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Clinical; Diabetic foot; Ulcer

Introduction

In this article, we focus on the design and implementation of a shoe sensor prototype to study diabetic foot disorders. We have tried to use low-cost core technology because cost is a significant barrier to the adoption of new technologies. This approach has made Raspberry Pi an attractive option for controlling data collection with low purchase cost, private python environment with LAN connection, multiple USB ports and desktop environment significantly reduces development time [1, 2]. The Rasbian operating system has proven to be a very stable data collection platform with few background tasks running, greatly improving the stability of critical tasks compared to PCs or Apple computers. Diabetes is a chronic endocrine disease that can develop at any stage of life and affects the body's production and/or use of insulin, leading to blood sugar regulation. Unless well controlled, it can cause vascular and neurological disease throughout the body, often leading to serious comorbidities such as retinopathy, kidney failure, and diabetic foot disorders. Diabetic foot disorder is classified as a medical emergency because it can become severe enough to require amputation and is the second most feared diabetic comorbidity after blindness. Diabetes is also a financial burden, and now the NHS consumes 10% of its annual budget, which is expected to rise to 17% by 2035 in direct costs [3-5]. Current best practice recommends that patients perform daily foot monitoring supported by regular physical examination by trained professionals, with the use of non-contact temperature measurement for those most at risk. Although non-contact thermography reduces the risk of ulcers, it is still a significant risk. Many unique parameters such as temperature, pressure, or force in various forms, gait changes, and blood flow have been shown to be indicative of ulcers, but none are predictive of ulcers. Commercial devices such as the Sensoria socks, which incorporate three force sensors and a three-axis accelerometer, are available as base devices. Devices such as the TekScan Mat and F-Scan are intended exclusively for laboratory or clinical use. We present a new portable and scalable synthetic sensor system, capable of measuring multiple factors simultaneously, providing an alternative multifactorial pathway for tissue failure prediction. This device increases the number of metrics measured at the same time in predicate devices from 3 to 8 [6-8]. Biological impedance is a complex measurement consisting of real and imaginary components. The extracellular fluid forms the resistive path while the intracellular fluid forms the capacitive component with the plasma membrane in between acting as the dielectric. Inflammation is a systemic response to soft tissue injury as increased blood flow and vascular permeability lead to extravasation. Fluid entering the intracellular space changes the balance of resistive and capacitive pathways. Impedance tests the response of a material to a range of induced frequencies, with phase difference and gain as measurements. The outer layer of the skin, the stratum corneum, consists of a dense layer of dead cells with a high resistivity. Because the thickness, humidity, density and activity of sweat glands vary from person to person and are affected by disease, the skin's resistance is also very different. To overcome this, techniques such as peels and/or conductive gels have been used to normalize resistance to skin contact. Short-term use of these means of contact is a minor inconvenience, but they are known to cause skin irritation if used for long periods of time [9,10]. By using capacitive coupling, we have eliminated the need for electrical contact media, reducing the risk of skin irritation with prolonged use.

Discussion

The occluded blood flow test was performed with an occlusion time of 1 min, providing an appropriate change in signal that could be measured to demonstrate the effectiveness of the device with minimal volunteer discomfort. As can be, the signal frequency has changed from 0.16 Hz before occlusion to -0.26 Hz after occlusion in the given example, with some variation in the amplitude of the phase measurement. Similar to the post-obstruction reactive hypertension test, it is noted that pretest stabilization takes several minutes, this is due to the time required to normalize O2, CO2, NO and tissue metabolites after restoration of blood flow. We are currently investigating the usefulness of this new metric because we can observe a measurable impact in tissues in vivo.

Conclusions

Previous devices incorporate up to three measurement modes. The device shown measures eight, 42 individual, bilateral sensors plus ambient temperature and humidity. This provides an opportunity to assess the interdependence in the metrics used and thus to quantify the value of each measure and the multifactor detection algorithm. Evaluating the interrelationships of some factors is always difficult because multifactor data cannot be measured without restriction, this device will overcome this limitation. With eight measurements taken, it is now possible to collect comprehensive data from the footwear environment. This development will lead to a better understanding of the biological mechanisms and local environmental considerations that influence foot health. With the growing understanding of diabetic foot-related problems, it will be necessary to modify sensor networks to tailor them to specific investigations. The instrument is a scalable and adaptive measuring system that can be modified to optimize sensor selections and positions as needed. The standout instrument demonstrated multi-element data measurement using both analog, 10-bit and I2C interfaces in real time. These interfaces can be quickly adapted to measure other sensors as required by each investigator allowing for customization of the measurement network.

Conflict of Interest

None

Acknowledgment

None

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