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
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
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
Objective
Development 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
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 fC Y 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
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
erpraveen @ iitb.ac.in, pcpandey @ ee.iitb.ac.in 9/25
Bluetooth ModuleSerially interfaced Bluetooth module: Roving Networks RN-42
Range: 20 m range
Data rate: 240 kbps in slave mode
Vdd: 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.
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 MODULE
POWER SUPPLY
erpraveen @ iitb.ac.in, pcpandey @ ee.iitb.ac.in 11/25
Micro-controller pin connections
DVDD3V3
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
erpraveen @ iitb.ac.in, pcpandey @ ee.iitb.ac.in 12/25
Sensor inter-facing
SCL2 SCL
SDA
INT
FSYNC
VDD
CPOUT
23
24
1225
23
248
24FJ64GB004MICROCONTROLLER
SDA2
INT
11
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
erpraveen @ iitb.ac.in, pcpandey @ ee.iitb.ac.in 13/25
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
erpraveen @ iitb.ac.in, pcpandey @ ee.iitb.ac.in 14/25
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
erpraveen @ iitb.ac.in, pcpandey @ ee.iitb.ac.in 15/25
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
erpraveen @ iitb.ac.in, pcpandey @ ee.iitb.ac.in 16/25
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.
erpraveen @ iitb.ac.in, pcpandey @ ee.iitb.ac.in 17/25
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
erpraveen @ iitb.ac.in, pcpandey @ ee.iitb.ac.in 18/25
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)
erpraveen @ iitb.ac.in, pcpandey @ ee.iitb.ac.in 19/25
Accelerometer outputs during simulated falls
erpraveen @ iitb.ac.in, pcpandey @ ee.iitb.ac.in 20/25
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)
erpraveen @ iitb.ac.in, pcpandey @ ee.iitb.ac.in 21/25
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.
erpraveen @ iitb.ac.in, pcpandey @ ee.iitb.ac.in 23/25
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 wirelesslyReal-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.
A wearable inertial sensing device for Continuously sensing and recording of the motion related variables, &
transmitting the data wirelesslyReal-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
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