL)Simulated fall Real fall & ADL

Falls: forward, backward, sideways. ADL: walking, sitting, getting up, stair climbing, jogging, skipping.No of trials: 5 of each type.

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Test results100% sensitivity and specificity, with θ = 2g, t1 = 250 ms, t2 = 850 ms.

Variation functions crossed threshold (for less than t1 = 250 ms) during skipping, jogging, and fast sitting, but not during other ADLs.

Fall successfully detected with any orientation of the device.

Current drain of 40 mA during wireless transmission and 3 mA during sleep mode.

Data recording for approx. 2 hours at sampling freq. of 100 Hz.

erpraveen @ iitb.ac.in, pcpandey @ ee.iitb.ac.in 24/25

5. SUMMARY & CONCLUSION A wearable inertial sensing device for

Continuously sensing and recording of the motion related variables, & transmitting the data wirelessly

Real-time fall detection and wireless alert to a base station

A low complexity fall detection algorithm for

separation of activities of daily life from the fall using the acceleration data with any orientation of the waist-worn device.

Further workExtensive testing on a large number of subjects.Fusion of accelerometer and gyroscope data and fusion of data from

multiple devices.

erpraveen @ iitb.ac.in, pcpandey @ ee.iitb.ac.in 25/25

Thank You