RunnersRatio by Pepijn Verburg

RunnersRatio measuring respiration and steps

written by BSc. P. Verburg (m1.2) coached by prof.dr. A. C. Brombacher for the project There is no I in Team dated June 2015

table of contents

part 0: introduction

part 3: data sharing

0.1 abstract

3.1 requirements

0.2 introduction

3.2 ESP8266

0.3 process overview

3.3 discussion

part 1: data gathering

part 4: merging

1.1 requirements

4.1 casing

1.2 measuring respiration

4.2 strap

1.3 building respiration sensor

4.3 electronics

1.4 measuring steps

4.4 software

1.5 building pedometer

4.5 discussion

1.6 discussion

part 5: bibliography

part 2: data presentation

5.1 references

2.1 requirements

5.2 table of figures

2.2 technological probes

2.3 discussion

part 0: introduction 4

What are the goals and outcomes of this project and how is this report structured?

0.1 abstract In this report the development of a data gathering device for runners is presented: RunnersRatio. Respiration and step rate are collected. Combined as a ratio they are are key parameters to indicate performance for runners (Daniels, 2013). Respiration rate is measured through a custom designed sensor using a piezo element (Bhaskar et al., 2013). Step rate is measured using an accelerometer and several elegant algorithms (Zhao, 2010). In the end, a set of highly reliable devices are crafted to be used in a decentralized network (Stirling, D. & Al-Ali, F., 2003) to enable seamless data sharing in between devices. Light indicates whether the user has a proper ratio in comparison with the others strengthening the relation with the group and possibly creating social influences.

0.2 introduction The project â&#x20AC;&#x2DC;There is no I in teamâ&#x20AC;&#x2122; aims to explore the social influences of showing data about their performance to a sports team. The challenge is to show this data during the training or match. This opens up a wide range of new opportunities. Current monitoring gadgets most of the times only focus on data presentation afterwards (Taub, 2013). RunnersRatio does this in the context of runners, in particular group runners. Breathing is very important during running as it has significant influence on the physical state of your body (C. A. Walton, 1980). Therefore professional runners pay attention to their respiratory rate in comparison to their step rate. Having this at a certain ratio will benefit the performance (Daniels, 2013 & Dreyer, 2004).

The goal of this project is to create a reliable data gathering device that can be used seamlessly and is affordable. The device keeps track of your respiratory and step rate and fuses the two measurements to one comprehensive statistic: your RunnersRatio. More importantly, the data is gathered within a societal context: it is compared between individuals of the runners group. Social influences of these comparisons are explored during the project through the creation of multiple high-end prototypes that are fully functional.

0.3 process overview The design process of RunnersRatio has gone through three major iterations: data gathering, data presentation and data sharing. All of them were executed in parallel of each other. Each of them is described in an individual part of this report. Part 4 explains how they are merged together.

part 1: data gathering This iteration focused on the exploration of technologies that could be used to collect data about respiration and steps taken. Several technologies are considered for the respiration sensor. In the end an elegant solution is found by using a piezo element as a sensitive pressure sensor (Bhaskar et al., 2013). Also, a single algorithm to recognize steps in accelerometer data is implemented (Zhao, 2010).

part 4: merging

part 2: data presentation

In this final part the methods of data gathering, data presentation and data sharing are combined to one coherent final prototype. Three high-end artefacts are milled by hand to give it all a proper embodiment.

During this iteration a set of 5 technological probes (Peeters & Megens, 2014) are made that gathers the amount of steps taken of an individual in a certain period. It is tested with a basketball team to see what kind of social influences emerged when the person with the highest amount of steps is highlighted.

part 3: data sharing This iteration enabled the data to be shared across devices seamlessly. The ESP8266 microcontroller (Espressif Systems, 2015) offers a wonderful platform in doing this where there is no need for a centralized device for connectivity; a local embedded network emerges from a set of devices. 9

part 1: data gathering 10

What sensing techniques can be used to measure respiration and step rate?

1.1 requirements Measuring respiration and step rate is subject to three requirements (visual 1.1.1).

reliable

(1) The sensors should have a high reliability to prevent incorrect data presented to the user. Still, small error margins are allowed as it is not meant for a medical application. (2) They should be seamlessly usable, implying it should not be invasive and there should be no complex calibration procedures. And (3) the measurement methods should be affordable in both materials and required computational power.

seamless

fundamental requirements affordable

visual 1.1.1 - requirements 12

1.2 measuring respiration Measuring respiration is often done in the medical world. Many of the existing applications originate from this field. In this chapter they are briefly assessed on the mentioned requirements.

acoustic

capacitance

technologies to measure respiration

A total of 5 technologies (see visual 1.2.1) are explored that are alternative to the standard respirometer used in hospitals (where a tube is invasively inserted into the users mouth).

ECG

stretch

piezo

visual 1.2.1 - respiration technologies 13

acoustic

ECG

capacitance

Massimo Corporation (2010) introduced an noninvasive method of analyzing acoustic data of the throat. However, the environment of runners is very unreliable considering acoustic parameters and moving artefacts.

Abtahi et al. (2015) used a standard ECG to derive respiratory rate. Once placed correctly on the user this sensor gives very reliable data and also provides insight in heart rate. However, the placement of the sensor is distributed and should be done precisely. Also, firm contact with the skin is needed.

When breathing the capacitance of your body changes due to the increase and decrease of air volume in your lungs. This method is used several times in the development of a respiration sensor and proves itself to be reliable and affordable (Luis et al., 2014 & Kundu et al., 2013). The biggest downside is the placement of electrodes around the body: not centralized on one place decreasing the ease of using it. Still Min et al. (2014) managed to build a capacitive pressure sensor placed within a single strap around the body.

piezo

stretch

Bhaskar et al. (2013) wrote about one of the most affordable methods by using a piezoelectric element as a simple pressure sensor. A strap with a knob allows the piezo to be pressed on inhalation and released on exhalation.

Ratnarathron (2012) used cheap textile stretch sensors integrated in a shirt to measure both chest and abdomen breathing. They give reliable readings, but are very dependent on the integration in the garment. Also, they are sensitive to folds and unexpected displacement of the textile.

conclusion The pressure capacitive sensor and the piezo sensor appear to be the most useful for a respiratory sensor for runners. They (1) are almost not influenced by the environment, (2) are affordable in material costs and computational power, (3) have centralized components that are not scattered around the body. However, there is still one important difference between these two when looking at the physical properties of them. The capacitive sensing still requires some sort of calibration as it measures absolutely. The piezo element on the other hand reacts to change and only gives a reading when a change occurs: a temporary current is produced. This fundamental physical property is very convenient and allows the system to get rid of complex calibration procedures, because the component itself contains the calibration mechanisms. With the above in mind the decision is made to use the piezoelectric element as a starting point for the respiratory rate sensor.

1.3 building respiration sensor

version 2

An initial version is build to test whether the readings are the same as presented in the paper (Bhaskar et al., 2013). The visual below shows that repetitive and recognizable patterns are created on a 50Hz measurement rate.

The straps used in version 1 were not comfortable and adjustable. Version 2 explores the use of wider elastic and non-elastic straps. Readings remained the same (see visual 1.3.2), but it is noticed the geometry (e.g. length elastic strap, width piezo holder) influences the shape of the respiration pattern greatly.

volt

version 1

time

visual 1.3.1 - readings version 1 16

visual 1.3.2 - readings version 2

volt

140mm time

version 3

volt

210mm time

350mm

volt

In version 3 different lengths of the elastic and non-elastic straps are explored to create a reliable pattern. During these tests the movement artefact of running is taken into account by smoothing the measurements through averaging the last 8 readings. Measurement is still done at 50Hz. Visual 1.3.3 shows the different results for the different lengths of the elastic strap. Bhaskar et al. (2013) used an elastic strap between 140 and 350. The 210 elastic strap turned out to show the most clean and reliable readings while running.

time

visual 1.3.3 - elastic strap length test 17

version 4 In this version the recognition of inhalation and exhalation was developed. For this digital signal processing (DSP) is required, however the original respiration sensor (Bhaskar et al., 2013) didnâ&#x20AC;&#x2122;t provide one. The movement artefacts with runners make proper DSP crucial for the sensor to work properly. A total of four DSP methods were tested: (1) low-pass filter to enable peek-to-peek detection, (2) moving average to determine crossings, (3) auto-correlation for peek-to-peek detection and (4) independent component analysis also for peek-to-peek detection.

Using a moving average appeared to be the most effective and elegant. The low-pass filter didnâ&#x20AC;&#x2122;t provide a great enough difference in comparison with the smoothed values and is therefore obsolete. Auto-correlation required a high amount of processing power and the independent component analysis required a second-order sensor input registering the movements artefacts. Visual 1.3.4 shows a graph of the moving average and highlighting the recognition of inhalation

and exhalation. There was a minor error margin while running where 1 in 50 respirations are not recorded properly. This is not of significant influence on the calculations for the step and respiration rate ratio (see chapter 1.6).

volt time

threshold level piezo element

exhale recognition

inhale recognition

visual 1.3.4 - respiration rate analysis 19

1.4 measuring steps Accelerometers are a common method to measure steps, displacement, speed and even calories. Zhao (2010) puts forward a set of simple algorithms to implement these variables for any 3-axis accelerometer. Visual 1.4.1 shows the algorithm that will be implemented. Identical to the respiratory sensor (see chapter 1.3) it uses a moving average to determine a step on a crossing point.

visual 1.4.1 - algorithm flowchart (Zhao, 2010) 20

1.5 building pedometer The algorithm shown in visual 1.4.1 is programmed on the Arduino platform to perform the initial tests (see appendix 1 for the code).

threshold level

point of step recognition

x-axis y-axis

A standard 3-axis accelerometer of Pololu (Pololu, 2015) is used for these measurements on a 50Hz rate. Visual 1.5.1 shows the analysis of one axis and its threshold level to recognize a step. A step is basically recognized when the threshold level and one of the three axisâ&#x20AC;&#x2122;s intersect with a negative slope.

volt

An error margin of 1 in 100 steps is achieved after some of the algorithm sensitivity parameters are tweaked.

time

visual 1.5.1 - accelerometer step recognition 21

1.6 discussion This chapter briefly evaluates the respiratory and step rate sensors considering the requirements defined in chapter 1.1. Reliability The error margins found in both the respiration sensor (1 in 50) and pedometer (1 in 100) are acceptable as the ratio is not influenced significantly by these deviations. Visual 1.6.1 shows this in a calculation using error margins are 2 in 50 and 2 in 100. Seamlessness Both sensors donâ&#x20AC;&#x2122;t need any calibration procedures as this is fundamental to their physical properties and underlying algorithms (e.g. moving average and detection of change for the piezo element). Affordability The piezo element can cost up to $0.10 (Cosson Electronic, 2015), the accelerometer $1.00 (Shenzhen Guangshun Electronic, 2015) making them very cost efficient. These prices are merely to indicate the low material costs, as they even represent prices for low quantities. 22

Maximum deviations of 2: R

= 50

Rmin = 48 Rmax = 52 S

= 100

Smin = 98 Smax = 102 The two most extreme possible ratios: Ratiomin = Rmin / Smax = 0.47 Ratiomax = Rmax / Smin = 0.51 This minor deviation can be neglected in comparison with the expected ratio when no errors occur of: Ratio = R / S = 0.50

visual 1.6.1 - maximum error margin

part 2: data presentation 24

How should the data be presented to the user? What are the abstractions? What time-frame of data is presented?

2.1 requirements The gathering of data is one challenge, but presenting it correctly is another. One of the aims is to develop an intuition of what social influences the presenting of data has during a training or match, instead of afterwards. For this to work properly there are two main requirements.

real-time

(1) The data should be real-time, giving instant feedback about the performance. (2) There should be an abstract representation instead of showing the raw data directly to the user. Raw data is likely to be distracting from the current activity.

abstraction

fundamental requirements

visual 2.1.1 - requirements 26

2.2 technological probes Explorations for the data presentation are done in parallel with the process of the development of the sensors (see part 1). Therefore a brief sidetrack is designed to still be able to make design decisions for the data presentation. Method The method of Experiential Design Landscapes (Peeters & Megens, 2014) has been adapted to guide the process. A technological probe is developed to give answers to: (1) How does data during a training/match influence the behaviour of the team? (2) How does data publically presented influences the individual? (3) Where to allocate the presentation of data on the body? Data type For this side-track it is rather arbitrary what data to measure. For the convenience the fully developed pedometer has been chosen (see chapter 1.5). The idea is to collect all the steps taken by the individuals of a team and present the one person that has taken the most steps. The designer observes how this will influence the team and asks them informal questions afterwards.

Technology & design A total of five probes are made to test with (see visual 2.2.1). They are build with four components. (1) LightBlue Beans This nifty little microcontroller contain an accelerometer and Bluetooth connection by default. This will instantly support the datacollection and connectivity features. (2) Light Three LEDs are integrated to display information in a subtle way. They can be controlled individually for more complex purposes. (3) Casing All the electronics are embedded in a casing for protection of the sports activities. (4) Clips With clips and Velcro the casing can be attached to a piece of garment on the body. Velcro is used to prevent any damage to the garments when a strong force is applied to the probe; then it will simply fall off.

visual 2.2.1 - technological probes 27

Probe controller A mobile application for Android has been developed to connect all the LightBlue Beans (see visual 2.2.2 for the interface). The code can be viewed upon request. This app receives the total steps taken from each one of the probes generating an array of total steps on certain times. The amount of steps can then be calculated when looking back in time for a requested amount of minutes. An analysis is performed when a specific interval period has passed. One probe is highlighted as a result of this analysis; in this case the probe that took the highest amount of steps.

connect to probes

disconnect all probes

status tester reset step counter

probe overview

analysis interval

[ visual 2.2.2 - probe controller interface

visual 2.2.3 - participants Demo Day test An initial test is done during the Demo Days. Five people got their hands on a probe and performed a game of throwing balls to each other. They are not told to respond to the probes in any way. However, the observation is made that the probes cause to let another game emerge within the game of throwing the balls. People are eager to get rid of the light or to purposely get it. Basketball team test Via a staff member of the Department of Industrial Design a basketball team was recruited to participate in the test. During a match 5 of out 6 participants got a technological probe. The functionalities of the probes were explained in the consent form (see appendix 2 for all the signed forms).

The probes were carried around for 25 to 30 minutes. A total of three important observations were made:

(2) In a match you pay more attention to the opponents, it is hard to check the devices of your team mates.

(1) Not all participants were able to pay attention to the probes while playing basketball.

(3) It is hard to divide your attention, even though the information is subtle.

(2) At the beginning the participants notified each other when the light was turned on with someone.

(4) It is not about who is the best, but who has the best stamina.

(3) Two out of the five had a very good stamina, resulting in two people claiming the light all the time during the second half of the match.

(5) It would create a higher incentive to try your best if the tagging of the light represented something negative, for example, the person who took the least steps.

The participants made the following remark: (1) The lights were very clear, only not for yourself. 29

2.3 discussion The tests indicate several useful conclusions for future concept development. The requirements of chapter 2.1 are also taken into account. Social influences During the tests there are no significant behavioural changes. The main influence can be found in that people acknowledge that other team members have a better stamina. This might have a long-term influence to motivate them to get better. However, this cannot be confirmed in any way with these tests. Visibility The visibility of the presented data appears to be not that easy. The technological probes are mainly focused on a frontal view receiving several remarks on how to improve (see chapter 2.2). Placing the probes near the head of the participants has been one of the improvements accounting for the eye contact made during a match. However, using light in multiple directions is important, especially with RunnersRatio where it is unknown how the group is in formation during training. 30

Real-time The notion of real-time data is perceived as a natural thing during the tests. No explicit comments are made about this and it is already implemented properly. Abstract representation The abstract representation of light and tagging of one person representing a specific type of information proves to be effective. The participants are not overloaded with information and can easily comprehend what it is trying to communicate. Still, some participants mention that they could hardly pay any attention to it stressing the importance of one simple concept to present in RunnersRatio.

visual 2.3.1 - probes

part 3: data sharing 32

How to create an outdoor decentralized network? How to ensure seamless connectivity?

3.1 requirements The probes build with the LightBlue Beans are already able to share data effectively. However, there are a few additional requirements for the final concept.

decentralized

(1) A decentralized system is preferred to disable the need of a controlling device. (2) The connectivity of the devices should be seamless. The devices should immediately share data when they are worn and turned on by a group of runners.

seamless connectivity

fundamental requirements

visual 3.1.1 - requirements 34

3.2 ESP8266 The ESP8266 (see visual 3.2.1) is even a more niftier module than the LightBlue Bean. It contains a microcontroller, WiFi chip, serial communication and 16 GPIO ports (see visual 3.2.2 for internal components). This all costs around $4.00 in low quantities (Shenzhen S-Smart Electronics, 2015). This module is currently revolutionizing the Internet of Things (Karlinger, 2015). Zero-configuration networking Still, the most beautiful thing is that with a few lines of code this module can create its own Access Point allowing other modules to connect

visual 3.2.1 - ESP8266 module

to it. This allows the design to implement zeroconfiguration networking seamlessly (Stirling & Al-Ali). One device will sacrifice itself to be the server when none is available yet. Other modules will then connect to this device and possibly take over the server when the server device is disabled. Instant interfacing The modules create their own WiFi Access Point making it possible to directly connect to them with any WiFi device (smartphone, tablet, laptop). The module is then able to send a web-app directly

visual 3.2.2 - pin layout

to the browser of the connected device. This means there is no additional interfacing required, such as a native app. In visual 3.2.3 one can see a screenshot of a web-app sent to a smartphone directly from the ESP8266 chip. This page shows your ratio with two parameters: (1) the amount of steps taken on inhalation and (2) the amount of steps while exhaling. This is a standard notation for respiration rate for runners (Daniels, 2013).

visual 3.2.3 - web app 35

3.3 discussion The ESP8266 module is a wonderful solution to the two requirements put forward in chapter 3.1. (1) It is able to create a approximation to a decentralized system as all the devices are equal and one will take on the task of functioning as the server. The initial tests show a reliable connection. It is not tested in the field, especially outdoors. Still, the ESP8266 has been reported to work fine on ranges up to 366 meters outdoors when connecting an ESP8266 to an off-the-shelf router (CNLohr, 2014). See visual 3.3.1 for the setup of this test. (2) The zero-configuration networking principle automatically creates the notion of seamless connectivity. The user isnâ&#x20AC;&#x2122;t required to set up any connection between the devices. Limitations of WiFi is the high power consumption during intensive usage. They are documented up to 215mA (Espressif Systems, 2013). Still, if correctly implemented the ESP8266 can go as low as a power consumption of 10uA when in deep sleep (Espressif Systems, 2013). Future software optimizations should implement such mechanics. 36

visual 3.3.1 - range test setup (CNLohr, 2014)

part 4: merging 38

This part shows how the three previous parts are combined to one final prototype.

4.1 casing The goal is to create a product that looks reliable, sturdy, professional, high-tech and sporty. This chapter briefly shows the process of designing a casing for all the components from the previous parts. Components In visual 4.1.1 one can see an overview of all the required components. Together they will build (1) the respiration sensor from chapter 1.3,(2) the pedometer from chapter 1.5, (3) the light from chapter 2.2 and (4) the decentralized system from chapter 3.2. Additional components not yet discussed that are required for the functioning of the device are: (1) a LiPo battery, (2) a connector for an external power supply or recharging, (3) an on/off switch. Iterations A total of five iterations are made for the design of the casing. The next pages depicts all of them including a short description.

piezo element

accelerometer

as respiration sensor

as pedometer

array of LEDs

ESP-8266

as light

as decentralized system

LiPo battery

connector

as power

as external power visual 4.1.1 - components

ESP8266 accelerometer

lever as on/off switch

piezo element connector LiPo battery lever harness

iteration one: 115 x 75mm The piezo element requires a sturdy harness to let the force of the strap while inhaling apply properly. This harness can also be used as protection for the other electronics. A first version is made to explore the dimensions of the device. This is used as a reference point for the other iterations.

iteration two: 145 x 75 mm

iteration three: 135 x 51 mm

Two angled sides are added to fit the body more naturally and avoid the shape of a simple square box. It shows a more friendly expression while maintaining the high-tech look.

The casing is still fairly big in iteration 2. The layout of the components is slightly adjusted to decrease the size. However, the length of 135mm might not feel comfortable, because the surface of the torso is not very straight and moves around while running. Therefore a next iteration is made.

iteration four: 114 x 72 mm

iteration five: 105 x 72 mm

The piezo element doesnâ&#x20AC;&#x2122;t necessarily need to be placed in the center of the casing. It provides the best weight distribution as this is where the strap will be attached. However, the weight of the other components is that low the need for centering can be neglected. Moving the piezo element to one of the sides on top of the battery allows the other components to be placed together a lot more efficiently.

In part 2, data presentation, the remark about the light is put forward it is only visible on a frontal view. Therefore the choice has been made to use the two angled sides of the casing as a compartment for the LEDs, centralizing all the electronics and increasing the angle on which the light is visible for a group. Furthermore, the size of one of the circuits is decreased by excluding a breakout board. Finally, a smaller (lever) switch is integrated. 43

This will create a stronger casing in comparison with 3D printed materials which might be needed for testing it in the field.

105

63 44

25,500 105

40 3

34,500

20,500

28 28

33 33

20,500

The next pages will explain the manufacturing process as a visual summary.

6,869

,71

째

71째

The prototype casing is made out of two materials: (1) Ketron PEEK (Quadrant Plastics, 2015), a strong type of plastic, (see visual 4.1.3) and (2) Perspex, semi-transparent for the integration of the lights.

4,5

55,

Manufacturing The manufacturing of the casing is done by hand on the milling machine (see visual 4.1.2 for the technical drawings).

19 24 42

45 63

UNLESS OTHERWISE SPECIFIED: DIMENSIONS ARE IN MILLIMETERS SURFACE FINISH: TOLERANCES: LINEAR: ANGULAR: NAME

DEBUR AND BREAK SHARP EDGES

FINISH:

SIGNATURE

DATE

DO NOT SCALE DRAWING

REVISION

TITLE:

DRAWN CHK'D

SolidWorks Student Edition. For Academic Use Only.

APPV'D MFG Q.A

visual 4.1.2 - technical drawings

MATERIAL:

WEIGHT:

A3 electronics_casing_smalles DWG NO.

SCALE:1:1

SHEET 1 OF 1

visual 4.1.3 - raw pieces of Ketron PEEK 45

visual 4.1.4 - milling machine setup 46

visual 4.1.5 - the angled sides added 47

visual 4.1.6 - milling the compartments for the LEDs 48

visual 4.1.7 - casings without top plate 49

visual 4.1.8 - material for top plates 50

visual 4.1.9 - sanding to remove traces of machinery and to give a finishing touch 51

visual 4.1.10 - two sanded casings and their components 52

4.2 strap The textile strap for holding the casing on the body is made out of three different materials.

130mm

420mm

Materials (1) Elastic strap for flexibility which is needed for the respiration sensor, (2) Velcro for the attachment to the body and (3) a sturdy strap to prevent flexibility on specific places. Allocation Visual 4.2.1 shows the allocation of these different materials around the body. This distribution is based on the elasticity test from chapter 1.3 and several measurements done on male and female students. The result can be seen in visual 4.2.2.

body

210mm

visual 4.2.1 - material allocation 54

elastic sturdy velcro

visual 4.2.2 - strap

4.3 electronics This chapter shows how the electronics are placed into the casing (visual 4.3.1) and how all the components are connected (visual 4.3.2 and visual 4.3.3).

magnet LEDs ESP8266 piezo element LiPo battery accelerometer

external power power switch visual 4.3.1 - components in casing visual 3.2.2 - textile handheld 56

visual 4.3.2 - components in visual

visual 4.3.3 - components in schematic 57

4.4 software When looking at the software the electronic components (chapter 4.3) need to be handled differently according to their functionality. Timers are fundamental for this and are of great importance to let the sensors work correctly. Each process has a specific frequency tuned for its functionality. Very small steps are handled on each execution allowing multiple complex processes to run in parallel. There are several main processes in the software: 1) handling commands from the serial connection 2) handling requests to the webserver 3) handling transitional animations of the LEDs 4) reading new sensory data 5) handling sensory data to control the LEDs 6) checking for a new step 7) checking for a new inhale or exhale 8) applying any other behaviour than the LEDs Visual 4.4.1 shows these processes in a flowchart highlighting the most important relations. In appendix 3 the code of the final prototype can be found. 58

handleSerial

100Hz handleAnimations

handleWebserver

30Hz

100Hz

visual 4.4.1 - flowchart

Behaviour The respiration and step rates are compared with the other modules in the handleLeds process through sending its current ratio to the webserver. The webserver responds with whether the ratio is good or not. If itâ&#x20AC;&#x2122;s not good the LEDs will transition towards a red color.

execution order

piezo element data respiration event

handleSensors

50Hz

handleLeds

handleRespirationRate

25Hz

50Hz step event

applyBehaviour handleStepRate

1Hz

5Hz

accelerometer data

actuation

4.5 discussion In this final chapter several aspects of the final prototype are discussed. All the fundamental requirements that are put forward in the previous parts (see visual 4.5.2) are successfully merged into one coherent design. Respiration sensor The initial tests with the new prototype show the influence of the geometry on the respiration sensor. The dimensions are slightly changed in this prototype adjusting its readings. This shows the importance of testing on different dimensions of bodies and respiration rates. At the time of writing the sensor is not yet on the satisfaction level of the designers as it was with previous prototypes. The error frequency lies above the 1 on 50 mentioned in chapter 1.3. Adjusting how fast the moving average and current value react to new readings should be tuned again to solve this. Data presentation The desired breathing pattern when the ratio of the user is not good is currently not shown by the prototype. This has been considered, but external influences might direct the physical activity towards dangerous levels, because the 60

device is not aware of the physical condition of the user. Also, the device could provide the user with incorrect information. Testing The recalibration of the respiration sensor takes some additional time, but the plan is to test two or three modules out in the field in future phases. The goal is to see how (1) the user reacts to the data, (2) how a possible coach interprets this and (3) what social influences emerge from this.

visual 4.5.1 - presentation

reliable

abstraction

seamless

fundamental requirements real-time

affordable

seamless connectivity

decentralized

visual 4.5.2 - requirements 61

part 5: bibliography 62

A summary of all the references and sources to specific visuals used throughout this report.

5.1 references Abtahi, F., Snäll, J., Aslamy, B., Abtahi, S., Seoane, F. & Lindecrantz, K. (2015). Biosignal PI, an Affordable Open-Source ECG and Respiration Measurement System.

Luis, J. A., Romero, L. M. R., Gómez-Galán, J. A., Hernández, D. N., EstudilloValderrama, M. A., Barbarov-Rostán, G. & Rubia-Marcos, C. (2014). Design and Implementation of a Smart Sensor for Respiratory Rate Monitoring.

Bhaskar, A., Subramani, S. & Ojha, R. (2013). Respiratory belt transducer constructed using a singing greeting card beeper.

Masimo Corporation (2010). Continuous and Noninvasive Respiration Rate with Rainbow Acoustic Monitoring.

CNLohr (2014). ESP8266 Wifi Range/Distance Tests (Wi07C). https://www. youtube.com/watch?v=7BYdZ_24yg0. Visited on 18th of June 2015

Min, S. D., Yun, Y. & Shin, H. (2014). Simplified Structural Textile Respiration Sensor Based on Capacitive Pressure Sensing Method.

Daniels, J. (2013). Daniels’ Running Formula.

Peeters, M., Megens, C. (2014). Experiential Design Landscapes.

Dreyer, D. (2004). Chi Running: A Revolutionary Approach to Effortless, InjuryFree Running.

Pololu (2015). https://www.pololu.com/product/1252. Visited on 7th of February 2015.

Espressif Systems (2015). Esp8266. http://espressif.com/en/products/esp8266/. Visited on 7th of February 2015.

Quadratn Plastics (2015). Ketron PEEK. http://www.quadrantplastics.com/euen/products/machinable-plastics/advanced-160-220-c/ketron-R-peek.html. Visited on 17th of June 2015.

Espressif Systems (2013). ESPRESSIF SMART CONNECTIVITY PLATFORM: ESP8266. Karlinger, M. (2015). Meteor and the Internet of Things. https://www.youtube. com/watch?v=MdeaFLrvqtc&list. Visited on 16th of June 2015. Kundu, S. K., Kumagai, S. & Sasaki, M. (2013). A Wearable Capacitive Sensor for Monitoring Human Respiratory Rate. 64

Ratnarathorn, S., (2012). Design of a Stress Monitor Based on Breathing Signals Using a Smart Textile Shirt. Shenzhen Guangshun Electronic (2015). Piezo Element. http://www.alibaba. com/product-detail/35mm-piezo-ceramic-disc-with-49pvc_60262728930. html?s=p. Visited on 13th of June 2015.

Shenzhen Smart Electronics Co. (2015). Smart Bes ESP-12 large flash - 4 m capacity ESP8266 WIFI serial port ESP8266 module. http://www.alibaba.com/ product-detail/Smart-Bes-ESP-12-large-flash_60230547850.html Stirling, D. & Al-Ali, F. (2003). Zero configuration networking. Crossroads, 9(4), pp. 19-23. Taub, E. A. (2013). Wrangling Data From a Huge Variety of Fitness Gadgets. http://www.nytimes.com/2013/10/31/technology/personaltech/wranglingdata-from-a-huge-variety-of-fitness-apps-and-devices.html. Visited on 15th of March 2015. Walton, C. A. (1980). Device for measuring pulse, breathing and running rate for joggers. https://www.google.com/patents/US4202350. Visited on 5th of March 2015. Weiye Electronic (2015). ADXL345 Accelerometer. http://www.alibaba.com/ product-detail/ADXL345-3-axis-Digital-Tilt-Sensors_60141954779.html?s=p. Visited on 13th of June 2015. Zhao, N. (2010). Full-Featured Pedometer Design Realized with 3-Axis Digital Accelerometer.

5.2 table of figures Chapter 1.2 Stretch icon, width by Tracy Hudak from the Noun Project. Capacitance icon, capacitor by Lloyd Humphreys from the Noun Project. Piezo icon, piezoelectric crystal by Lloyd Humphreys from the Noun Project. ECG icon, activity by Ashwin Dinesh from the Noun Project. Acoustic icon, sound by Golden Roof from the Noun Project. Visual 1.4.1 Adapted from Zhao (2010). Visual 3.2.1 http://www.aliexpress.com/store/product/Certificate-of-Complianceesp8266-serial-WIFI-coexistence-module-AP-STA-AP-STA-WIFI-wirelesstransceiver-module/323553_32265303966.html Visual 3.2.2 https://esp8266hints.wordpress.com/ Visual 3.3.1 Adapted from video (CNLohr, 2014). Visual 4.1.1 Fritzing, http://www.fritzing.org/parts/ Visual 4.3.2 Fritzing, http://www.fritzing.org/parts/ 66

Thank you for reading.