Test 2 Flashcards
Wearable technology vs wearable sensors
wearbale tech: any technology that you can wear
wearable sensors: often synonymous with the term wearable technology. Measruing some pysical quantity
wearable sensor components
physcial quality: movement, force, light, temp, chemicals, ect
(measured using)
Sensors: intertial sensors, force sensors, light sensors, thermocouplors, chemical sensors
electrica output: change in voltage calibrated to change in the physical quantity
measuing movement- inertial sensors
utilize the principle of inertia
huge variety of applications:
-drones and wehcles (naviagtion, stability, control, impacts)
-smart phones and electronics (orientation, position, motion)
-industry measurements (vibration related to machinery and pipeline “health”)
-Human movement (ie smart watch)
inertial sensors for human movement
many potential applications
-activiy trackers
-gait analysis
-balance assessment
-fall detection
-sleep analysis
-sport performance
+
tools to caputre movement- need to consider
what movment constructs (and metrics) you are interested in?
movement constructs
-pysical activity
-mobility
-gait
movement metrics
give insight into constructs
(physical activity:) step counts, activity tracking, activirty intensity, ect
(mobility:) timing functional tasks, turining velocity, balance, ect
(gait:) gait speed, stride time, impact acceleration, knee flexion angle
What are the two primary types of inertial sensors
-Accelerometer (ACCEL)
-Gyroscope (GYRO)
Integrated together as an “internal measurement unit” or IMU
often combined with
-Magnometer (MAG)
-Microcontroller unit (MCU)
magnetometer (MAG)
electric compas (heading vector)
IMU
microcontroller unit (MCU)
brings info together and sends out for use
IMU
Accelerometers
measures linear accelerations (m/s or g’s)
-displacement, velocity, accleration
we feel acccleration
utilize the principle of inertia
newton’s laws of motion
first law: objects reamins constant at rest (orconstant velocity) unless acted on by a force
second law: F=ma - how accelerometers work)
third law: equal and opposite reation
How do acclerometers work
use newtons second law
prof mass is known
force= force that can be measured
free falling- reading=0
stationary acclerometer reading= 1g
prof masses aligned orthogonally to measure accleration in 3D - can measure in all directions
powerin-power out
physical quantity–> sensor–> electrical output (sensors always give)
measuring a change in voltage (requiring calibration to interpret as g’s)- multiply by a conversion factor
converting voltage to G’s
Align each axis with +1 and -1g to determine the calibration factor
example:
change in g’s from 1-2= 2g change in mV from 1-2= 4000mV
change of 2g/ change of 4000mV
Important accelerometer properties- Range
Min/Max values that can be accurately measured
-change of accelerations that can be measured (+/-g)- some designed to measure vith big or small changes
-+/-g (red) vs +/-8g (blue): Acclerometer of dorsum of foot during walking – continue measure full range (cliped)
important acclerometer properties- different areas of the body
Walking:
head >1g
body 1g
foot 2-4gs
running
head 1-2g (want to keep head and vesibular system steady- body dampens as go up the vhain)
body 2-4g
foot 10+gs (nned a bigger g sensor to prevent clipping)
important acclerometer properties- sensitivity
sensitivty: how musch the output changes in relation to the input
-how well you can measure changes in movement (inversly realted to range)– higher g’s= lower sensitivity ‘ ideal to have 2 sensors to be able to get best of both worlds
important accelerometer properties- linerarity
linearity: the output does not follow a straight line with input
-change in voltage is not linearly proportional to change in accleration
-usually more common near end-range
-try to stay with away from end-ranges
Tips when selecting an acclerometer
select a sensor (or settinfs within a unit) that offers the greatest sensitivity within a safe range for you movement/placement
also depends on your variable of interest (eg impact vs orientation)
always pitlot test and examine the data
general recommenations when selcting an accelerometer
+/- 2g- lower back during walking/activities of daily living
+/- 8g- lower limbs during walking/activies of daily living
+/-16-32g- lower limbs during running
+/- 100-200g- high impact activities (ballistic movements, falls, concussions)
Gyroscopes
-same principle as acclerometer but for rotation
Measure angular velocity (w)
-w= 0/t
-radians/second (or degrees/second)
-Linear velocity is proportional to the distance from the axis (v=wr)
-Angular velocity is independent of distance from the axis
note on magnometers-hall effect
basically, an electronic compass
magnometers work based on the hall effect
-voltage produced across an electrical conductor due to a perpendicular magnetic field
-uses earth’s magnetic field to measure heading
-outputs can be significantly distorted by other magnetic feilds, eletrical signals or devices.
Linear acclerations and angular velocity outputs are often used for:
-assessing overall amount of movment or type of activity
-determine timing of gait events
-segment motion, pelvic stability, or between limb asymmetry
-impact magnitudes (ACCEL- walking, running, concussions,ect)
also intergerated to linear and angular displacement to mesure: location or positon, segment or joint angles (kinesmatics)
Using acclerometers as tilit sensors
acclerometers can provide an excellent estimate of orientation
-uses gravity and simple trig
-works well when there is little to no movement
computation:
sin(0)= opp/hyp
sampling frequency (sampling rate)
the number of samples (measurements) taken in 1 second
-1 Hz= 1 sample per second; 100Hz= 100 samples per second
Depends on what you want to measure
-lower sampling frequency (ie <50Hz) can work for activity monitors
0higher sampling frequencies are foten needed
Sampling to storage- sampling rate
sampling rate: number of data points collected per second
data sotrage: higher frequencies= more data stoarge and battery usage
physical ativity (30Hz; range 1-100); Gait (100Hz; range: 60-200+)
storage examples
accelerometer and gyroscope at 100Hz: 7 days
Acclerometer at 100Hz: 17 days
Acclerometer at 50 Hz: 34 days
EACh ~1Gb
Nyquist sampling theorem
sampling should occur >2 times the frequency of the signal of interst- to get represnetaton of signal (although wont ve best unrless increase the sampling rate)
Dynamic system response
previous properties (eg sensitivity, ect) are more related to a static response
How sensors react to dynamic changes in input is tied to:
1) the type of input: step, ramp, impulse, sinusoidal
2) the type of mechanical system:
-Zero, first, second order
-n+1 coefficients characterize the system
Dynamic system response input types:
these relate to changes in the input, where we can measure the time response of the systems (ie how quickly the system responds)
step input, ramp input, impulse input, sinusoidal input
Dynamic system response input types: step input
sudden change in input that is held constant
ie
Dynamic system response input types: ramp input
linear increase in input
ie
Dynamic system response input types: impulse input
sudden spike in input
ie
Dynamic system response input types: sinusoidal input
sine wave input
ie
Zero order systmes
The output at a zero order system is proportional (1:1) to the input.
-depends only on displacement and 1 constant K
F(t)= k x(t)
example: potentiometer- ie dimmer switch
First order systems
The output of a first order system depends on differntial equation
-Depends only on displacement (x) and velocity (x’) and 2 constants (c and K)
f(t)= c x’(t) + k x(t)
c= damping, viscosity, friction coefficeint
experiences a time delay between input and output
second order systems
the output of a second order system depends on differential equation
-Depends on displacement (x), velocity (x’), and acceleration (x’’) and 3 constants (m-mass,c and k)
F(t)= m x’‘(t) + c x’(t) + k x(t)
involves oscillations related to: natrual frequency, damping ratio
how system responds relaes to the relationship between the 3 constants
second order systems: natrual frequency
frequency the system would oscillate if there was no damping: wn (omega- dependent on mass and spring)
wn= squrt(k/m)
increase k- increase natural frequency (tigher= faster oscilation)
increase m- decrease frequency (smaller mass= faster oscilation)
second order systems: damping ratio
Describes how oscilations decay after an input : z (zeta)
z= c/ 2squrt(mk)
z=0 - undamped system
1>z>0- underdamped
z=1 - critically damped
z>1 overdamped system
second order systems: undamped system
z=0
Nothing to damp out oscillations (c=0) and oscilations occur at wn
second order systems: underdamped system
1>z>0- underdamped
some damping but not enough to eliminate all oscillations (fast response)
second order systems:
optimally damping
z~0.7
does not eliminate all oscillation, but settles very quickly
second order systems:
critically damped system
z=1
No overshoot or undershoot (damping just just enough to eliminate oscillations)
second order systems:
overdamped system
z>1
no overshoot but reaches final value slower than critically damped
damping can be adjusted depending on what is most important in the system
-If overshoots cannot be tolerated: z= 1+
-If fast response time is desired, with small overshoot tolerated: z~0.7
Second order systems- example 1
underdamped suspension- will have large oscillations that take multiple cycles to settle (ie suspension too soft)
Overdamped suspension will not give quickly (ie suspension too stiff)
second order systems- example 1: acclerometers
wn- built with very small masses and stiff springs (~5kHz+)
z- Can very greatly, but optionally damped (0.707) generally best operating range
While minor oscillations could occur due intrinsic properties, these are often more related to properties extrinsic to the sensor
-sensor monting in unit (skin oscilating woth movement)
-sensort attatchment to body/system
-soft tissue artifact (be congnisant of location- try to attatch near bony landmark-used to go straight to done)
Filtering data
sensor outputs can be quite “messy” - ie oscilations
-oscillations, sensor noise (ie random variations in outputs), ect
-filtering the outputs can remove unwanted noise
low pass filter
-removes unwanted high freqeuncy components (lets the low pass signals pass)
High pass filter
removes unwanted low-frequency compoent (lets high frequency data pass)
