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

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ÉCOLE DES SCIENCES DE LACTIVITÉ PHYSIQUE SCHOOL 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.

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ÉCOLE DES SCIENCES DE LACTIVITÉ PHYSIQUE SCHOOL OF HUMAN KINETICS The Goal !!! Accuracy in Data Acquisition Good Decision Objectivity in Data Interpretation

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ÉCOLE DES SCIENCES DE LACTIVITÉ PHYSIQUE SCHOOL 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

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ÉCOLE DES SCIENCES DE LACTIVITÉ PHYSIQUE SCHOOL 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.)

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ÉCOLE DES SCIENCES DE LACTIVITÉ PHYSIQUE SCHOOL OF HUMAN KINETICS Factors that Maximize Accuracy in Data Acquisition Attention to the Details Good Decisions

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ÉCOLE DES SCIENCES DE LACTIVITÉ PHYSIQUE SCHOOL OF HUMAN KINETICS Biomechanical Data Whats it like? Continuous Wide range of Amplitudes Variability of Duration Wide range of Frequencies

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ÉCOLE DES SCIENCES DE LACTIVITÉ PHYSIQUE SCHOOL OF HUMAN KINETICS Data are ……Continuous ROM EMG

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ÉCOLE DES SCIENCES DE LACTIVITÉ PHYSIQUE SCHOOL OF HUMAN KINETICS Wide Range of Amplitudes Ground Reaction Forces – Hundreds of Newton EMG – Millionths of a volt

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ÉCOLE DES SCIENCES DE LACTIVITÉ PHYSIQUE SCHOOL OF HUMAN KINETICS Wide Range of Amplitudes MeasurementRangeFrequency, HzMethod Blood flow1 to 300 mL/s0 to 20Electromagnetic or ultrasonic Blood pressure0 to 400 mmHg0 to 50Cuff or strain gage Cardiac output4 to 25 L/min0 to 20Fick, dye dilution Electrocardiography0.5 to 4 mV0.05 to 150Skin electrodes Electroencephalography5 to 300 V0.5 to 150Scalp electrodes Electromyography0.1 to 5 mV0 to 10000Needle electrodes Electroretinography0 to 900 V0 to 50Contact lens electrodes pH3 to 13 pH units0 to 1pH electrode pCO 2 40 to 100 mmHg0 to 2pCO 2 electrode pO2pO2 30 to 100 mmHg0 to 2pO 2 electrode Pneumotachography0 to 600 L/min0 to 40Pneumotachometer Respiratory rate2 to 50 breaths/min0.1 to 10Impedance Temperature32 to 40 °C0 to 0.1Thermistor

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ÉCOLE DES SCIENCES DE LACTIVITÉ PHYSIQUE SCHOOL OF HUMAN KINETICS Wide Variability of Duration Continuous Motion Studies - hours Reaction Time Studies - msec

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ÉCOLE DES SCIENCES DE LACTIVITÉ PHYSIQUE SCHOOL OF HUMAN KINETICS Wide Range of Frequencies ROM in Walking – 2 to 4 Hz Foot Impact Shock – 200 to 300 Hz EMG – > 2000 Hz

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ÉCOLE DES SCIENCES DE LACTIVITÉ PHYSIQUE SCHOOL OF HUMAN KINETICS How Do We Acquire Biomechanical Data?? Video/Cine Force Plates Electromyography Pressure Plates Accelerometers Force Transducers Electrogoniometers Etc.

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ÉCOLE DES SCIENCES DE LACTIVITÉ PHYSIQUE SCHOOL OF HUMAN KINETICS How do we Record the Data?? Old technology (yuk) Chart Recorders Oscilloscopes Tape Recorders New Technology Computers Data loggers

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ÉCOLE DES SCIENCES DE LACTIVITÉ PHYSIQUE SCHOOL OF HUMAN KINETICS The Problem !!! Instruments produce continuous data (Analog Data) Computers like discrete data (Digital Data)

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ÉCOLE DES SCIENCES DE LACTIVITÉ PHYSIQUE SCHOOL OF HUMAN KINETICS The Problems!!! (a) An input signal which exceeds the dynamic range. (b) The resulting amplified signal is saturated at 1 V.

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ÉCOLE DES SCIENCES DE LACTIVITÉ PHYSIQUE SCHOOL OF HUMAN KINETICS The problems!!! (a) An input signal without dc offset. (b) An input signal with dc offset.

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ÉCOLE DES SCIENCES DE LACTIVITÉ PHYSIQUE SCHOOL OF HUMAN KINETICS The Solution The Analog to Digital (A/D) Converter Changes the in-coming (analog) signal to (digital) information that can be processed by the computer

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ÉCOLE DES SCIENCES DE LACTIVITÉ PHYSIQUE SCHOOL 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

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ÉCOLE DES SCIENCES DE LACTIVITÉ PHYSIQUE SCHOOL 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

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ÉCOLE DES SCIENCES DE LACTIVITÉ PHYSIQUE SCHOOL OF HUMAN KINETICS Data Sampling and Data Treatment

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ÉCOLE DES SCIENCES DE LACTIVITÉ PHYSIQUE SCHOOL 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

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ÉCOLE DES SCIENCES DE LACTIVITÉ PHYSIQUE SCHOOL OF HUMAN KINETICS The Analog to Digital (A/D) Converter Analog Signals ROM GRF EMG

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ÉCOLE DES SCIENCES DE LACTIVITÉ PHYSIQUE SCHOOL 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

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ÉCOLE DES SCIENCES DE LACTIVITÉ PHYSIQUE SCHOOL 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

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ÉCOLE DES SCIENCES DE LACTIVITÉ PHYSIQUE SCHOOL 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 units D/A Capacity

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ÉCOLE DES SCIENCES DE LACTIVITÉ PHYSIQUE SCHOOL OF HUMAN KINETICS Time and Frequency Domain Analysis Time Domain Frequency Domain Time (seconds) Frequency (hz) Mv

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ÉCOLE DES SCIENCES DE LACTIVITÉ PHYSIQUE SCHOOL 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

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ÉCOLE DES SCIENCES DE LACTIVITÉ PHYSIQUE SCHOOL OF HUMAN KINETICS Frequency Domain Analysis Examples

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ÉCOLE DES SCIENCES DE LACTIVITÉ PHYSIQUE SCHOOL OF HUMAN KINETICS Determining Optimal Sampling Rates How Fast Do We Need to Sample the Data ? The 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

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ÉCOLE DES SCIENCES DE LACTIVITÉ PHYSIQUE SCHOOL 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 -

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ÉCOLE DES SCIENCES DE LACTIVITÉ PHYSIQUE SCHOOL 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: Whats needed Computer Processing Speed Amount of Data Requisite Processing

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ÉCOLE DES SCIENCES DE LACTIVITÉ PHYSIQUE SCHOOL 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

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ÉCOLE DES SCIENCES DE LACTIVITÉ PHYSIQUE SCHOOL OF HUMAN KINETICS Typical Sampling Rates for Biomechanical Data Force Platform - 10 Hz (balance) to 1000 Hz (running, jumping, etc.) EMG Hz to 2000 Hz Video - 15 fps to 500 fps

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ÉCOLE DES SCIENCES DE LACTIVITÉ PHYSIQUE SCHOOL 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!!!

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ÉCOLE DES SCIENCES DE LACTIVITÉ PHYSIQUE SCHOOL OF HUMAN KINETICS Determining Optimal Sampling Rates What Happens if we…… Sample too slow – Aliasing Error (introduces frequencies into the data that arent actually there Sample Too Fast – Generates excess data

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ÉCOLE DES SCIENCES DE LACTIVITÉ PHYSIQUE SCHOOL OF HUMAN KINETICS Sampling Rate Examples

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ÉCOLE DES SCIENCES DE LACTIVITÉ PHYSIQUE SCHOOL OF HUMAN KINETICS Prevention and Treatment of Noisy Data A BIG Problem!! !

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ÉCOLE DES SCIENCES DE LACTIVITÉ PHYSIQUE SCHOOL OF HUMAN KINETICS Noisy Data Noisy EMG Signal Not Noisy (clean)

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ÉCOLE DES SCIENCES DE LACTIVITÉ PHYSIQUE SCHOOL 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

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ÉCOLE DES SCIENCES DE LACTIVITÉ PHYSIQUE SCHOOL OF HUMAN KINETICS Filtering Data The Goal Extracting the Noise without Changing the Signal

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ÉCOLE DES SCIENCES DE LACTIVITÉ PHYSIQUE SCHOOL OF HUMAN KINETICS Digitial Filtering Based on the Frequency characteristics of the data A mathematical process that selectively eliminates that part of the data that is caused by noise Based on the assumption that the noise occurs at frequencies that are different from those of the actual signal

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ÉCOLE DES SCIENCES DE LACTIVITÉ PHYSIQUE SCHOOL OF HUMAN KINETICS Digital Filtering Raw Signal – (signal + noise) Filtered Signal

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ÉCOLE DES SCIENCES DE LACTIVITÉ PHYSIQUE SCHOOL OF HUMAN KINETICS Digital Filtering Examples

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ÉCOLE DES SCIENCES DE LACTIVITÉ PHYSIQUE SCHOOL OF HUMAN KINETICS Noise Reduction Other 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

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ÉCOLE DES SCIENCES DE LACTIVITÉ PHYSIQUE SCHOOL OF HUMAN KINETICS Noise Reduction - other Examples

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ÉCOLE DES SCIENCES DE LACTIVITÉ PHYSIQUE SCHOOL 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 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

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ÉCOLE DES SCIENCES DE LACTIVITÉ PHYSIQUE SCHOOL 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

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ÉCOLE DES SCIENCES DE LACTIVITÉ PHYSIQUE SCHOOL OF HUMAN KINETICS Data Normalization Other types of data normalization – Normalizing time by the duration of a cycle Ex. Expressing gait events relative to a gait cycle – ie. 20% of the gait cycle Normalizing O 2 consumption by expressing it 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 THE END

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