Abnormal Gait Detection using Wearable IMU Sensor via IoT and Alert for Condition Monitoring of Elderly People

Authors

  • Muhammad Haziq Mohd Zakhiruddin Electrical Engineering Studies, College of Engineering, Universiti Teknologi MARA, Cawangan Pulau Pinang, 13500 Permatang Pauh, Pulau Pinang, Malaysia
  • Mohd Hanapiah Abdullah Electrical Engineering Studies, College of Engineering, Universiti Teknologi MARA, Cawangan Pulau Pinang, 13500 Permatang Pauh, Pulau Pinang, Malaysia
  • Syahrul Afzal Che Abdullah School of Electrical Engineering, College of Engineering, Universiti Teknologi MARA Shah Alam, 40450 Shah Alam, Selangor, Malaysia
  • Aini Hafizah Mat Saod Electrical Engineering Studies, College of Engineering, Universiti Teknologi MARA, Cawangan Pulau Pinang, 13500 Permatang Pauh, Pulau Pinang, Malaysia
  • Zuraidi Saad Electrical Engineering Studies, College of Engineering, Universiti Teknologi MARA, Cawangan Pulau Pinang, 13500 Permatang Pauh, Pulau Pinang, Malaysia
  • Zainal Hisham Che Soh Electrical Engineering Studies, College of Engineering, Universiti Teknologi MARA, Cawangan Pulau Pinang, 13500 Permatang Pauh, Pulau Pinang, Malaysia

Keywords:

Gait detection, elderly healthcare internet of things (IoT), inertial measurement unit (IMU) sensor

Abstract

Gait detection is a type of analysis that can detect patterns of walking in a person. In this research, a wearable sensor is used to detect the walking pattern of the user. This wearable sensor will alert the user if abnormal gait is detected. Result from this project is the gait analysis is performed on stance and swing phases of a walk. Sensor 6-axis Inertial Measurement Unit (IMU) decided to be used in this device with Arduino Nano 33 IoT and analysis is done based on the results monitored in ThingSpeak. The data obtained from sensor in Arduino Nano 33 IoT is sent to ThingSpeak to be converted into csv file which is then readable by MATLAB software. Result obtained from the analysis is then concluded into the programming code of Arduino which then the Arduino board can identify the gait by itself based on the analysis done after collecting data.

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Author Biographies

Muhammad Haziq Mohd Zakhiruddin, Electrical Engineering Studies, College of Engineering, Universiti Teknologi MARA, Cawangan Pulau Pinang, 13500 Permatang Pauh, Pulau Pinang, Malaysia

2020968791@student.uitm.edu.my

Mohd Hanapiah Abdullah, Electrical Engineering Studies, College of Engineering, Universiti Teknologi MARA, Cawangan Pulau Pinang, 13500 Permatang Pauh, Pulau Pinang, Malaysia

hanapiah801@uitm.edu.my

Syahrul Afzal Che Abdullah, School of Electrical Engineering, College of Engineering, Universiti Teknologi MARA Shah Alam, 40450 Shah Alam, Selangor, Malaysia

bekabox181343@uitm.edu.my

Aini Hafizah Mat Saod, Electrical Engineering Studies, College of Engineering, Universiti Teknologi MARA, Cawangan Pulau Pinang, 13500 Permatang Pauh, Pulau Pinang, Malaysia

aini.hafizah@uitm.edu.my

Zuraidi Saad, Electrical Engineering Studies, College of Engineering, Universiti Teknologi MARA, Cawangan Pulau Pinang, 13500 Permatang Pauh, Pulau Pinang, Malaysia

zuraidi570@uitm.edu.my

Zainal Hisham Che Soh, Electrical Engineering Studies, College of Engineering, Universiti Teknologi MARA, Cawangan Pulau Pinang, 13500 Permatang Pauh, Pulau Pinang, Malaysia

zainal872@uitm.edu.my

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Published

2024-12-19

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