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@article{188494,
        author = {Ranjeet Rabade and Saksham Patil and Harshad Rathod and Siddhant Phadtare and Prof. Vishal Nayakwadi},
        title = {Wearable AI-driven ClosePosture and Muscle Fatigue Prevention System Using Fused IMU and Surface EMG with Adaptive On-Device Actuation.},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {7},
        pages = {1792-1798},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=188494},
        abstract = {Internal sensors and wearables Edge AI is introducing the possibilities of a whole new dimension of monitoring our movements and muscle activity. In this paper, I will speak about the creation of a cool smart bio-signal monitor, which can be used to collect the information about IMUs and surface EMG pads simultaneously on the upper and lower back. The device uses MCU with TinyML, which implies that it can crunch the data on the device itself and identify muscle fatigue or unhealthy posture at the time. A major advantage of it is the automatic enhancement of electrode impedance - this prevents the crashing out of the signal even after a long period of use. It is also a closed-loop system that is capable of producing vibration or slight electric shocks (FES) within less than 200 ms. We concluded that we can assess the degree of fatigue in the person and adjust his/her posture without even having to use the cloud and still remain energy-efficient. Smart Wearables that assist in maintaining a healthy posture, establishing healthy habits, and monitoring muscle wellness have a strong foundation on this platform.},
        keywords = {Wearable Device, sEMG, IMU, TinyML, Posture Correction, Muscle Fatigue Detection.},
        month = {December},
        }