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 demon- stratessignificantimprovementsoverexistingapproaches,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 cost- effective wearable technology.
Introduction
1. Clinical Context
Diabetes affects over 537 million adults worldwide.
34% develop diabetic foot complications; DFUs are the leading cause of diabetes-related hospitalizations and 20% can result in amputation.
Existing diagnostic tools like MRI and pressure mats are effective but costly and inaccessible in low-resource settings.
2. Limitations of Current Technologies
Pressure-only sensors miss early inflammation.
Temperature-based systems lack localization.
Vibration alerts detect ulcers too late.
3. Proposed Innovations
Multimodal Sensor Fusion: Combines pressure (100 Hz) and bilateral foot temperature (0.1°C resolution).
Edge-optimized CNN-BiLSTM AI: Processes spatial-temporal patterns with 94.2% accuracy on low-power embedded hardware (ESP32).
Clinical Decision Support: Integrates with hospital EHRs (HL7/FHIR) for real-time risk assessment.
4. Literature Insights
Prior solutions lacked either:
Multimodal sensing,
Advanced AI for sequential data, or
Real-time/edge computing support.
The proposed hybrid CNN-BiLSTM model addresses these gaps by offering accurate, low-latency, interpretable diagnostics.
Power: LiPo battery with piezoelectric energy harvesting.
AI Model: CNN-BiLSTM with attention, trained for 3-class classification (low, medium, high risk).
6. Experimental Results
Accuracy: 94.2%
Sensitivity: 92.4%
Specificity: 95.1%
F1-Score: 0.93
Comparison: Outperformed previous models (Bai et al., Berugu et al., and S?ahin & Cingil).
7. Clinical Impact
88% of high-risk patients correctly identified within 90 days.
40% reduction in hospitalizations (from 18.7 to 11.2 days/year).
Cost: ~$0.23/day per patient vs. $3,120/year for standard care.
Conclusion
Thisresearchdemonstratesthefeasibilityandimpactof an AI-enhanced, multimodal wearable system for early de- tection of diabetic foot ulcers (DFUs). By integrating plantar pressure mapping and thermal imaging with a hybrid CNN- BiLSTM deep learning model, the proposed solution achieves a high prediction accuracy of 94.2% and significantly reduces false positives compared to single-modality systems. The system’s real-time edge processing, low production cost, and energy harvesting capabilities make it practical for continu- ous, community-based monitoring and scalable deployment in resource-limited settings.
The clinical validation on 75 diabetic patients over 90 days highlightsseveralkeybenefits:a40%reductioninhospitaliza- tionrates,earlierriskstratification,andsubstantialcostsavings compared to conventional MRI-based screening. The explain- able AI framework, with Grad-CAM visualizations, not only enhancesclinicaltrustbutalsosupportstargetedinterventions, potentially preventing ulcer progression and amputations.
Despite these strengths, some limitations remain. Sensor drift requires regular calibration, and the current study cohort hadlimitedrepresentationofneuropathicpatients.Addressing these will be a focus of future work, alongside the planned integration of photoplethysmography (PPG) for blood flow assessment, federated learning for privacy-preserving model updates, and expansion to multi-center clinical trials.
Insummary,thisworkbridgesthegapbetweenhospitalgradediagnosticsandaccessiblepreventivecarefordiabeticfootcomplications.Theplatform’sflexibility,accuracy,and cost-effectiveness position it as a promising tool for reducing theglobalburdenofDFUs.Futuredirectionswillinclude regulatory validation, integration with smart bandage systems forclosed-loopcare,andopen-sourcereleaseofcodeanddatasetstoacceleratefurtherresearchandreal-worldadoption. ThisworkdemonstratesthatedgeAIenabledmultimodalwearablescanachievehospitalgradeDFUpredictionaccuracyatcommunityhealthcarecosts.Ourclinicalvalidationwith75patientsover90daysconfirmsthesystem’spotentialtotransformdiabeticfootcarethrough:
• Early detection of pre-ulcerative states (14 days median lead time)
• PersonalizedriskstratificationusingexplainableAI
• Seamlessintegrationwithexistinghealthcareinfrastruc- ture
Future work will focus on multi-center trials (n=500+) and regulatory approval pathways for clinical deployment.
References
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