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ÉCOLE DES SCIENCES DE L’ACTIVITÉ PHYSIQUESCHOOL OF HUMAN KINETICS
Biomechanical Instrumentation
Considerations in Data Acquisition
ÉCOLE DES SCIENCES DE L’ACTIVITÉ PHYSIQUESCHOOL OF HUMAN KINETICS
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.
ÉCOLE DES SCIENCES DE L’ACTIVITÉ PHYSIQUESCHOOL OF HUMAN KINETICS
The Goal !!!
Accuracy in Data Acquisition
Good Decision
Objectivity in Data InterpretationObjectivity in Data Interpretation
ÉCOLE DES SCIENCES DE L’ACTIVITÉ PHYSIQUESCHOOL OF HUMAN KINETICS
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
ÉCOLE DES SCIENCES DE L’ACTIVITÉ PHYSIQUESCHOOL OF HUMAN KINETICS
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.)
ÉCOLE DES SCIENCES DE L’ACTIVITÉ PHYSIQUESCHOOL OF HUMAN KINETICS
Factors that Maximize Accuracy in Data
Acquisition
Attention to the Details
Good Decisions
ÉCOLE DES SCIENCES DE L’ACTIVITÉ PHYSIQUESCHOOL OF HUMAN KINETICS
Biomechanical DataWhat’s it like?
Continuous Wide range of Amplitudes Variability of Duration Wide range of Frequencies
ÉCOLE DES SCIENCES DE L’ACTIVITÉ PHYSIQUESCHOOL OF HUMAN KINETICS
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
ÉCOLE DES SCIENCES DE L’ACTIVITÉ PHYSIQUESCHOOL OF HUMAN KINETICS
Wide Range of Amplitudes
Ground Reaction Forces – Hundreds of Newton
EMG – Millionths of a volt
ÉCOLE DES SCIENCES DE L’ACTIVITÉ PHYSIQUESCHOOL OF HUMAN KINETICS
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
ÉCOLE DES SCIENCES DE L’ACTIVITÉ PHYSIQUESCHOOL OF HUMAN KINETICS
Wide Variability of Duration
Continuous Motion Studies - hours
Reaction Time Studies - msec
ÉCOLE DES SCIENCES DE L’ACTIVITÉ PHYSIQUESCHOOL OF HUMAN KINETICS
Wide Range of Frequencies
ROM in Walking – 2 to 4 Hz Foot Impact Shock – 200 to
300 Hz EMG – > 2000 Hz
ÉCOLE DES SCIENCES DE L’ACTIVITÉ PHYSIQUESCHOOL OF HUMAN KINETICS
How Do We Acquire Biomechanical Data??
Video/Cine Force Plates Electromyography Pressure Plates Accelerometers Force Transducers Electrogoniometers Etc.
ÉCOLE DES SCIENCES DE L’ACTIVITÉ PHYSIQUESCHOOL OF HUMAN KINETICS
How do we Record the Data??
Old technology (yuk)• Chart Recorders • Oscilloscopes • Tape Recorders
New Technology• Computers• Data loggers
ÉCOLE DES SCIENCES DE L’ACTIVITÉ PHYSIQUESCHOOL OF HUMAN KINETICS
The Problem !!!
Instruments produce continuous data (Analog Data)Computers like discrete data(Digital Data)
ÉCOLE DES SCIENCES DE L’ACTIVITÉ PHYSIQUESCHOOL OF HUMAN KINETICS
The Problems!!!
(a) An input signal which exceeds the dynamic range. (b) The resulting amplified signal is saturated at 1 V.
ÉCOLE DES SCIENCES DE L’ACTIVITÉ PHYSIQUESCHOOL OF HUMAN KINETICS
The problems!!!
Time
Amplitude
Dc offset
(a) An input signal without dc offset. (b) An input signal with dc offset.
ÉCOLE DES SCIENCES DE L’ACTIVITÉ PHYSIQUESCHOOL OF HUMAN KINETICS
The SolutionThe Analog to Digital (A/D)
Converter
Changes the in-coming (analog) signal to (digital) information that can be processed by the computer
ÉCOLE DES SCIENCES DE L’ACTIVITÉ PHYSIQUESCHOOL OF HUMAN KINETICS
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
ÉCOLE DES SCIENCES DE L’ACTIVITÉ PHYSIQUESCHOOL OF HUMAN KINETICS
Principles of A/D Conversion
The Analog values that represent the signal are then stored, as an array of numbers, for processing.
02344321
ÉCOLE DES SCIENCES DE L’ACTIVITÉ PHYSIQUESCHOOL OF HUMAN KINETICS
Data Sampling and Data Treatment
ÉCOLE DES SCIENCES DE L’ACTIVITÉ PHYSIQUESCHOOL OF HUMAN KINETICS
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
ÉCOLE DES SCIENCES DE L’ACTIVITÉ PHYSIQUESCHOOL OF HUMAN KINETICS
The Analog to Digital (A/D) ConverterAnalog Signals
ROM
GRF
EMG
ÉCOLE DES SCIENCES DE L’ACTIVITÉ PHYSIQUESCHOOL OF HUMAN KINETICS
The Analog to Digital (A/D) Converter
Changes the in-coming analog signal to digits (numerical information) that can be processed by the computer
ÉCOLE DES SCIENCES DE L’ACTIVITÉ PHYSIQUESCHOOL OF HUMAN KINETICS
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
ÉCOLE DES SCIENCES DE L’ACTIVITÉ PHYSIQUESCHOOL OF HUMAN KINETICS
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
ÉCOLE DES SCIENCES DE L’ACTIVITÉ PHYSIQUESCHOOL OF HUMAN KINETICS
Time and Frequency Domain Analysis
Time Domain
Frequency Domain
Time (seconds)
Frequency (hz)
Mv
Mv
ÉCOLE DES SCIENCES DE L’ACTIVITÉ PHYSIQUESCHOOL OF HUMAN KINETICS
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
ÉCOLE DES SCIENCES DE L’ACTIVITÉ PHYSIQUESCHOOL OF HUMAN KINETICS
Frequency Domain Analysis
Examples
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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
ÉCOLE DES SCIENCES DE L’ACTIVITÉ PHYSIQUESCHOOL OF HUMAN KINETICS
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 -
ÉCOLE DES SCIENCES DE L’ACTIVITÉ PHYSIQUESCHOOL OF HUMAN KINETICS
Considerations in Selecting a Sampling Rate
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
ÉCOLE DES SCIENCES DE L’ACTIVITÉ PHYSIQUESCHOOL OF HUMAN KINETICS
Considerations in Selecting a Sampling Rate
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
ÉCOLE DES SCIENCES DE L’ACTIVITÉ PHYSIQUESCHOOL OF HUMAN KINETICS
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
ÉCOLE DES SCIENCES DE L’ACTIVITÉ PHYSIQUESCHOOL OF HUMAN KINETICS
Determining Optimal Sampling Rates
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!!!
ÉCOLE DES SCIENCES DE L’ACTIVITÉ PHYSIQUESCHOOL OF HUMAN KINETICS
Determining Optimal Sampling Rates
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
ÉCOLE DES SCIENCES DE L’ACTIVITÉ PHYSIQUESCHOOL OF HUMAN KINETICS
Sampling Rate
Examples
ÉCOLE DES SCIENCES DE L’ACTIVITÉ PHYSIQUESCHOOL OF HUMAN KINETICS
Prevention and Treatment of
Noisy Data A BIG Problem!!!
ÉCOLE DES SCIENCES DE L’ACTIVITÉ PHYSIQUESCHOOL OF HUMAN KINETICS
Noisy Data
Noisy EMG Signal
Not Noisy (clean)
ÉCOLE DES SCIENCES DE L’ACTIVITÉ PHYSIQUESCHOOL OF HUMAN KINETICS
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
ÉCOLE DES SCIENCES DE L’ACTIVITÉ PHYSIQUESCHOOL OF HUMAN KINETICS
Filtering DataThe Goal
Extracting the Noise without Changing the
Signal
ÉCOLE DES SCIENCES DE L’ACTIVITÉ PHYSIQUESCHOOL OF HUMAN KINETICS
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
ÉCOLE DES SCIENCES DE L’ACTIVITÉ PHYSIQUESCHOOL OF HUMAN KINETICS
Digital Filtering
Raw Signal – (signal + noise)
Filtered Signal
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Digital Filtering
Examples
ÉCOLE DES SCIENCES DE L’ACTIVITÉ PHYSIQUESCHOOL OF HUMAN KINETICS
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
ÉCOLE DES SCIENCES DE L’ACTIVITÉ PHYSIQUESCHOOL OF HUMAN KINETICS
Noise Reduction - other
Examples
ÉCOLE DES SCIENCES DE L’ACTIVITÉ PHYSIQUESCHOOL OF HUMAN KINETICS
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”
ÉCOLE DES SCIENCES DE L’ACTIVITÉ PHYSIQUESCHOOL OF HUMAN KINETICS
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”
ÉCOLE DES SCIENCES DE L’ACTIVITÉ PHYSIQUESCHOOL OF HUMAN KINETICS
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
ÉCOLE DES SCIENCES DE L’ACTIVITÉ PHYSIQUESCHOOL OF HUMAN KINETICS
THE END