Inertial Measurement Unit Projects for ME, MTech, Masters, MS abroad, and PhD students. These Inertial Measurement Unit ieee projects are implemented with future work and extension for final year students with research paper writing and publishing. These research projects guide final year students to learn, practice, and complete their academic submissions successfully. Each project includes complete source code, project report, PPT, a tutorial, documentation, and a research paper.
Latest Inertial Measurement Unit Projects
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Gaitmap An Open Ecosystem for IMU-Based Human Gait Analysis and Algorithm Benchmarking
This project creates a free and open software system called gaitmap to study how people walk using small motion sensors on their feet. It gives researchers ready-to-use tools, datasets, and examples to test and compare their methods. This helps speed up medical research on movement problems and makes gait analysis easier for everyone to use. -
Wearable Accelerometer and Gyroscope Sensors for Estimating the Severity of Essential Tremor
This project develops a wearable device to measure hand tremors in people with essential tremor. The device records movements while participants draw a spiral. Data from sensors are used to estimate tremor severity and classify tremor types. The method showed high accuracy and could help doctors monitor tremors and test new treatments. -
Wearable Loop Sensors for Knee Flexion Monitoring Dynamic Measurements on Human Subjects
This project develops new wearable sensors that can measure how joints bend. The sensors were tested on people performing walking and bending movements, as well as on a model limb. The results show the sensors are accurate, safe, and do not restrict movement. This work moves closer to fully wearable devices for monitoring joint motion. -
Human Activity Recognition Based on Wireless Electrocardiogram and Inertial Sensors
This project uses wearable sensors to monitor a person’s heart activity and body movements. It applies deep learning to identify what activity a person is doing, such as running, sitting, or climbing stairs. The system achieved very high accuracy, showing that combining heart signals and motion data gives better results. It helps in tracking health and daily activity remotely with precision.
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