1. Reporter: AGNES Purwidyantri Student ID no: D0228005 Biomedical Engineering Dept.

2.  Recent Inertial sensor with MEMS technology: airbag deployment and automotive pressure sensors, Nintendo® Wii™ and the Apple® iPhone  Future outlook:  Detection of acceleration and deceleration.  MEMS accelerometers and gyroscopes in areas such as medical devices, industrial equipment, consumer electronics, and automotive electronics.  Commercially available MEMS accelerometers and gyroscopes to transform an incredibly diverse scope of end products though the five types of motion sensing.

3.  Acceleration (remember, including translational movement) measures the change in velocity in a unit of time ((m/s 2).  Velocity = (m/s) and includes both the rate of displacement and direction of movement. It follows that acceleration is measured in meters per  Deceleration: Acceleration with a negative value–imagine a car slowing down as brake applied  Vibration : acceleration and deceleration that happens quickly and in a periodic manner.

4.  Shock : acceleration that occurs instantaneously but it is a nonperiodic function that typically happens once.  Tilt : When an object is moved to alter its tilt, or inclination, some change in position with respect to gravity is involved. That movement tends to happen rather slowly compared with vibration and shock.  Rotation : measure of angular rate motion  may take place without a change in acceleration (1)

5.  These first four modes involve a certain aspect of acceleration  They are measured by g-force (gravity) (One g = 9.8 m/s 2.)  A MEMS accelerometer detects tilt by measuring the effect the force of gravity exerts on the axes of the accelerometer.  In the instance of a 3-axis accelerometer, three separate outputs measure acceleration along the X, Y, and Z axes of motion. An accelerometer, and a size comparison

6.  MEMS gyroscopes are used to sense this rotational motion.  Certain end products must measure rotation in addition to other forms of motion, gyroscopes may be integrated in an IMU (inertial measurement unit) that embeds a multiaxis gyroscope and multiaxis accelerometer.

7. Newton’s second law of motion (F=ma) & Hooke’s law (F=-kx) where, m = mass (kg) a = acceleration (m/s 2 ) k = spring constant (N/m) x = displacement (m)

8.  The mass is allowed to move in one direction which is the sensitive direction of the accelerometer. The displacement of the mass is a measure of the difference of acceleration (a) and gravity (g) along the sensitive axis given by the unit vector n. A to be measured electrical signal S A, n, is related to these physical quantities according to: with A n, k representing the scaling factor and A n, o the offset

10. A. Explicit Information  Acceleration: raw data, combination of dynamic movement (acceleration) with static gravity force  Vibration: fast back-and-forth magnitude periodically  Shock: a significant change in magnitude within a short time period

11. B. Implicit Information  Distance: double integration of a linear acceleration  Potential issues  Acceleration measured contains gravity force.  The pure movement should subtract the gravity.  Errors (noise, calculations, etc.)  accumulated fast from integration

12. B. Implicit Information  Tilt: angles between gravity vector and the coordination axes when net acceleration or force is gravity  Potential issues  Dynamic tilting or inclination information required addition information  Computational complexity

13.  Inertial and portable sensors are becoming widely accepted and greatly interesting tools for the assessment of human motion in clinical settings and in scientific research.  „Integration of such sensors  monitoring system & functional evaluation of human movement  Benefits: „ Low cost, „ small size, „ low-power systems, long term, „ unsupervised, „ free-living conditions.

14.  Activity recognition is the attempt to recognize actions and motions of a user from a observations from their actions using various sensors.  A scenario like this: a grandmother wakes up her small house where she currently lives alone. She turns on the stove and starts cooking her breakfast, After going through her morning routine, tracked via a device, a computer-generated voice gently reminds her to turn off everything. The activities she's done is stored to a server so her loved ones can make sure that she is doing alright, even miles from her. An example of visual activity recognition, in this case, tracking specific movements within martial arts.

15.  2-axis accelerometers (ADXL210) with a range of ±10g and frequency response between 0-500 Hz.  Accelerometers were built into a pager-size box in a way to resemble a 3-axis accelerometer.  The box also included a AA battery, a radio, and some anti- aliasing circuitry.  Resolution of 0.025g. With a sampling rate of 45 Hz the device drew 15 mA from the 1.5V battery

16.  Mobile accelerometry is in the detection of falls of elderly people.  A reliable fall detector would not be able to prevent falls of course, however, with the proper infrastructure it could reduce the arrival time of the paramedics when a fall had occurred, and thus greatly reducing the strain on the body and fear of being helpless after a fall.  The first step is a collision detection.  When an impact above a certain threshold has occurred the device enters a continuous monitoring stage until no more impacts are detected (this is to account for a person falling down several steps).  The second step determines if the orientation of the device before and after the impact has changed significantly, since a walking person most likely will be lying down after a fall.

17. 2 Steps fall detection algorithm Commercial fall detector from Tunstall

18.  To classify and identify people with existing or growing balance problems in- time, before the first fall occur.  2 triaxial accelerometers, one attached at the head with a helmet, and the other strapped to the hip with a belt.  Both accelerometers were connected to a laptop placed in a backpack carried by the test subject.  A sampling rate of 200 Hz was used.  The tests consisted of walking down a 20m long times 1.5m wide hallway.  Two different surfaces : plane surface and one covered with 5mm artificial grass, two layers of 20mm thick soft foam rubber, and 20mm thick wooden blocks of varying shapes and sizes laid out in an arbitrary matter. Finite Fourier Series

21.  Gravitational acceleration  constant component of the measured acceleration  establish the orientation of the accelerometer with regards to the earths gravity.  The measured acceleration shifts between the three axis when the accelerometer is tilted, projecting the horizontal components, measured in the accelerometers reference frame, onto a horizontal plane on the ground, one can get an outline of the accelerometers movement. Modeling Approximation of the human body. By displacing the hip, the upright posture can be maintained

22.  Accelerometer below hip  Accelerometer above hip

23. 1. http://www.analog.com/en/content/over_five_motion_senses/fca.html http://www.analog.com/en/content/over_five_motion_senses/fca.html 2. http://www.xsens.com/images/stories/PDF/Inertial_Sensing_of_Human_Move ment_Thesis_Luinge.pdf http://www.xsens.com/images/stories/PDF/Inertial_Sensing_of_Human_Move ment_Thesis_Luinge.pdf 3. M. Chang. 2006. Review of Clinical Applications with Human Accelerometry Marcus Chang Department of Computer Science University of Copenhagen Denmark marcus@diku.dkmarcus@diku.dk 4. K. Doughty, R. Lewis, and A. McIntosh. 2000. “The Design of a Practical and Reliable Fall Detector for Community and Institutional Telecare”, Journal of Telemedicine and Telecare, vol. 6. sup. 1. 5. M. J. Mathie, A. C. F. Coster, N. H. Lovell, and B.G. Celler. 2003. “Detection of Daily Physical Activities Using a Triaxial Accelerometer”, Med. Biol. Eng. Comput., 41, 296-301. 6. Hylton B. Menz,, Stephen R. Lord, Richard C. Fitzpatrick. 2003. Acceleration patterns of the head and pelvis when walking on level and irregular surfaces. Gait and Posture, 18. 35-46. 2003 Hylton B. MenzStephen R. Lord 7. R. E. Mayagoitia, J. C. Lötters, P. H. Veltink, and H. Hermens. 2002. “Standing Balance Evaluation Using a Triaxial Accelerometer”, Gait and Posture, 16. 55-59. 2002.

24. Thank you