Cough is an important symptom in children with acute and chronic respiratory disease. Daily cough is common in Cystic Fibrosis (CF) and increased cough is a symptom of pulmonary exacerbation. To date, cough assessment is primarily subjective in clinical practice and research. Attempts to develop objective, automatic cough counting tools have faced reliability issues in noisy environments and practical barriers limiting long-term use. This single-center pilot study evaluated usability, acceptability and performance of a mechanoacoustic sensor (MAS), previously used for cough classification in adults, in 36 children with CF over brief and multi-day periods in four cohorts. Children whose health was at baseline and who had symptoms of pulmonary exacerbation were included. We trained, validated, and deployed custom deep learning algorithms for accurate cough detection and classification from other vocalization or artifacts with an overall area under the receiver-operator characteristic curve (AUROC) of 0.96 and average precision (AP) of 0.93. Child and parent feedback led to a redesign of the MAS towards a smaller, more discreet device acceptable for daily use in children. Additional improvements optimized power efficiency and data management. The MAS's ability to objectively measure cough and other physiologic signals across clinic, hospital, and home settings is demonstrated, particularly aided by an AUROC of 0.97 and AP of 0.96 for motion artifact rejection. Examples of cough frequency and physiologic parameter correlations with participant-reported outcomes and clinical measurements for individual patients are presented. The MAS is a promising tool in objective longitudinal evaluation of cough in children with CF.

Soft Pneumatic Actuators (SPAs) have recently become popular for use as fingers in robotic hands because of their inherent compliance, low cost, and ease of construction. We seek to overcome two key limitations which limit SPAs' abilities to grasp and manipulate objects: 1) Current SPAs lack position or force sensor feedback, which prevents controlling them precisely (e.g. to achieve a hand preshape or apply a specified pushing force), and 2) the tip of the SPA is compliant and has high friction against common surfaces, causing the SPA to stick against surfaces when grasping objects from above. To overcome the first limitation we propose methods to integrate soft eGaIn sensors into SPAs and controllers that use these sensors' feedback for position and force control. To overcome the second limitation, we explore embedding rigid fingernails into the tip of the SPA so that the finger does not stick against surfaces and can wedge under objects. Our experiments suggest that we can achieve low steady-state error and overshoot in position and force using feed-forward models that relate pressure, force, and curvature along with a PID controller. We also compare several fingernail designs and show that the best-performing design significantly outperforms having no fingernails when grasping a set of common objects from a table.

This work demonstrates an all-elastomer MEMS capacitive strain sensor with high dynamic range (5000:1), and features an inexpensive molding microfabrication process. The sensor is comprised of conductive elastometric comb capacitors embedded in a dielectric. Two different sensor designs, lateral combs (LC) and transverse combs (TC), were developed to evaluate sensor sensitivity as a function of load orientation. Both sensors have combs with a gap of 30 μm, length of 4 mm, and depth of 65 μm. A linear elastic analytical model was developed to predict change in capacitance as a function of strain, and experimental results show a reasonable agreement with the theoretical predictions. The observed strain responses have high linearity and dynamic range, and negligible hysteresis. The strain resolution of the LC and TC sensors is 100 μstrain and 500 μstrain, respectively, tested up to 50% strain.

A precise and implantable micromanipulator is presented for automatically advancing electrodes during single unit recordings in freely-behaving animals. The modular design and enhanced clamping mechanism with simple mechanical components are designed to provide reliable linear motion using a piezo motor with a stroke of 3 mm. To be specific, a closed loop control system, based on the position feedback from a magnetoresistive (MR) sensor, was implemented to overcome the non-linear characteristics of the piezo motor and to locate electrodes precisely at the targeted position with the accuracy of 1 μm, even under load. The weight of the micromanipulator is only 0.84 g when it is fully assembled with the MR sensor, PCBs, and connectors. In addition, a protective cover is employed to prevent breakage during semi-chronic recording. The positioning performance of the micromanipulator was tested at various loading conditions using various control methods. Finally, the activities of a single unit were isolated successfully using small step adjustments, such as 1 to 5 μm, in freely-moving mice.