This document summarizes an activity recognition project that uses a Genuino 101 device, AWS IoT, and data visualization tools. The Genuino 101 collects accelerometer data and classifies activities using a pattern matching engine. Bluetooth Low Energy sends the data to an Android app, which uses MQTT to send messages to AWS IoT. The messages are stored in DynamoDB. Redash queries the DynamoDB table and creates visualizations of activity data over time. This project aims to recognize activities like walking, running, and sitting using IoT devices and cloud services.

1. Activity Recognition
Internet of Things 2019

2. OUR TEAM
Alessandro Giannetti | Andrea Napoletani
Riccardo Pattuglia

3. Activity
ML
algorithm
PredictionAccelerometer Data
visualization
Idea
Cloud

4. Technologies
Cloud Technologies Visualization Technologyhw/sw Technologies

5. Project workflow
Visualize on RedashTrain Genuino 101 and
classify the activities
Send data through BLE
to Android
Store messages in AWS
DynamoDB
Send MQTT messages
to AWS IoT

6. Genuino 101
Technical Specification
● Microcontroller Intel Curie
● Flash Memory 196 kB
● SRAM 24 kB
● Clock Speed 32MHz
● I/0 communication Bluetooth LE
● Sensors 6-axis
accelerometer/gyro

7. Genuino 101
● Low-Power Consumption
● Onboard Bluetooth LE and
a 6-axis
accelerometer/gyro.
● Dedicated core for pattern
matching capabilities.
PRO CONS
● The memory is very low
● The Pattern Matching Engine
has some limitations

8. Arduino IDE
● Simple (sometimes too much!)
● C/C++ support
● Many libraries implementing
the most used features (BLE,
Curie Module, …)

9. Setup function
BLEPeripheral blePeripheral;
BLEService fitnessService("19B10000-E8F***");
BLEUnsignedCharCharacteristic fitnessTypeChar("19B1****",
BLERead | BLENotify);
void setup() {
CurieIMU.begin();
CuriePME.begin();
CurieIMU.setAccelerometerRate(sampleRateHZ);
CurieIMU.setAccelerometerRange(1);
// CuriePME.setClassifierMode(CuriePME.KNN_Mode);
blePeripheral.setLocalName("Activity");
blePeripheral.setAdvertisedServiceUuid(fitnessService.uuid()
);
// add service and characteristic
blePeripheral.addAttribute(fitnessService);
blePeripheral.addAttribute(fitnessTypeChar);
// advertise the service
blePeripheral.begin();
}

10. Loop function
BLECentral central = blePeripheral.central();
byte vector[120];
unsigned int category;
if (central) {
while (central.connected()) {
readVectorFromIMU(vector);
category = CuriePME.classify(vector, 120);
switch (category) {
case 1: Serial.println(F("Still")); break;
case 2: Serial.println(F("Sit")); break;
case 3: Serial.println(F("Walk")); break;
case 4: Serial.println(F("Run")); break;
default:Serial.println(F("Unknown")); break;
}
fitnessTypeChar.setValue((byte) category);
Serial.println(fitnessTypeChar.value());
}
}
void readVectorFromIMU(byte vector[]){
byte accel[250];
int raw[3];
unsigned int samples = 0;
unsigned int i = 0;
while (samples < 250) {
if (CurieIMU.dataReady()) {
CurieIMU.readAccelerometer(raw[0], raw[1], raw[2]);
/* Map raw values to 0-255, only z axis taken */
accel[i] = (byte) map(raw[0], IMULow, IMUHigh, 0, 255);
i += 1;
++samples;
}
}
undersample(accel, samples, vector);
}

11. Train Genuino
101
Run Sit
Walk Stand
Solved Problems
● Progmem to load training data
at startup
● Training Datasets not working
● Manual training
Genuino 101 position
● Pocket
● Chest (discarded)

12. Train Genuino
101
Walk
Still

13. Bluetooth Low Energy (BLE)
● 2.4 GHz radio frequencies
● Simpler modulation system
● Data rate <1 Mbit/S
● No pairing required

14. How can we store this data?
Amazon Cognito Rule
Amazon DynamoDB
AWS IoT

15. MQTT protocol
MQTT
Broker
Topic
Message
Message
Subscribe

16. JSON
Format
Messages

17. Authentication with Amazon Cognito
● Create an Identity Pool with an
unauthenticated role in Amazon
Cognito
● Allows the application to perform all
operation on the Amazon IoT service
granting AWSIoTFullAcess permission
● Allow access from your Android App
to this Identity in the
awsconfiguration.json file

18. Configure AWS IoT
● Create a policy on AWS IoT Console
to allow connecting to AWS IoT as
well as allowing publishing,
subscribing and receiving messages
A policy ARN uniquely identifies a policy in AWS.

19. AWS IoT - Console

20. Store data on AWS DynamoDB
Store our messages in a table
We need a rule that allows us to take information from
an incoming MQTT message and write it to a
DynamoDB table

21. Create the rule
● Create a table in DynamoDB
● Create a rule to read
messages from our topic and
store them in our new table

22. Data visualization - Mango Display
PROBLEMS WITH MANGO DISPLAY/MIRROR
● Apple device + Android device.
● The software is not Open Source
● Limited Data Sources (Apple health and Fitbit)

23. Data visualization -
REDASH STRENGTHS
● Flexible, Powerful and Easy to Use
● Popular Open Source Project
● +50 Data Sources
(including dynamoDB)

24. Add DynamoDB to Redash
● Name:
DynamoDB table
● Access Key / Secret Key :
AIM Panel

25. How Querying the Data Sources

26. Querying DynamoDB
PROBLEM WITH DYNAMODB
● The QL of DynamoDB don’t support the aggregation functions like SUM(),
AVG(), complex COUNT() and specific operation for the dates like
FORMAT().
SOLUTION TO THE PROBLEM
● Redash allows you to query results from other queries

27. Querying on Queries results
● Add the Data Source Query Results
● Each queries constitutes its own "table" to SQLite. The
table name is the string query_ concatenated with the
Query ID.

28. Parameterizing Queries
Query Parameters let you insert values at
run time without editing your base query.
● Redash recognizes any string between
double curly braces {{ }} as a Query
Parameter.
● By clicking on the “{{}}” button, you can
enter the parameters.

29. HOURS FILTERING
SELECT substr(activity_number,11,19),
activity_type
FROM query_188916
WHERE substr(activity_number,0,11) == "{{
day }}"
ALL DAY
SELECT substr(activity_number,0,11)
FROM query_188916
GROUP BY substr(activity_number,0,11)
ALL DATA (on DynamoDB)
SCAN activity_type, activity_number
FROM activity_recognition
NUMBER OF OCCURRENCES
SELECT activity_type,count(activity_type),
FROM query_188916
WHERE substr(activity_number,0,11) == "{{day}}"
GROUP BY activity_type
Our Queries

30. Data visualization

31. Dashboard

32. Plan & Subscription
● 25 Gb storage
● 200 million requests per month
● 50.000 Monthly Active User
● 10 Gb storage
● 1 million operations of synchronization
● 250.000 messages per month

33. References
● GITHUB: https://github.com/riccardo97p/IoT_ActivityRecognition
● HACKSTER : https://www.hackster.io/andreanapoletani/activity-recognition-using-genuino-101-and-aws-
iot-fbeea2
● PROJECT
PRESENTATION: https://www.slideshare.net/AndreaNapoletani/activity-recognition-137479240
● REDASH GIFs: https://redash.io/
● IMGs: https://www.vectorstock.com/
● ALESSANDRO GIANNETTI: https://www.linkedin.com/in/alessandro-giannetti-2b1864b4/
● ANDREA NAPOLETANI: https://www.linkedin.com/in/andrea-napoletani-aa0b87166/
● RICCARDO PATTUGLIA: https://www.linkedin.com/in/riccardo-pattuglia-3a09ab182/

34. DEMO