Optical fiber sensors for posture monitoring, ulcer detection and control in a wheelchair: a state-of-the-art.

BACKGROUND: In the last ten years, the design and implementation of Optical Fiber Sensors (OFS) in biomedical applications have been discussed, with a focus on different subareas, such as body parameter monitoring and control of assistive devices. MATERIALS AND METHODS: A scoping review was performed including scientific literature (PubMed/Scopus, IEEE and Web of Science), patents (WIPO/Google Scholar), and commercial information. RESULTS: The main applications of OFS in the rehabilitation field for preventing future postural diseases and applying them in device controllers were discussed in this review. Physical characteristics of OFS, different uses, and applications of Polymer Optical Fiber pressure sensors are mentioned. The main postures used for posture monitoring analysis when the user is sitting are normal position, crooked back, high lumbar pressure, sitting on the edge of the chair, and crooked back, left position, and right position. Additionally, it is possible to use Machine Learning (ML) algorithms for posture classification, and device control such as Support Vector Machine, k-Nearest Neighbors, etc., obtaining accuracies above 97%. Moreover, the literature mentions wheelchair controllers and Graphical User Interfaces using pressure maps to provide feedback to the user. CONCLUSIONS: OFS have been used in several healthcare applications as well as postural and preventive applications. The literature showed an effort to implement and design accessible devices for people with disabilities and people with specific diseases. Alternatively, ML algorithms are widely used in this direction, leaving the door open for further studies that allow the application of real-time systems for posture monitoring and wheelchairs control.

IMPLICATIONS FOR REHABILITATIONPosture monitoring and ulcer detection systems are very useful to prevent or treat diseases related to bedsores or pressure ulcers using a different kind of electronic or optic instrumentation to improve the user's quality of life.The system characteristics using optical fiber sensors discussed in this review set an important precedent in the fabrication of low-cost systems for biomedical applications.

Enhancing Part-to-Part Repeatability of Force-Sensing Resistors Using a Lean Six Sigma Approach.

Polymer nanocomposites have found wide acceptance in research applications as pressure sensors under the designation of force-sensing resistors (FSRs). However, given the random dispersion of conductive nanoparticles in the polymer matrix, the sensitivity of FSRs notably differs from one specimen to another; this condition has precluded the use of FSRs in industrial applications that require large part-to-part repeatability. Six Sigma methodology provides a standard framework to reduce the process variability regarding a critical variable. The Six Sigma core is the DMAIC cycle (Define, Measure, Analyze, Improve, and Control). In this study, we have deployed the DMAIC cycle to reduce the process variability of sensor sensitivity, where sensitivity was defined by the rate of change in the output voltage in response to the applied force. It was found that sensor sensitivity could be trimmed by changing their input (driving) voltage. The whole process comprised: characterization of FSR sensitivity, followed by physical modeling that let us identify the underlying physics of FSR variability, and ultimately, a mechanism to reduce it; this process let us enhance the sensors' part-to-part repeatability from an industrial standpoint. Two mechanisms were explored to reduce the variability in FSR sensitivity. (i) It was found that the output voltage at null force can be used to discard noncompliant sensors that exhibit either too high or too low sensitivity; this observation is a novel contribution from this research. (ii) An alternative method was also proposed and validated that let us trim the sensitivity of FSRs by means of changing the input voltage. This study was carried out from 64 specimens of Interlink FSR402 sensors.

Liquid Level Sensor with Two FBGs Embedded in a PDMS Diaphragm: Analysis of the Linearity and Sensitivity.

This paper presents a fiber optic, liquid level sensor system based on a pair of fiber Bragg gratings (FBGs), embedded in a circular silicone (PDMS-polydimethylsiloxane) rubber diaphragm. The measurement principles of this sensor, whose diaphragm structure is about 2.2 mm thick with 45 mm in diameter, are introduced. To analyze the linearity and sensitivity of the sensor, the diaphragm was subjected to compression tests as well as to liquid level loading and unloading. The force and liquid level increase tests showed that inserting two FBGs (0.99453 for force and 0.99163 for liquid level) in the diaphragm resulted in a system with greater linearity than that with individual FBGs. This occurred where FBG1 showed 0.97684 for force and 0.98848 for liquid level and FBG2 presented 0.89461 for force and 0.93408 for liquid level. However, the compression and water level decrease tests showed that the system (R2 = 0.97142) had greater linearity with FBG2 (0.94123) and lower linearity with FBG1 (0.98271). Temperature characterization was also performed, and we found that sensitivity to FBG1 temperature variation was 11.73 pm/°C and for FGB2 it was 10.29 pm/°C. Temperature sensitivity was improved for both FBGs when compared with uncoated FBGs with typical values of 9.75 pm/°C. Therefore, the proposed FBG-based sensor system is capable of simultaneous measurement of force and temperature in a compact diaphragm-embedded system.

Gait Segmentation Method Using a Plantar Pressure Measurement System with Custom-Made Capacitive Sensors.

Gait analysis has been widely studied by researchers due to the impact in clinical fields. It provides relevant information on the condition of a patient's pathologies. In the last decades, different gait measurement methods have been developed in order to identify parameters that can contribute to gait cycles. Analyzing those parameters, it is possible to segment and identify different phases of gait cycles, making these studies easier and more accurate. This paper proposes a simple gait segmentation method based on plantar pressure measurement. Current methods used by researchers and clinicians are based on multiple sensing devices (e.g., multiple cameras, multiple inertial measurement units (IMUs)). Our proposal uses plantar pressure information from only two sensorized insoles that were designed and implemented with eight custom-made flexible capacitive sensors. An algorithm was implemented to calculate gait parameters and segment gait cycle phases and subphases. Functional tests were performed in six healthy volunteers in a 10 m walking test. The designed in-shoe insole presented an average power consumption of 44 mA under operation. The system segmented the gait phases and sub-phases in all subjects. The calculated percentile distribution between stance phase time and swing phase time was almost 60%/40%, which is aligned with literature reports on healthy subjects. Our results show that the system achieves a successful segmentation of gait phases and subphases, is capable of reporting COP velocity, double support time, cadence, stance phase time percentage, swing phase time percentage, and double support time percentage. The proposed system allows for the simplification of the assessment method in the recovery process for both patients and clinicians.