IIT Bombay

1. 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 A Wearable Inertial Sensing Device for Fall Detection and Motion Tracking Praveen Kumar Prem C. Pandeyerpraveen @ iitb.ac.in, pcpandey @ ee.iitb.ac.in IIT Bombay

2. Outline Introduction Hardware Design Data Acquisition & Testing Real-Time Fall Detection Summary & Conclusion

3. 1. INTRODUCTION 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. • Actigraphy Logging of orientation & movement of limbs and torso for analysis & treatment of sleep disorders. Techniques ▫ Optical ▫ Image based ▫ Acoustic ▫ Magnetic ▫ Inertial sensing

4. 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.

5. 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.

6. 2. HARDWARE DESIGN Design objective Continuous 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 devices Internal memory: data recording Compact & wearable: single supply operation with low power consumption, no switches & connectors. Components MEMS-based sensor with integrated tri-axial accelerometer & gyroscope; Microcontroller; Flash memory; Serially interfaced Bluetooth module; Regulator

7. Sensor MEMS-based sensor with integrated tri-axial accelerometer & gyroscope: InvenSenseMPU 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

8. Microcontroller 16-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 fCYof 4 MHz • Vdd: 2 – 3.6 V, Idd: 2.9 mA (at 4 MIPS) Memory 64-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

9. Bluetooth Module Serially 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) Power MCP 1802 LDO regulator: 3.3 V output for 3.5 – 12 V input, with max current of 300 mA.

10. Block diagram

11. Micro-controller pin connections

12. Sensor inter-facing

13. Memory inter-facing

14. Serial communication & Bluetooth interface

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

16. 3. DATA ACQUISITION & TESTING Sample-by-sample data acquisition Read 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 acquisition Read 1024 bytes from FIFO at each interrupt; write to flash using page program; check for IRQ from UART and service it if needed.

17. Testing & calibration • 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 PC based GUI for operation control & data transfer through Bluetooth Test setup: Control Moment Gyroscope Model 750 (Educational Control Products)

18. ResultsAccelerometer outputs: Max deviations of 0.06, 0.01, 0.09 g in x, y, zGyroscope outputs: Close match to CMG encoder outputs Example: device output for x-axis (solid), CMG output (broken)

19. Accelerometer outputs during simulated falls

20. 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)

21. 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 Ifdi(n) > θfor duration less than t1., reset. Ifdi(n) > θfor duration greater than t1but less than t2, declare fall. Ifdi(n) > θfor duration greater than t2, wait for di(n) < θand then reset.

22. 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.

23. Test results 100% 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.

24. 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 work Extensive testing on a large number of subjects. Fusion of accelerometer and gyroscope data and fusion of data from multiple devices.

25. Thank You