eng Oriental Scientific Publishing Company Biomedical and Pharmacology Journal 0974-6242 2025-02-20 18 March Spl Edition 229 243 10.13005/bpj/3084 64146 article Sandeep Kumar Pandey 1 Geetika Srivastava 1 Department of Physics and Electronics Dr. Rammanohar Lohia Avadh University, Ayodhya, India Diabetic Sensorimotor Polyneuropathy (DSPN) is a common complication of diabetes, significantly increasing the risk of diabetic foot ulcers and potential amputations. Timely monitoring and early detection of DSPN severity are crucial for prevention. Recent advancements in Machine Learning (ML) have led to highly accurate diagnostic models in the medical field and with the advancements in emerging edge devices these models offer patient-friendly solutions for continuous monitoring. Developing edge-compatible devices necessitates less complex ML models to optimize power consumption, resources, and processing speed. The choice of activation function is critical, as it directly impacts model complexity and performance. While complex data requires sophisticated functions to maintain accuracy, resource-constrained edge platforms demand a balance between complexity and effectiveness. This study presents a performance comparison of hardware-implemented Neural Network (NN) classifiers utilizing various linear and non-linear activation functions for Electromyography (EMG)-based DSPN classification, tested on the ZCU102 FPGA board. Results indicate that the NN employing the ReLU activation function achieved 78% accuracy with only 4.33 W power dissipation, a time delay of 20.1 mS, and resource utilization of 70,134 Look Up Tables (LUT) and 123 Block RAM (BRAM). These findings demonstrate that ReLU-based NNs offer better power efficiency, resource utilization, and speed compared to other activation functions for EMG-based DSPN classification. These insights serve as a valuable reference for researchers developing hardware-friendly NN models for edge-based ML applications in biomedical devices. https://biomedpharmajournal.org/vol18marchspledition/comparative-study-of-hardware-footprints-of-neural-network-activation-functions-for-emg-based-diabetic-sensorimotor-polyneuropathy-severity-classifier/ Activation Function DSPN FPGA ML NN