: IoT Device and Visualizing Application for Monitoring the Running Gait

Our project suggests a mobile application that monitors running gait using IoT. We utilized MPU-6050 sensors and a Force Sensitive Resistor(FSR) to measure the angle of the foot and identify different strike types.

Jihyeon Yun	Hojeong Eom	Minju Kim	Hyejin Kim	Suyoung Lee	Nicole Hornbrook
Chungnam National Univ.	Chungnam National Univ.	Soongsil Univ.	Chungnam National Univ.	Chungnam National Univ.	Purdue Univ.
IoT Device	IoT Device	Application	IoT Cloud	IoT Cloud	Application

More details about Thingsboard

Our goal is to make an application that monitors the user's running gait. We will provide running information such as distance, average pace, and total time of the run, much like other existing running applications. Unlike other applications, however, we will provide the measured foot angle and the location of the first point of contact. After the IMU sensors and FSR sensor measure the user's run, the data will be sent to the server. After that, the server will processes and analyze the data. Once this process is over, the app will visualize this data to help the user monitor his/her running gait.

1. Growth potential of IoT usage in healthcare is exponentially increasing
2. Current lack of research on running gait monitoring & analysis
3. The reoccuring rate of injury from running is high

Previous studies were costly and restricted to experimental environments only. Most existing related research used motion capture; meaning, they used many cameras and a treadmill to monitor their runners' gait. Additionally, there are hardly any running gait monitoring applications for real users.

1. Cost Effective

   • Using sensors is more cost effective over using motion capture
   • Sensors have similar accuracy to motion capture

2. Free from Environmental Constraints

   • Studies we've previously mentioned were limited to experimental conditions indoors
   • In this study, all the experiments are conducted in an actual running environment

3. User-friendly

   • It can be used in sports training
   • Easy to use(Similar to other existing running application)
   • We clearly vizualize statistical data so users can easily understand their personal running information

1. Provide personal Statistical Data

This application provides personal statistical data to the user. Once the user finishes their run, he/she reports their emotional and physical state by responding to the question, 'How was your run?'.

2. Help prevent running injuries

Runners can choose to use the data to self-correct their running posture or utilize it as reference material for a consultation with an medical expert (that could include a doctor, physical therapist, or sports trainer). So, this app could help prevent running injuries.

✔️ 4 vCPU
✔️ 4GB memory
✔️ 10GB distributed

✔️ OS: Ubuntu Desktop 20.04 LTS

✔️ ThingsBoard Version: thingsboard-3.4.3

✔️ Specific Settings:
   - ThingsBoard Queue Service: In memory (built in, default)
   - Dependency: openjdk-11-jdk, postgresql-12

Flutter: 3.7.1
Dart: 2.19.1
DevTools: 2.20.1
Xcode: 14.1
Android Studio: 2022.1
Android SDK: 33.0.0
VS Code: 1.73.1

Arduino IDE 2.0.3
|_ ArduinoJson (Version 6.20.1)
|_ Adafruit BusIO (Version 1.14.0)
|_ Adafruit GFX Library (Version 1.11.4)
|_ Adafruit MPU6050 (Version 2.2.3)
|_ Adafruit SSD1306 (Version 2.5.6)
|_ Adafruit Unified Sensor (Version 1.1.6)
|_ ThingsBoard (Version 0.9.4)
|_ PubSubClient (Version 2.8.0)
|_ ArduinoHttpClient (Version 0.3.2)
|_ ArduinoJson (Version 6.20.1)
|_ DHT sensor library for ESPx (Version 1.18.0)