AI-Enhanced Multimodal Sensing for Early Detection of Diabetic Foot Ulcers: A Cost-Effective Wearable Solution

Abstract: This paper presents a novel wearable IoT system integrating multi-modal plantar pressure mapping and thermal imaging for early detection of diabetic foot ulcers (DFUs). Our solution combines six FlexiForce A502 pressure sensors (0-500 kPa range) and four MLX90614 infrared temperature sensors (±0.5◦C accuracy) with a hybrid CNN-BiLSTM deep learning architecture, achieving 94.2% prediction accuracy on a clinical dataset of 75 diabetic patients over 90 days. The system demonstratessignificantimprovementsoverexistingapproaches,reduc- ing false positives by 32% compared to single-modality systems whilemaintainingaproductioncostof$14.80perunit.Real-time edge processing on an ESP32-C6 microcontroller enables 28 ms inference latency, with piezoelectric energy harvesting extending operational lifetime to 72 hours. Clinical validation shows 40% reductioninhospitalizationratesthroughearlyintervention,with potentialannualhealthcaresavingsexceeding$9billionintheUS alone.Thisworkbridgesthegapbetweenclinic-gradediagnostics and community healthcare through explainable AI and costeffective wearable technology