Textile and electronic components are critical elements of most wearable technologies (wearables); both components deteriorate at different rates depending on factors of use, care, and user handling. The differences in mechanical performance characteristics (MPC) (i.e., abrasion, elongation, and bursting strength) of these components create a challenge for researchers and product designers to develop user-centric and economical wearables. For example, athletic wearables made of nylon/spandex knit blends exhibit drastically different MPC from minimal fiber content changes (1- 10%). However, the wearable's end-use remains constant. This article presents ideas and methods for testing MPC and how to evaluate results for different end-use cases. Designing for end-user activities also highlights these performance differences because specific, end-uses drive textiles design, which may or may not be the wearable design's end-use. Three American Society for Testing and Materials (ASTM) test methods were used to test MPC of athletic fabrics and soft robotic sensors (SRS) to determine the abrasion resistance, elongation, and bursting strength of these components and two-tail ttest comparisons were performed on the results. The SRS's durability is less than the textiles they are integrated into, and with no standards for MPC testing on SRS, it can be unclear how long a sensor will last. Such methods need to be developed so product developers can find efficient combinations of fibers and electronic components to ensure user-centric functionality, wearer comfort, extended product longevity, and overall consumer satisfaction.
Dielectric elastomers have emerged in recent years as a smart material capable of acting as an actuator, a sensor, or a generator. When used as a sensor, this soft, flexible material exhibits a change in capacitance as it is deformed in both compression and tension. This has led to the adaptation of dielectric elastomer sensors in the wearable technology space, where careful sensor placement can enable the measurement of biomechanical movement. However, these sensors may not be measured using traditional capacitance measurement techniques due to their increased electrode resistance. Thus, a low frequency, low voltage capacitive measurement methodology needs to be derived for these sensors to thrive in wearable applications. In this work, we propose such a methodology which utilizes phase detection with the Goertzel algorithm. Traditionally used for tone detection, the Goertzel algorithm provides an efficient method for recovering individual terms of the DFT. Our sensing methodology is integrated into a low-cost microcontroller and integrated with a wireless microcontroller to enable remote measurement of the dielectric elastomers. The open sourcing of this device may jump-start the widespread adoption of dielectric elastomers as biomechanical sensors.
Agroecosystems compose large economic sectors in dominantly agriculture-based societies. Availability and management of water resources have a huge influence on the sustainability of agroecosystems. Low soil moisture is a major constraint on crop growth due to its vital role in providing crops with sufficient nutrition for root uptake. Current methodologies in precision agriculture are insufficient for direct soil moisture sensing since reflected shortwave solar radiation and infrared long-wave emission can only provide information about surface characteristics. While microwave signals are known to be highly sensitive to water within plants and soil, its implementation from small Unmanned Aircraft Systems (UAS) platforms are at relatively low technological readiness level compared to the use of shortwave / longwave optical sensors. In this paper, we summarize our efforts to apply radio frequency (RF) / microwave remote sensing from UAS for water utilization in agroecosystems. Recently, we developed a comprehensive UAS-based RF testbed, including a microwave radiometer, a scatterometer, wideband ground penetrating radar system as well as Signals of Opportunity (SoOp) receivers. These instruments operate from UAS platforms and use the microwave / radio wave portions of the spectrum. The testbed is accompanied with proximal sensing via autonomous unmanned ground vehicles that acquire in- situ soil moisture and vegetation geophysical parameters to provide appropriate datasets for training and testing physics aware, machine learning-based models. In this paper, we introduce the RF sensing framework that can enable non-intrusive high-resolution soil moisture estimates at multiple depths of soil via UAS-based active / passive / SoOp RF instruments.
Adaptive cruise control (ACC), a common feature in an autonomous vehicle, is intended to automatically adjust the vehicle speed and maintain a safe distance from its preceding vehicle to avoid a collision. The main challenge is to filter the sensor data accurately, and the control system can make a decision quickly. This paper proposed a control method for ACC using the Extended Kalman filter (EKF) and a Proportional Integral Derivative (PID) controller, which can estimate the acceleration or braking of the preceding vehicle by adjusting the speed of the following vehicle. The proposed control method is assessed under various PID parameters using a Genetic Algorithm (GA) to optimize the ACC system using four loss metrics: (1) throttle loss, which accounts for fuel usage, and is proportional to the throttle setting; (2)ride quality, which is penalized by an excessive jerk (the first derivative of acceleration); (3) a distance penalty, which measures how far compared to the safe distance
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