ÉCOLE DES SCIENCES DE LACTIVITÉ PHYSIQUE SCHOOL OF HUMAN KINETICS Biomechanical Instrumentation Considerations in Data Acquisition

ÉCOLE DES SCIENCES DE L’ACTIVITÉ PHYSIQUESCHOOL OF HUMAN KINETICS

Biomechanical Instrumentation

Considerations in Data Acquisition


Data Acquisition in Biomechanics

Why???

Describe and Understand a Phenomena Test a Theory Evaluate a condition/situation

Data Acquisition provides information that is used in making decisions.


The Goal !!!

Accuracy in Data Acquisition

Good Decision

Objectivity in Data InterpretationObjectivity in Data Interpretation


Levels of Data Acquisition

Visual Observation and Human Interpretation• Limited Information Processing Capacity• Subjectivity in interpretation

Instrumented Observation and Human Interpretation• Un-limited information processing capacity• Decreased subjectivity of interpretation

Instrumented Observation and Interpretation• Un-limited information processing capacity

and Objectivity of interpretation* Lack of spontaneity and creativity


Factors that Maximize Accuracy in Data Acquisition

Selection of the correct measurement technique• Use of established techniques

Attention to appropriate sensitivity levels

Calibration Standardization of protocols Adequate preparation (ie

training, pilot testing, etc.)


Factors that Maximize Accuracy in Data

Acquisition

Attention to the Details

Good Decisions


Biomechanical DataWhat’s it like?

Continuous Wide range of Amplitudes Variability of Duration Wide range of Frequencies


Data are ……Continuous

ROM

EMG

0

100

200

300

400

500

600

EMG (uV)

0

20

40

60

80

100

0 10 20 30 40 50 60 70 80 90 100

Gait Cycle

Degrees


Wide Range of Amplitudes

Ground Reaction Forces – Hundreds of Newton

EMG – Millionths of a volt


Wide Range of AmplitudesMeasurement Range

Frequency, Hz

Method

Blood flow 1 to 300 mL/s 0 to 20 Electromagnetic or ultrasonic

Blood pressure 0 to 400 mmHg 0 to 50 Cuff or strain gage

Cardiac output 4 to 25 L/min 0 to 20 Fick, dye dilution

Electrocardiography 0.5 to 4 mV 0.05 to 150 Skin electrodes

Electroencephalography

5 to 300 V 0.5 to 150 Scalp electrodes

Electromyography 0.1 to 5 mV 0 to 10000 Needle electrodes

Electroretinography 0 to 900 V 0 to 50 Contact lens electrodes

pH 3 to 13 pH units 0 to 1 pH electrode

pCO2 40 to 100 mmHg 0 to 2 pCO2 electrode

pO2 30 to 100 mmHg 0 to 2 pO2 electrode

Pneumotachography 0 to 600 L/min 0 to 40 Pneumotachometer

Respiratory rate2 to 50 breaths/min

0.1 to 10 Impedance

Temperature 32 to 40 °C 0 to 0.1 Thermistor


Wide Variability of Duration

Continuous Motion Studies - hours

Reaction Time Studies - msec


Wide Range of Frequencies

ROM in Walking – 2 to 4 Hz Foot Impact Shock – 200 to

300 Hz EMG – > 2000 Hz


How Do We Acquire Biomechanical Data??

Video/Cine Force Plates Electromyography Pressure Plates Accelerometers Force Transducers Electrogoniometers Etc.


How do we Record the Data??

Old technology (yuk)• Chart Recorders • Oscilloscopes • Tape Recorders

New Technology• Computers• Data loggers


The Problem !!!

Instruments produce continuous data (Analog Data)Computers like discrete data(Digital Data)


The Problems!!!

(a) An input signal which exceeds the dynamic range. (b) The resulting amplified signal is saturated at 1 V.


The problems!!!

Time

Amplitude

Dc offset

(a) An input signal without dc offset. (b) An input signal with dc offset.


The SolutionThe Analog to Digital (A/D)

Converter

Changes the in-coming (analog) signal to (digital) information that can be processed by the computer


Principles of A/D Conversion

An analog signal (typically a voltage) is measured at periodic intervals. At each interval the voltage is given a numerical value that represents the amplitude of

the voltage. 0 2 3 4 4 3 2 1



The Analog values that represent the signal are then stored, as an array of numbers, for processing.

02344321


Data Sampling and Data Treatment


Data Sampling and Data Treatment

Issues

Transferring Analog Signals to a Digital Computer

Time and Frequency Domain Analysis Determining Optimal Sampling Rates Prevention and Treatment of Noisy Data Data Normalization


The Analog to Digital (A/D) ConverterAnalog Signals

ROM

GRF

EMG


The Analog to Digital (A/D) Converter

Changes the in-coming analog signal to digits (numerical information) that can be processed by the computer



An analog signal (typically a voltage) is measured at periodic intervals. At each interval the voltage is given a numerical value that represents the amplitude of

the voltage. 0 2 3 4 4 3 2 1


Features of the A/D Converter

Channels - 4, 8 16, 32, 64 Gain - 2, 4 8, 10 (typical) Input Range - variable (+-10

Volts) Sampling Rate

• Low 1000 Hz to High 500 kHz

Resolution• 8 Bit 256 units• 12 Bit 4096 units• 16 Bit 65536 units

D/A Capacity


Time and Frequency Domain Analysis

Time Domain

Frequency Domain

Time (seconds)

Frequency (hz)

Mv

Mv


Time and Frequency Domain Analysis

Time Domain – Represents change in signal Amplitude relative to change in Time

Frequency Domain – Represents change in signal Amplitude relative to the Rate of Change in Amplitude

Time Domain Fourier Transform (FFT)Frequency Domain


Frequency Domain Analysis

Examples


Determining Optimal Sampling Rates

How Fast Do We Need to Sample the Data ?

The Real IssueThe Real Issue

Sampling Rate: The rate at which periodic measurements of a signal are made. Units are samples per second or Hz

Examples – An EMG signal being sampled at 1000 Hz A video picture being sampled at 60 Hz


Considerations in Selecting a Sampling Rate

Frequency Characteristics of the Signal – the rate at which the amplitude of the signal changes

Examples:

Rapidly Changing Signals –

Slowly Changing Signals -



Frequency Characteristics of the Signal - the Nyquist Sampling Theory

Speed of Signal Processing and Data Analysis

Depends on:

What’s neededComputer Processing SpeedAmount of DataRequisite Processing



Frequency Characteristics of the Signal - the Nyquist Sampling Theory

Speed of Signal Processing and Data Analysis

Storage Capacity of the System Number of Channels Simultaneously

Sampled Capacity (speed and channels) of A/D

system


Typical Sampling Rates for Biomechanical Data

Force Platform - 10 Hz (balance) to 1000 Hz (running, jumping, etc.)

EMG - 100 Hz to 2000 Hz Video - 15 fps to 500 fps



Theoretical –Determine the frequency characteristics of the signal to be sampled – Apply the Nyquist Theory ( i.e. At least 2 x the highest frequency in the signal)

Practical – Copy what someone else has done!!!



What Happens if we……

Sample too slow – Aliasing Error (introduces frequencies into the data that aren’t actually there

Sample Too Fast – Generates excess data


Sampling Rate

Examples


Prevention and Treatment of

Noisy Data A BIG Problem!!!


Noisy Data

Noisy EMG Signal

Not Noisy (clean)


Minimizing the Effect of Noisy Data

Control sources of noise before contamination – eliminate sources of noise• Vibration• Radiant electrical energy• Movement artifact (cable movement)

Filter data after contamination – with appropriate hardware and/or software filters


Filtering DataThe Goal

Extracting the Noise without Changing the

Signal


Digitial FilteringBased on the Frequency characteristics of the dataA mathematical process that selectively eliminates that part of the data that is caused by noiseBased on the assumption that the noise occurs at frequencies that are different from those of the actual signal


Digital Filtering

Raw Signal – (signal + noise)

Filtered Signal


Digital Filtering

Examples


Noise ReductionOther Techniques

Smoothing – Moving Window

Curve Fitting – Cubic Spline

Root Mean Square

*All of the above are effective – but less specific*May also be used to simplify complex waveforms to enhance analysis


Noise Reduction - other

Examples


Data NormalizationThe Goal – To convert the data from one base unit to an

alternative base unit

1. To enhance ease of interpretation

2. To establish a common base so that averaging across subjects/conditions is possible

Examples

•“The mean level of muscle activity in the biceps during the arm curl was 80 mv.”

•“The mean level of muscle activity in the biceps during the arm curl was 98% of a maximum voluntary contraction”


Data Normalization

The Goal – To convert the data from one base unit to an alternative base unit

1. To enhance ease of interpretation

2. To establish a common base so that averaging across subjects/conditions is possible

Examples

•“The force on impact with the ground was equal to 1100 Newtons”

•“The force on impact with the ground was equal to 1.5 bodyweights”


Data Normalization

Other types of data normalization –

• Normalizing time by the duration of a cycleEx. Expressing gait events relative to a

gait cycle – ie. 20% of the gait cycle

• Normalizing O2 consumption by expressingit as a function of body mass and/or time

Ex. Ml/Kg/Min


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ÉCOLE DES SCIENCES DE LACTIVITÉ PHYSIQUE SCHOOL OF HUMAN KINETICS Biomechanical Instrumentation Considerations in Data Acquisition