Modeling, Fabrication and Integration of Wearable Smart Sensors in a Monitoring Platform for Diabetic Patients

Analyzing the Thermal Characteristics of Three Lining Materials for Plantar Orthotics

INTRODUCTION

The choice of materials for covering plantar orthoses or wearable insoles is often based on their hardness, breathability, and moisture absorption capacity, although more due to professional preference than clear scientific criteria. An analysis of the thermal response to the use of these materials would provide information about their behavior; hence, the objective of this study was to assess the temperature of three lining materials with different characteristics.

MATERIALS AND METHODS

The temperature of three materials for covering plantar orthoses was analyzed in a sample of 36 subjects (15 men and 21 women, aged 24.6 ± 8.2 years, mass 67.1 ± 13.6 kg, and height 1.7 ± 0.09 m). Temperature was measured before and after 3 h of use in clinical activities, using a polyethylene foam copolymer (PE), ethylene vinyl acetate (EVA), and PE-EVA copolymer foam insole with the use of a FLIR E60BX thermal camera.

RESULTS

In the PE copolymer (material 1), temperature increases between 1.07 and 1.85 °C were found after activity, with these differences being statistically significant in all regions of interest (p < 0.001), except for the first toe (0.36 °C, p = 0.170). In the EVA foam (material 2) and the expansive foam of the PE-EVA copolymer (material 3), the temperatures were also significantly higher in all analyzed areas (p < 0.001), ranging between 1.49 and 2.73 °C for EVA and 0.58 and 2.16 °C for PE-EVA. The PE copolymer experienced lower overall overheating, and the area of the fifth metatarsal head underwent the greatest temperature increase, regardless of the material analyzed.

CONCLUSIONS

PE foam lining materials, with lower density or an open-cell structure, would be preferred for controlling temperature rise in the lining/footbed interface and providing better thermal comfort for users. The area of the first toe was found to be the least overheated, while the fifth metatarsal head increased the most in temperature. This should be considered in the design of new wearables to avoid excessive temperatures due to the lining materials.

Plantar pressure measurement in diabetic foot disease: A scoping review

AIMS/INTRODUCTION

Patients with a healed diabetic foot ulcer (DFU) have a 40% risk of ulcer recurrence within a year. New and effective measures to prevent DFU recurrence are essential. We aimed to highlight emerging trends and future research opportunities in the use of plantar pressure measurement to prevent DFU recurrence.

MATERIALS AND METHODS

Our scoping review protocol was drafted using the Preferred Reporting Items for Systematic Reviews and Meta-analysis - Scoping Review protocol. Peer-reviewed, English-language papers were included that addressed both plantar pressure measurement and diabetic foot disease, either as primary studies that have advanced the field or as review papers that provide summaries and/or opinion on the field as a whole, as well as specific papers that provide guidelines for future research and advancement in the field.

RESULTS

A total of 24 eligible publications were identified in a literature search using PubMed. A further 36 eligible studies were included after searching the references sections of these publications, leaving a total of 60 publications included in this scoping review.

CONCLUSIONS

Plantar pressure measurement can and will play a major role in the prevention of DFU. There is already a strong, albeit limited, evidence base in place to prove its benefit in reducing DFU recurrence. More research is required in larger populations, using remote monitoring in real-world settings, and with improved technology.

A Deep Learning-Based Platform for Workers' Stress Detection Using Minimally Intrusive Multisensory Devices

The advent of Industry 4.0 necessitates substantial interaction between humans and machines, presenting new challenges when it comes to evaluating the stress levels of workers who operate in increasingly intricate work environments. Undoubtedly, work-related stress exerts a significant influence on individuals' overall stress levels, leading to enduring health issues and adverse impacts on their quality of life. Although psychological questionnaires have traditionally been employed to assess stress, they lack the capability to monitor stress levels in real-time or on an ongoing basis, thus making it arduous to identify the causes and demanding aspects of work. To surmount this limitation, an effective solution lies in the analysis of physiological signals that can be continuously measured through wearable or ambient sensors. Previous studies in this field have mainly focused on stress assessment through intrusive wearable systems susceptible to noise and artifacts that degrade performance. One of our recently published papers presented a wearable and ambient hardware-software platform that is minimally intrusive, able to detect human stress without hindering normal work activities, and slightly susceptible to artifacts due to movements. A limitation of this system is its not very high performance in terms of the accuracy of detecting multiple stress levels; therefore, in this work, the focus was on improving the software performance of the platform, using a deep learning approach. To this purpose, three neural networks were implemented, and the best performance was achieved by the 1D-convolutional neural network with an accuracy of 95.38% for the identification of two levels of stress, which is a significant improvement over those obtained previously.

Screen-Printed Textile-Based Electrochemical Biosensor for Noninvasive Monitoring of Glucose in Sweat

Wearable sweat biosensors for noninvasive monitoring of health parameters have attracted significant attention. Having these biosensors embedded in textile substrates can provide a convenient experience due to their soft and flexible nature that conforms to the skin, creating good contact for long-term use. These biosensors can be easily integrated with everyday clothing by using textile fabrication processes to enhance affordable and scalable manufacturing. Herein, a flexible electrochemical glucose sensor that can be screen-printed onto a textile substrate has been demonstrated. The screen-printed textile-based glucose biosensor achieved a linear response in the range of 20-1000 µM of glucose concentration and high sensitivity (18.41 µA mM^-1 cm^-2, R² = 0.996). In addition, the biosensors show high selectivity toward glucose among other interfering analytes and excellent stability over 30 days of storage. The developed textile-based biosensor can serve as a platform for monitoring bio analytes in sweat, and it is expected to impact the next generation of wearable devices.

Human Postures Recognition by Accelerometer Sensor and ML Architecture Integrated in Embedded Platforms: Benchmarking and Performance Evaluation

Embedded hardware systems, such as wearable devices, are widely used for health status monitoring of ageing people to improve their well-being. In this context, it becomes increasingly important to develop portable, easy-to-use, compact, and energy-efficient hardware-software platforms, to enhance the level of usability and promote their deployment. With this purpose an automatic tri-axial accelerometer-based system for postural recognition has been developed, useful in detecting potential inappropriate behavioral habits for the elderly. Systems in the literature and on the market for this type of analysis mostly use personal computers with high computing resources, which are not easily portable and have high power consumption. To overcome these limitations, a real-time posture recognition Machine Learning algorithm was developed and optimized that could perform highly on platforms with low computational capacity and power consumption. The software was integrated and tested on two low-cost embedded platform (Raspberry Pi 4 and Odroid N2+). The experimentation stage was performed on various Machine Learning pre-trained classifiers using data of seven elderly users. The preliminary results showed an activity classification accuracy of about 98% for the four analyzed postures (Standing, Sitting, Bending, and Lying down), with similar accuracy and a computational load as the state-of-the-art classifiers running on personal computers.

Artificial Intelligence Methodologies Applied to Technologies for Screening, Diagnosis and Care of the Diabetic Foot: A Narrative Review

Diabetic foot syndrome is a multifactorial pathology with at least three main etiological factors, i.e., peripheral neuropathy, peripheral arterial disease, and infection. In addition to complexity, another distinctive trait of diabetic foot syndrome is its insidiousness, due to a frequent lack of early symptoms. In recent years, it has become clear that the prevalence of diabetic foot syndrome is increasing, and it is among the diabetes complications with a stronger impact on patient's quality of life. Considering the complex nature of this syndrome, artificial intelligence (AI) methodologies appear adequate to address aspects such as timely screening for the identification of the risk for foot ulcers (or, even worse, for amputation), based on appropriate sensor technologies. In this review, we summarize the main findings of the pertinent studies in the field, paying attention to both the AI-based methodological aspects and the main physiological/clinical study outcomes. The analyzed studies show that AI application to data derived by different technologies provides promising results, but in our opinion future studies may benefit from inclusion of quantitative measures based on simple sensors, which are still scarcely exploited.