biomedical transducer: inertial sensors
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Biomedical Transducers - Inertial Sensors 1
Biomedical Transducers a.a. 2011/12
Inertial SensorsDaniele AntonioliLuca Faggianelli
Jian HanMekki Mtimet
6/16/2012
Biomedical Transducers - Inertial Sensors 2
Outline
Introduction to Inertial Sensors;
Static Evaluation of the Noise;
Sit to Stand Task Evaluation;
Conclusions.
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Inertia and Inertial Frame
• Inertial Frame of Reference: is a frame in a state of constant, rectilinear motion with respect to one another: an accelerometer at rest in one would detect zero acceleration;
• Newton’s First Law of Inertia: an observer in a inertial frame of reference observes a body: inertia is the natural tendency of that body to remain immobile or in motion with constant speed along a straight line;
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Inertia and Inertial Frame
• Newton’s Second Law: A force will accelerate a body, in the direction of the force at a rate inversely proportional to the mass of the body;
• Mass is the linear quantification of inertia; • The laws of Classical Mechanics
(Biomechanics included) are valid and maintain the same form in all inertial reference systems.
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What is a sensor?
• Instrument capable to transduce a physical quantity to a measurable electric signal;
• Accuracy vs Precision;
• Inertial sensor: functioning principle based on inertial phenomena.
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Inertial Sensors
• Accelerometers: sense linear acceleration [m/s^2] along a specific axis;
• Gyroscopes: sense angular velocity axis, measured in [rad/s];
• Magnetometer: sense the strength of a magnetic field, measured in [mGauss].
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Inertial Sensor Benefits and Applications
• Low cost; • Small size, Portable;• Ultra Low-power systems;• Wireless.
• Ambulatory monitoring;• Unsupervised monitoring;• Fall & Gait;• Activity detection.6/16/2012
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2.STATIC CALIBRATION EXPERIMENT
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2.1 Brief Hardware Description
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2.2 Static Noise Evaluation
2.2.1 Description
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INERTIAL MEASUREMENT UNITS
XSENS SENSOR(with cables) OPAL SENSOR(wireless)
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2.2.2 Evaluate and characterize the noise in terms of mean and standard
deviation of the ouputs
• Mean() function• Std() function
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The results for XSENS IMU are as follows:
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The results for OPAL IMU are as follows:
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2.3 Evaluate the drift effect
• Detrend() function• Polyfit() function, y=mx+b
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The results for XSENS IMU are as follows:
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The results for OPAL IMU are as follows:
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2.4 What are the main difference between the noises on each sensor?
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Ay vs Ay1
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From these plots we can conclude that:
• The Xsens IMU, has overall better performance with respect to the Opal IMU;
• The Xsens trend of noise drift is almost parallel to the time axis and the signals have lower offsets with respect to the Opal signals.
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2.5 Does the standard deviation of the noise correspond to that reported in the data sheet?
• Xsens: As we can see in the tables above, the data reported in the
datasheet and our measured ones, differ from a factor of ±.001; So we
obtain very good measurements in terms of accuracy and precision;
• Opal: In this case we have to convert the data from [μg/»Hz] to [m/s2]
for the linear acceleration Noise and from [°/s/»Hz] to [rad/s] for the
angular velocity, using the bandwidth data B = 50[Hz]. Also in this case
we obtain good measurement in terms of accuracy and precision.
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3. Sit to Stand
• Opal IMU1 placed on the Thigh, in lateral position;
• Opal IMU2 placed on the Trunk, at L5 height;
• 4 trials with 5 repetitions at different speed;
• f_{sample} = 128[Hz];
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Sit to Stand
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Extracted Signals
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Digital Filtering
2sample
cutn f
fW
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Normalized CutOff Frequency
Because of Noisy signals: Lowpass Filtering needed
[b,a] = butter(order,Wn,type): extract the coefficients;
filtfilt(b,a,input): No Phase Shift, forward + backward filtering.
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Algorithm
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Results
LPF PulsesDetection
Edges Detection
Integration
Validation
Good/Bad
Knee Angles
Timings
Acc(x,y)
Gyro(z)
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Results: Plots
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Thigh Accelerometer x and y axis Thigh Gyroscope z axis
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Results: Table
StS Time mean [s]
TtS Time mean [s]
StS Angle mean [°]
TtS Angle mean [°]
Trial 1 1.7984 1.4375 94.7484° - 90.1806°
Trial 2 1.391 1.1719 96.3518° - 92.9096°
Trial 3 1.4672 1.3531 75.5568° - 71-7260°
Trial 4 .9906 .09562 71.6656° - 69.1158°
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4 Trials 5 Repetitions StS = Sit to Stand Task TtS = Time to Sit Task
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Sit to Stand Conclusions
+ Results achievable with only 1 IMU (on the thigh)
+ Robust algorithm
• Kalman fusion filter to improve the algorithm
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