Wearable Near-Field Communication Sensors for Healthcare: Materials, Fabrication and Application

Printable and flexible integrated sensing systems for wireless healthcare

The rapid development of wearable sensing devices and artificial intelligence has enabled portable and wireless tracking of human health, fulfilling the promise of digitalized healthcare applications. To achieve versatile design and integration of multi-functional modules including sensors and data transmission units onto various flexible platforms, printable technologies emerged as some of the most promising strategies. This review first introduces the commonly utilized printing technologies, followed by discussion of the printable ink formulations and flexible substrates to ensure reliable device fabrication and system integration. The advances of printable sensors for body status monitoring are then discussed. Moreover, the integration of wireless data transmission via printable approaches is also presented. Finally, the challenges in achieving printable sensing devices and wireless integrated systems with competitive performances are considered, so as to realize their practical applications for personalized healthcare.

Physical Therapists' Acceptance of a Wearable, Fabric-Based Sensor System (Motion Tape) for Use in Clinical Practice: Qualitative Focus Group Study

BACKGROUND

Low back pain (LBP) is a costly global health condition that affects individuals of all ages and genders. Physical therapy (PT) is a commonly used and effective intervention for the management of LBP and incorporates movement assessment and therapeutic exercise. A newly developed wearable, fabric-based sensor system, Motion Tape, uses novel sensing and data modeling to measure lumbar spine movements unobtrusively and thus offers potential benefits when used in conjunction with PT. However, physical therapists' acceptance of Motion Tape remains unexplored.

OBJECTIVE

The primary aim of this research study was to evaluate physical therapists' acceptance of Motion Tape to be used for the management of LBP. The secondary aim was to explore physical therapists' recommendations for future device development.

METHODS

Licensed physical therapists from the American Physical Therapy Association Academy of Leadership Technology Special Interest Group participated in this study. Overall, 2 focus groups (FGs; N=8) were conducted, in which participants were presented with Motion Tape samples and examples of app data output on a poster. Informed by the Technology Acceptance Model, we conducted semistructured FGs and explored the wearability, usefulness, and ease of use of and suggestions for improvements in Motion Tape for PT management of LBP. FG data were transcribed and analyzed using rapid qualitative analysis.

RESULTS

Regarding wearability, participants perceived that Motion Tape would be able to adhere for several days, with some variability owing to external factors. Feedback was positive for the low-profile and universal fit, but discomfort owing to wires and potential friction with clothing was of concern. Other concerns included difficulty with self-application and potential skin sensitivity. Regarding usefulness, participants expressed that Motion Tape would enhance the efficiency and specificity of assessments and treatment. Regarding ease of use, participants stated that the app would be easy, but data management and challenges with interpretation were of concern. Physical therapists provided several recommendations for future design improvements including having a wireless system or removable wires, customizable sizes for the tape, and output including range of motion data and summary graphs and adding app features that consider patient input and context.

CONCLUSIONS

Several themes related to Motion Tape's wearability, usefulness, and ease of use were identified. Overall, physical therapists expressed acceptance of Motion Tape's potential for assessing and monitoring low back posture and movement, both within and outside clinical settings. Participants expressed that Motion Tape would be a valuable tool for the personalized treatment of LBP but highlighted several future improvements needed for Motion Tape to be used in practice.

Advances in wearable electrochemical antibody-based sensors for cortisol sensing

Cortisol is a crucial hormone involving many physiological processes. Hence, cortisol detection is essential. This review highlights the key progress made on wearable electrochemical sensors using antibodies. It covers the design, principle, and electroanalytical methodology for detecting cortisol noninvasively. This article also analyzes and collects the analytical performances of electrochemical cortisol sensors. The development of these sensors continues to face challenges such as biofouling, sample management, sensitivity, flexibility, stability, and recognition layer performance. It is also necessary to develop a sensitive electrode and material. This article also presents potential strategies for designing antibody electrodes and provides examples of sensing systems. Additionally, it discusses the challenges in translating research into practical applications.

AAL and Internet of Medical Things for Monitoring Type-2 Diabetic Patients

Due to an aging population, assisted-care options are required so that senior citizens may maintain their independence at home for a longer time and rely less on caretakers. Ambient Assisted Living (AAL) encourages the creation of solutions that can help to optimize the environment for senior citizens with assistance while greatly reducing their challenges. A framework based on the Internet of Medical Things (IoMT) is used in the current study for the implementation of AAL technology to help patients with Type-2 diabetes. A glucose oxide sensor is used to monitor diabetic elderly people continuously. Spectrogram images are created from the recorded data from the sensor to assess and detect aberrant glucose levels. DenseNet-169 examines and analyzes the spectrogram pictures, and messages are sent to caregivers when aberrant glucose levels are detected. The current work describes both the spectrogram image analysis and the signal-to-spectrogram generating method. The study presents a future perspective model for a mobile application for real-time patient monitoring. Benchmark metrics evaluate the application's performances, including sensitivity, specificity, accuracy, and F1-score. Several cross--validations are used to evaluate the model's performance. The findings demonstrate that the proposed model can correctly identify patients with abnormal blood glucose levels.