Biomedical Transducers - Inertial Sensors 1

Biomedical Transducers a.a. 2011/12

Inertial SensorsDaniele AntonioliLuca Faggianelli

Jian HanMekki Mtimet

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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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