biomedical device design report
TRANSCRIPT
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Project title: Performance improvement of an Active Ankle
Foot Orthosis (AAFO) using muscle signal feedback.
Tejaswita Patil
NUID: 03922699
Supervisor name: Dr. Carl Nelson
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Abstract
The ability to walk and move around is often taken for granted. Lifestyles are drastically changed when one loses
the ability to get around, not on just a personal level but also on the familial and friends level. It has been shown
that subjects with stroke have decreased walking speed, asymmetry in gait pattern, muscular weakness,
abnormal movement strategies, and spasticity. Individuals who have these abnormalities are more likely to have
risks in falling which may lead into serious physical health issues. Dynamic active-ankle orthosis (AAFO) attempt
to reproduce normal knee motions during the whole gait cycle. In this research experiment, a new type of AAFO
with a series elastic actuator (SEA) mechanism is going to be developed which utilizes muscle nerve impulses
as the control system. This biomedical device will consists of SEA that will power the locomotion to those that
have difficulties in walking/movement in general. This biomedical device will be equipped with electromyography
(EMG) sensors that can sense the muscle impulses. The muscle impulses will send a signal that will then be
read by a 16 bit ADC and amplified for better detection and processing. A controller will then calculate and feed
the output to the motor driver, which in turns will run the motor. The power from motor will then be transmitted
through the SEA, thus moving the ankle.
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Introduction:
The ability to walk and move around is often taken for granted. Lifestyles are drastically changed when one loses
the ability to get around, not on just a personal level but also on the familial and friends level. Moving around,
yet alone being able to stand, can be lost in numerous different ways, but stroke is the second leading cause of
loss of mobility [1-2]. It has been shown that subjects with stroke have decreased walking speed, asymmetry in
gait pattern, muscular weakness, abnormal movement strategies, and spasticity [3-5]. Individuals who have
these abnormalities are more likely to have risks in falling and may lead into serious physical health issues. Not
only do people who experience stroke and lose functions in movement, but these individuals develop energy
inefficient patterns of walking, if they are still able to walk [4].
To aid in the movement to those that have loss mobility from stroke, there are currently numerous medical
devices that enhance mobility. These devices are collectively called foot orthosis, which may be sub categorized
into ankle foot orthosis (AFO) or knee-ankle-foot orthosis (KAFO). In general, a FO brace is designed to aid in
the control of biomechanical alignment, correct or accommodate deformity, protect and support an injury, assist
rehabilitation, reduce pain, increase mobility of the foot, as well as increase independence of the patient.
To date, several types of AFOs have been developed: passive AFOs, semi-dynamic KAFOs (SDAFOs), and
dynamic AFOs (sometimes referred to as active) [7]. Passive AFOs work without any power source and are the
most primitive type of AFOs. SDAFOs lock the knee joint during the stance phase. Dynamic AFOs attempt to
reproduce normal knee motions during the whole gait cycle. There are two types of dynamic AFOs that have
been reported in the literature. The first one is activated by using a pneumatic system and the second one which
uses a spring mechanism. Both systems are bulky and are controlled through complex control systems that limit
their application as assistive devices.
Dynamic AFOs have been recommended for this group of subjects [6-8]. However, the effect of various AFO on
the performance of these subjects during walking depends on the structure of orthosis and their ankle mechanism
[6-9]. In previous studies, Gok et al. showed that the gait of hemiplegic subjects improved more with metal
orthosis compared to plastic AFO, due to its better stabilization during stance phase
A common problem of stroke subjects during walking is contributed to the ankle joint plantar flexion power. Ankle
joint plantar flexion power is decreased significantly compared to normal subjects [5]. Various attempts have
been done to restore plantar flexion moment in subjects with stroke by use of externally powered orthosis [8, 11-
19]. The idea of incorporating active ankle mechanism in design of AFO has been fantasized since 1980.
It was recently shown that patients who lack complete muscular control can learn to control slow cortical potential
to operate e.g. an electronic spelling device [1-20 Charles]. It has been shown that motor imagery can modify
Rolandic mu and central beta rhythms in a task-specific way [2-21] and therefore, mental imagination of certain
movements can serve to produce self-induced variations of the electroencephalogram (EEG) [22-25]. Our goal
is to develop a lightweight powered orthosis for the human lower limb that could comfortably provide plantar
flexion and dorsiflexion torque during walking.
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In the first design of this orthosis, hydraulic and pneumatic actuators were used to power the ankle joint. Up to
now various approaches have been used to restore the power of ankle joint including: Series elastic actuator
(SEA), magneto rheological fluid, passive pneumatic element, frictional clutch, oil damper, artificial pneumatic
muscle, shape memory alloys [26-32]. On top of all above mentioned mechanisms, SEA provides more
advantages, due to the low controlled impedance, good control bandwidth, shock tolerance, low friction.
However, most of the studies that have looked at these mechanisms neglect precise control system according
to natural body function. As in, patients require to control the movement of their ankle by themselves rather than
operating it by separate computer and operator.
The project that our group wants to focus on is to improve an assisting device for patients with disabled or semi-
disabled ankle movement. It is generally referred to as “Drop foot”. “Drop foot” is the inability of an individual to
lift their foot because of reduced or no muscle activity around their ankle. In this research experiment a new type
of AFO with SEA mechanism and EMG sensor is going to be developed that utilized muscle nerve impulses as
the control system. The feasibility and validation of this new design will be investigated in this study.
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Product Specifications :
The product is required to produce plantar flexor torque and
dorsiflexor torque. These values depend on the weight and size
of the patient. From the previous researches we can conclude
that it will require peak plantarflexor torque of 70 Nm and peak
dorsiflexor torque of 38 Nm [33].
Another feature of walking is gait. Gait is a cyclical pattern of
leg and foot movement that creates locomotion. Gait is
commonly discussed in terms of a percentage of a single gait
cycle. A gait cycle is defined for a single leg and begins with
the initial contact of the foot with the ground or ‘heel strike’;
the conclusion of cycle occurs as the same foot makes a
second ‘heel strike’. To illustrate a typical pattern of gait,
consider the illustration of the ankle complex during stance
phase of a single cycle of gait, figure 1 and the kinematics
and kinetics of a normal ankle, figure 2. Notice that in figure
2, peak ankle moment occurs at roughly 45% of the gait cycle
and at a normalized value of -1.25 Nm/kg. The negative sign
represents the physiological direction of the plantarflexing
ankle complex. The foot rotates downwards to push off from
the ground. At the point at which the peak moment occurs,
the ankle angle begins a rapid descent to its lowest overall
value of -24 degrees at 60% of the gait cycle. The region of
gait approximately between 45% and 60% of the gait cycle
is known as ‘push off’. At the conclusion of ‘push off’, now
considered ‘toe off’, the leg initiates ‘swing’ and the foot is then positioned for the next ‘heel strike’.
Electronic Parts Specifications:
1. 16 ADC and Amplifier: ADS1115 16-Bit ADC - 4 Channel with Programmable Gain Amplifier
Description: For microcontrollers without an analog-to-digital converter or when you want a higher-precision
ADC, the ADS1115 provides 16-bit precision at 860 samples/second over I2C. The chip can be configured as 4
single-ended input channels, or two differential channels. As a nice bonus, it also includes a programmable gain
amplifier, up to x16, to help boost up smaller single/differential signals to the full range. This ADC was chosen
because it can run from 2V to 5V power/logic, can measure a large range of signals and its super easy to use.
It is a great general purpose 16 bit converter.
2. SEA and Motor
Figure1. Stance phase of a single gait cycle. 60-100% of gait is the swing phase, not shown.
Figure 1 Normal Ankle Gait: Kinematics and Kinetics [34].
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Description: The Series Elastic Actuator (SEA) was developed for the current version of this device. SEA includes
a coupling, metal plates, four compression springs, eight bushings, one ball screw, one ball nut, and four guide
rails the compression springs were used in this design to minimize the effect of backlash, to isolate motor from
shock loads and torque ripple. The ball screw and the ball nut convert rotations of a motor into linear motion.
The SEA generates enough force for the movement of ankle based on the control signal of the motor. The SEA
consists of a 100W AC servo motor with maximum speed of 5000 rpm (XML-SA01A, LS Mecapion). (FIG)
3. Controller and Motor Driver:
Description: A master controller (PCI1710 and PCI 1723, Advantech) is used to detect the signal from the
ADS1115 16-Bit ADC and produced output for the Industrial motor driver (XDLL740A001, LS Mecapion) which
in turns rotate the motor with required speed and torque.
4. EMG Sensor: Muscle Sensor v3 (SpurkFun)
Description: Measuring muscle activity by detecting its electric potential, referred to as electromyography (EMG).
This sensor will measure the filtered and rectified electrical activity of a muscle; outputting 0-Vs Volts depending
the amount of activity in the selected muscle, where Vs signifies the voltage of the power source.
Parameter Min TYP Max
Power Supply Voltage (Vs) ±3V ±5V ±30V
Gain Setting, Gain = 207*(X /1 kΩ) 0.01 Ω (0.002x) 50 kΩ (10,350x) 100 kΩ (20,700x)
Output Signal Voltage (Rectified & Smoothed) 0V -- +Vs
Differential Input Voltage 0 mV 2‐ 5mV +Vs/Gain
5. Sensor Pads: Sensor Cable - Electrode Pads (3 connector) [CAB-12970ROH]
Description: This is a simple three conductor sensor cable with electrode pad leads. These cables are 24" long
and feature a 3.5mm audio jack connector on one end with snap style receptacles for biomedical sensor pads.
Each cable has a red/blue/black set. FIG
Review of Existing Products
An example of a passive AFO that is currently out on the market is make by Townsend, Whose design is
composed of the following: 1 3/8 inch wide solid core graphite uprights and 1 3/8 inch of 2 inch wide solid core
graphite bands with radius edges, polypro foot plate trimmed to metatarsal joint heads with reinforced stirrups,
removable &; replaceable soft interface AK & BK liners, knee joint covers protective coating removable &;
replaceable soft interface strap pads. A figure of Townsends® standard KAFO is shown in figure 3. Townsend
Premier Series AFO says that it will maximize patient functional mobility. The patient will also achieve the
secondary benefit of wearing a brace that enhances their comfort and lifestyle. Townsend’s solid core graphite
shell designs are combined with patented knee and ankle hinge technology. Resulting in rigid, durable bracing
solution that achieves more effective and consistent control. Ultra-light, extremely low profile, and cosmetically
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appealing, these optimize patient compliance and satisfaction. Townsends® KAFO works in
the way that the patient must insert their leg into the device and then secure the device with
the provided straps. The device wearer will then have aided support in their movements. The
patient’s movement will be restricted in a manner that will strengthen their current way of
walking, in order to correct it to a more normal walking pattern. This device has its downfall
because some patients may not be able to move their lower part of their body easily, thus
will need more aid in movement. Thus, we may need to explore more dynamic AFOs.
The dynamic AFO that we’d like to talk about is a design that was proposed by Cullel et al.
whose group developed an electrically controlled dynamic AFO with compression spring
actuators to duplicate the normal knee stiffness pattern during the whole
walking gait cycle and to mimic the quadriceps and hamstrings muscle group
behavior [34]. The knee actuator includes: a stance control (SC) stiff spring,
a swing control soft spring, a solenoid, a control circuit, a shaft that both springs are wrapped
around, an upper block connected to the thigh segment, and a lower
block attached to the two compression springs. This can be seen in the
figure 4.
Technology gap to fill
As mentioned in our review of existing products, we wish to improve
current products our proposed dynamic AFO is innovated and
revolutionary due to the fact that we will incorporate SEA and EMG
sensors in order to obtain the muscles electrical impulses generated
from the patient's muscles. We believe that this method will allow
independence and comfort. Our proposed device out classes current
state-of- the-art dynamic AFOS because the current dynamic AFOs require
ones to be connected to a computer in order to receive the information. Our
rationale for using EMG sensors with our dynamic AFO is to better simulate natural motion, as in there is not a
computer connected to a person who does not suffer from orthosis problems.
Functional Decomposition
From our functional decomposition, as seen in Appendix A, we see that the main functional goal is to improve
mobility for the user. From there we broke our main function into three sub-functions which are: to have the
desired mechanical properties, to have desired electrical attributes, and to mimic natural locomotion. We chose
this breakdown because our biomedical device has two integral components being the base component of the
device (SEA) and the EMG sensors. The base of the biomedical device must have the desired mechanical
properties, namely: being able to support load and provide proper torque. To further expand on supporting load,
our biomedical device must be able to support load from the entire weight of the individual, the knee, and the
ankle. This is critical, since the biomedical device we are proposing is intended to relieve some of the load, thus
allowing easier movement for the patient.
Figure 2: Generic KAFO
Figure 4: Stance Control diagram
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The next sub function from our main function that we want to discuss is to have the desired electrical properties.
This sub function is critical in our biomedical device, because our device will consists of EMG sensors. In order
for our biomedical device to work efficiently, we must have good signals, and more specifically we must be able
to process the signals and sense the muscle nerve impulses. Going back to having to the desired electrical
properties, we must be able to provide the proper electrical power to our device, which in turns allow regulation
of current to our motor; this all should be shown on a display.
Finally, the third sub function from our main function, improve mobility, is to mimic natural locomotion. This is a
key sub function. Because our biomedical device is supposed to aid in movement. It would be more desirable to
have a device that can let you “not stand out,” thus mimicking natural motion to alleviate the “Drop foot,” would
be best. To mimic natural motion, our biomedical device will have to be able to move the foot and ankle at
variable speeds and have flexibility.
Morphology Chart
Appendix B shows a well-organized brainstorming approach for designing our proposed AAFO. The main
purpose of this table is to create certain number of concepts and methods that can be achieved in the physical
domain. According to the proposed morphological chart for this biomedical device, there are three columns of
concepts and one column for sub functions. In the first column of this chart a list of sub functions has been written
and three concepts of design have been specified for each sub function. For instance, mechanical power
transmission is one of the most significant functions that can directly affect efficiency of device. There are three
different concepts of design for power transmission including ball screw, pneumatic cylinders, and cable or pulley
system. According to many factors like friction, weight, and dimension, designers should find the best option for
current device. As mentioned before, AAFOs must work with minimum amount of energy and should be as small
and light as possible. Consequently, using reasonable combination of concepts for different functions, three or
more designs will be created that must be evaluated using Pugh matrix with certain methods which will be
described later.
Interpretation of Pugh Matrix
As indicated in our appendix we have two iteration of our Pugh matrix. In order to analyze the Pugh matrix that
we constructed, we take the information gained from our morphology chart and compare the different concepts
to each other. Giving a +1 for a better design, 0 for an equivalent design, and -1 for a worse design when
compared to a given concept. With that being said, we can take column one (D1) as our datum and compare it
to column two (D2) & three (D3) and compare row by row we will to determine which design is best. It is good
to note that by default, the D1 summation will be zero (since we are using D1 as the datum). We thus compare
D2 and D3 with D1 as the datum, row by row.
We see that when looking at the sub function, support load from the knee, D2 scored a -1 and D3 scored +1.
We see that when looking at the sub function, support body weight, D2 scored a 0 and D3 scored -1. We see
that when looking at the sub function, support load from the ankle, D2 scored a 0 and D3 scored -1. An exhaustive
and explicit scoring can be found in the Appendix. We wish to summarize this and report that the D2 has a final
score of -2 and D3 has a final score of -10.
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We now do another iteration of the Pugh matrix analysis by making D2 the datum and compare row by row to
D1 and D3. Again, the explicit scoring can be found in the Appendix and we will simply report that D1 has a final
score of 9 and D3 has final score of -11. Since we see that D1>D2>D3 in the first iteration and D1>D3 in the
second iteration, we can skip the work for the third iteration of the Pugh matrix (setting D3 as the datum) and
come to the conclusion that D1 is best.
QFD
Quality function deployment (QFD) is a technique where one can find out how to strongly influence the quality of
a product, subassembly, or component. One of the most important outcomes of the QFD matrix is contained in
areas 7-8. Area 8 is a scaled version of area 7, where all the entries sum to 1 (or 100%). Thus area 8 represents
the relative importance (in percent) of each DP. This information can be used to focus the design efforts on those
areas which have the greatest capacity to increase quality as measured by FR outcomes.
With that being said, we see from our QFD matrix in Appendix G, the most important DP are the sensors. This
makes sense because the product is improved based on the addition and improvement of the sensor system.
The value in the improvement column for the good sensitivity / filtering is assigned as 2.9 out of 3.0 as this is a
considerable improvement of the sensor system. Finally the sensors becomes the most important DP in the
technical evaluation section with 19% relative importance. The second most important DP is the user interface
in this new design as a portable interface with LCD display has been added to the existing device replacing the
stationary PC based monitoring system. This gives it the relative importance of 13%. Apart from Sensors and
user interface, material type, motor power and surface coating are the most influential DPs that has higher
impacts on the customer needs. Material type is important for the mechanical properties, motor power is the key
source of actuation and surface coating is important for the user comfort, sensor placement and wear resistance.
We replaced some metal parts (Steel/Al alloy) with carbon fiber composite in the new design. This enables us to
reduce the weight while retaining the strength.
Ease of use, light-weight, wear resistance, endurance for cyclic load, robust design, reusability, good response
, biocompatibility are our most important design considerations as the device is supposed to be used every day
and in uncontrolled environment. With the EMG sensor, user interface and the additional electronic components
the new design is much effective, robust and user friendly.
Modeling of Parts/Design
One of the most important parts of this project is designing mechanical components, mechanisms and support
components like plastic orthosis. The following figures show decomposition of parts along with assembled
design. Since weight plays an important role in design of the orthosis, we as designers must optimize the design
to minimize the use of materials while l decreasing weight.
Therefore, weight will be considerably decreased which will in turn remove stress free parts in specific
components (four horizontal plates in the SEA). Design of plastic orthosis for supporting leg, ankle and foot must
customize for the specific patient. This customizable orthosis must be done using 3D imaging of foot... The
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minimum thickness plastic plate can be found using preliminary finite element modeling of plastic orthosis under
static and dynamic loading. In this design, the thickness is 5mm.
Materials
To choose the best materials for our biomedical device, we must look at the performance index for that specific
materials function. The first component that we want to look at of our biomedical device is the shaft. The objective
function for the shaft is to minimize the weight of the shaft and maximizing the yield strength. With that being
said, we looked at numerous types of metals, as indicated by our Ashby diagrams in Appendix C.
The most optimal material is then stainless steel, martensitic, AISI 440B, wrought, tempered at 316°C. Here we
can see that the stresses are induced due to tension, compression and torsion. Referring to the performance
indices on Table 7.2 located in “A Primer on Engineering Design of Biomedical Devices “by Dr. Carl Nelson,
where common performance indices for different types of materials, in various failure modes are mentioned, the
index for a shaft in bending = σ/ρ.
Looking at the Ashby diagram created by CES EduPack 2015 software (Granta Design, Cambridge, UK) and by
setting the slope of the line equal to one (since it is on a log scale) we are able to find materials that are
considered better or worse for the performance index we chose.. Since we need to maximize the performance
index, we shift the line towards the materials on the upper side of the diagram. When doing this, we see numerous
different materials that can be a “good” choice. Here we choose stainless steel, because stainless steel is readily
available and is economical (which was also shown in Gok et al. as mentioned earlier). Some advantages of
using stainless steel include: high strength, good wear resistance, and ductile energy absorption. Some of the
disadvantages include: corrosion and high density.
Figure 5: Exploded view of parts Figure 6: 3D Models
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The second material we want to look at is the foot plate. We need to minimize the weight of the foot plate while
maintaining its yield strength. The foot plate undergoes tension, compression and torsion. Similar to the process
as mentioned above, our group chose polypropylene as the material to be used for the footplate. The Ashby
Diagram for foot plate casting can be found in Appendix A, where we chose polypropylene to be the best material.
The third part we want to look at is the Lead screw (ball screw). Stresses induced in the lead screw are due to
tension, compression and torsion. Since this screw will be very frequently used as the force is applied by the
motor to the shaft through this screw, we need the screw to be resistant to torsion and bending. We use AISI
1055 alloy steel as the material with similar reasons to why we used AISI 440B.
The next material selection that we want to examine is for the joints. Depending on which joint, we choose
different types of materials. For one of the joints we chose to use 6061 aluminum for the joints (similar type of
material has been used in Patoglu et al [39]. Aluminum is used in this application because of its good strength
and hardness, corrosion resistance, ease of fabrication and other advantages. It is good to note that similar
materials like 7075-T6- aluminum and 303 stainless steel have been used in existing FO from Becker
Orthopedics for the connection joints [40].
To fabricate our biomedical device we must use pins to constrain some movement of the ankle joint and provide
a moment. Failure in pins occur due to shear primarily and bending. Since pins are relatively small part, we
chose to use standardized pins to follow the rules of DFA. As stainless steel pins are known for their strength
and availability, we use stainless steel pins in this application.
We will require fasteners to attach different parts in the SEA. Thus, fasteners should be strong enough to bear
the load of the motor and the person. Again, for the sake of DFA and availability, we purchase standardized
fasteners from the market.
Finally we perform material selection of the Actuator. We want the actuator to be as light as possible since it is
attached near the calf and can induce a lot of stress while walking. We use a Series Elastic Actuator for this
purpose.
The last material we want to look at is really more of the device that is composed multiple components and is
the heart of our biomedical device. The SEA, which was developed at the MIT Leg Laboratory, is used to power
the Active Ankle Foot Orthosis (AAFO) [41].
This actuator can be used to control the motion of the ankle in the sagittal plane. The SEA consists of a brushless
DC motor in series with a set of springs to provide series elasticity (Pratt and Williamson, 1995). By measuring
the deflection of the springs, the SEA provides force control through a position sensor. The deflection of the
springs is measured by a linear potentiometer sampled at 1000 Hz. This measurement is passed through a first
order filter with a cutoff frequency of 50 Hz. It is then numerically differentiated and passed through another first
order filter with a cutoff frequency of 8 Hz. The SEA is controlled through an internal feedback loop with a
Proportional-Derivative (PD) controller with gains of 10 and 15, respectively.
The advantages of the SEA is that it has low impedance, the motor is isolated from shock loads and the effects
of backlash, torque ripple and friction are filtered (by the spring). The SEA allows the implementation of any
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virtual, torsion mechanical element around the ankle. Specifically, the AAFO will implement a virtual linear torsion
spring or spring-damper system with varying values of stiffness and damping to accommodate the user.
DFM/DFA
For our device we want to use as least amount of as parts, while still achieving all of our functional requirements.
Therefore, we choose to remove extra parts or combine multiple parts to create multifunctional parts (ie: ball
screw system). Are medical device has many prefabricated and standardized parts readily available for purchase
(ie: AC servo motor, ball screw system, fasteners and vertical shafts).In our medical dice, the thickness of the
horizontal metal plates are standard to minimize manufacturing process. Our vertical shafts have standard
diameter, but must be cut based the patient. We acknowledge this in our DFM/DFA analysis, but nothing can
really be done since it is in our device’s nature to be formed for a specific patient. We also must customize the
four vertical plates for our device. This can be done by wire cutting or laser cutting, however these methods are
relatively expensive
The last guideline that ensure the most efficient design for manufacturing is to avoid secondary operations. Only
some of the parts that we need in our device require secondary operations, since most of our parts are standard
components. Most of the parts especially in SEA are symmetric that do not have very tight tolerances.
As seen in Appendix E, our DFA analysis yields that foot plate>motor>SEA> transmission>pins fasteners
>EMG> interface>battery has an assembly index of 358 while motor>SEA> transmission>pins>
fasteners>battery>EMG sensor>interface >Foot plate has an assembly index of 374. Thus, we conclude that the
first assembly method is a better avenue than the latter.
FMEA Analysis
FMEA is a systematic, proactive method for evaluating a process to identify where and how it might fail and to
assess the relative impact of different failures, in order to identify the parts of the process that are in need of
change. FMEA consists of identifying what failures might occur, predicting what the effect on the overall system
would be, and taking or recommending steps to prevent the failures. Three factors are considered for ranking
each failure mode: severity of the failure, likelihood of the failure occurring, and likelihood that the failure would
be detected. Ultimately, a risk priority number (RPN) is calculated for each failure mode:
RPN = severity × occurrence × detection. Different failures modes are observed which can be seen in Appendix
F.
Here we considered numerous types of failures. Failures such as: bad motor driver, no power being sent, and
low power are considered. An exhaustive list of failure modes and their corresponding RPN can be seen in
Appendix F. To be brief, we will mention the top two RPNs and their corresponding failure modes. The highest
RPN (120) that we calculated was due to failure of the motor. This makes sense because without the motor, the
AAFO will not function at all (be more of a heavy weight). The second highest RPN (100) is when there is a
failing in charging. This makes sense due to the fact that one of the key sub functions that we defined earlier is
to have proper electrical properties. Thus, not charge implies that there is no proper electrical properties. Some
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possible avenues to correct our current issues could be incorporating an energy efficient battery, yet this may
increase in weight which is something we wish to avoid.
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Appendix A: Functional Decomposition
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Appendix B: Morphology Chart
Sub Function Concept 1 Concept 2 Concept 3
Support Knee Plastic Leg Orthosis Metal Structure Carbon Fibre Orthosis
Support body weight Composit Structure Metal Structure Glass
Support load for ankle Motor Pneumatic Systems Spring loaded system
Transmit power Ball Screw Pneumatic Cylinders Cable & Pulley
Provide eletrical power Wall Socket Battery IC Engine
Display device status LCD Screen LED Display LED bulbs
Regulate current to motor MOSFET Driver BJT Driver Relay Based Driver
Process Signals ADC with high resolution and amplifierADC with higher resolution Amplifier
Sense muscle/nerve impulseEMG Sensor Force Sensor Limit Switches
Move leg up/down SEA(Seres Elastic Actuator) Use of Cam Pump
Move/turn ankle SEA(Seres Elastic Actuator) Pnumatic System Spur gear
Have flexibility Elastic Plastics Super elastic metals Rubber
Have variable speeds Electronic Controller Change in the dimension of mechanical partsUse brakes
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Appendix C: Ashby Diagrams
Shaft
Plate
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Joints
Foot plate
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Appendix D: Pugh Matrices
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m-1
-1Se
nse
mu
scle
/ner
ve im
pu
lse
1d
atu
m-1
Mo
ve le
g u
p/d
ow
nd
atu
m-1
-1M
ove
leg
up
/do
wn
1d
atu
m-1
Mo
ve/t
urn
an
kle
dat
um
-1-1
Mo
ve/t
urn
an
kle
1d
atu
m-1
Ha
ve f
lexi
bili
tyd
atu
m1
0H
ave
fle
xib
ility
0d
atu
m-1
Ha
ve v
aria
ble
sp
eed
sd
atu
m-1
-1H
ave
var
iab
le s
pee
ds
1d
atu
m-1
0-3
-10
90
-11
19
Appendix E: DMA/DFA
Pa
rt#
Pa
rts
Ass
e
mb
ly
Pa
rt
Re
qu
ire
d
AL
= a
lignm
ent
diffi
cult
OB
= o
bstr
ucte
d
NTD
= n
ot t
op d
own
RE
S =
res
ista
nce
HIP
= h
old
in p
lace
Sm
all
Tang
led
Fle
xibl
e
No
end
No
inse
rt
Hea
vy
or t
ools
A
LO
BN
TDR
ES
HIP
FA
STE
N
<12
mm
(+
1)(+
2)Tw
ist
(+1)
<2m
m (
+2)
(+2)
(+2)
(+2)
(+1)
if c
lear
(+2)
(+2)
(+2)
(+2)
(+2)
(+2)
Scr
ew (
+3)
Foo
t P
late
22
22
22
41
16
mot
or2
22
22
22
24
120
Ser
ies
elas
tic a
ctua
tor
22
22
22
24
118
tran
smis
sion
22
22
22
41
16
pins
12
22
23
1619
2
fast
ener
s1
22
210
70
EM
G S
enso
r2
22
318
inte
rface
21
2
batt
ery
22
22
24
456
To
tal
3835
2 0
mot
or2
22
41
10
SE
A2
22
22
41
14
tran
smis
sion
22
22
24
114
pins
1
22
22
316
192
fast
ener
s1
22
210
70
batt
ery
22
22
24
456
EM
G s
enso
r2
22
318
inte
rface
1
0
Foo
t pl
ate
22
22
22
41
16
To
tal
3837
4
Goa
l: m
inim
ize
asse
mbl
y in
dex
and
part
s co
unt
Par
t re
quire
d if:
rela
tive
mot
ion
Par
ts r
educ
tion:
diffe
rent
mat
eria
l
(dis
)ass
embl
y im
poss
ible
with
out
Re
trie
veH
an
dle
Inse
rt
20
Appendix F: FMEA
21
Appendix G: QFD
22
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