Pediatric Medical Device Development Pathways

The Pediatric Medical Device Safety and Improvement Act (PMDSIA) was passed in 2007 to increase the number of pediatric devices approved by the Food and Drug Administration (FDA). PMDSIA also led to the introduction of the Pediatric Device Consortia grant program, which promotes institutions to pass new pediatric devices. In the past, people relied on nontraditional methods of gaining capital for medical device development. Since the act, pediatric devices have still lagged behind FDA-approved adult medical de­vices due to various clinical hurdles, including the small market size, which can result in a limited return on investment (ROI), a challenge for private investors

Recreating the Feel of the Human Chest in a CPR Manikin via Programmable Pneumatic Damping

It is well known that the human chest exhibits a strong force displacement hysteresis during CPR, a stark contrast to the non hysteretic behavior of standard spring manikins. We hypothesize that individuals with experience performing CPR on humans would perceive a manikin with damping as more realistic and better for training. By analyzing data collected from chest compressions on real patients, we created a dynamic model that accounts for this hysteresis with a linear spring and a one-way variable damper, and we built a new high-fidelity manikin to enact the desired force displacement relationship. A linkage attached to the chest plate converts vertical compression motions to the horizontal displacement of a set of pneumatic dashpot pistons, sending a volume of air into and out of the manikin through a programmable valve. Position and pressure sensors allow a microcontroller to adjust the valve orifice so that the provided damping force closely follows the desired damping force throughout the compression cycle. Eight experienced CPR practitioners tested both the new manikin and an identical looking standard manikin; the manikin with damping received significantly higher ratings for haptic realism and perceived utility as a training tool

Effect of mattress deflection on CPR quality assessment for older children and adolescents

Appropriate chest compression (CC) depth is associated with improved CPR outcome. CCs provided in hospital are often conducted on a compliant mattress. The objective was to quantify the effect of mattress compression on the assessment of CPR quality in children. Methods: A force and deflection sensor (FDS) was used during CPR in the Pediatric Intensive Care Unit and Emergency Department of a children's hospital. The sensor was interposed between the chest of the patient and hands of the rescuer and measured CC depth. Following CPR event, each event was reconstructed with a manikin and an identical mattress/backboard/patient configuration. CCs were performed using FDS on the sternum and a reference accelerometer attached to the spine of the manikin, providing a means to Calculate the mattress deflection. Results: Twelve CPR events with 14,487 CC (11 patients, median age 14.9 years) were recorded and reconstructed: 9 on ICU beds (9296 CC), 3 on stretchers (5191 CC). Measured mean CC depth during CPR was 47 +/- 8 mm on ICU beds, and 45 +/- 7 mm on stretcher beds with overestimation of 13 +/- 4 mm and 4 +/- 1 mm, respectively, due to mattress compression. After adjusting for this, the proportion of CC that met the CPR guidelines decreased from 88.4 to 31.8% on ICU beds (p < 0.001), and 86.3 to 64.7% on stretcher (p < 0.001 The proportion of appropriate depth CC was significantly smaller on ICU beds (p < 0.001). Conclusion: CC conducted on a non-rigid surface may not be deep enough. FDS may overestimate CC depth by 28% on ICU beds, and 10% on stretcher beds

On the Use of Unmanned Aerial Systems for Environmental Monitoring

Environmental monitoring plays a central role in diagnosing climate and management impacts on natural and agricultural systems; enhancing the understanding of hydrological processes; optimizing the allocation and distribution of water resources; and assessing, forecasting, and even preventing natural disasters. Nowadays, most monitoring and data collection systems are based upon a combination of ground-based measurements, manned airborne sensors, and satellite observations. These data are utilized in describing both small- and large-scale processes, but have spatiotemporal constraints inherent to each respective collection system. Bridging the unique spatial and temporal divides that limit current monitoring platforms is key to improving our understanding of environmental systems. In this context, Unmanned Aerial Systems (UAS) have considerable potential to radically improve environmental monitoring. UAS-mounted sensors offer an extraordinary opportunity to bridge the existing gap between field observations and traditional air- and space-borne remote sensing, by providing high spatial detail over relatively large areas in a cost-effective way and an entirely new capacity for enhanced temporal retrieval. As well as showcasing recent advances in the field, there is also a need to identify and understand the potential limitations of UAS technology. For these platforms to reach their monitoring potential, a wide spectrum of unresolved issues and application-specific challenges require focused community attention. Indeed, to leverage the full potential of UAS-based approaches, sensing technologies, measurement protocols, postprocessing techniques, retrieval algorithms, and evaluation techniques need to be harmonized. The aim of this paper is to provide an overview of the existing research and applications of UAS in natural and agricultural ecosystem monitoring in order to identify future directions, applications, developments, and challengespublishersversionPeer reviewe

Traumatic brain injury thresholds in the pre-adolescent juvenile

Traumatic brain injury (TBI) continues to be a major health epidemic, with an annual incidence in the United States in excess of 1.5 million per year, leading to 50,000 fatalities and 3.7 million people living with long-term disability from TBI. TBI is particularly devastating to the young. In countries around the globe, motor vehicle crashes are the leading cause of death for all children and traumatic brain and skull injuries are the most common serious injuries sustained by children in motor vehicle crashes. While the age-dependent tolerance of the infant and toddler have been investigated no information exists on the tolerance of pre-adolescent brain to injury. The research described herein provides critical experimental and computational TBI injury tolerance information for the pre-adolescent juvenile age group. In Chapter 2, we describe the development of a pre-adolescent TBI animal model that produces injury relevant to the human including diffuse axonal injury, subarachnoid and subdural hemorrhage, and secondary brain injury. We compare results with data from infant and toddler animals and propose an empirical scaling relationship to estimate velocity and acceleration tolerance. In Chapter 3, we develop a Finite Element Model (FEM) of pre-adolescent animal based upon a validated model from the younger animals, and determine the strain threshold for TBI in the pre-adolescent and compare it to the strain threshold of the infant. In Chapter 4, we develop an ultra high resolution finite element model with element edge length in the sub millimeter range and high geometric fidelity to both internal and external brain structures. We then use this high resolution model to investigate the effects of material heterogeneity on strain and injury prediction. Finally, in Chapter 5 we discuss the implications of our research in light of our long-term goal to support the development of human FEM brain models that can be coupled with an ATD and used to evaluate safety system design

Effect of mattress deflection on CPR quality assessment for older children and adolescents

Appropriate chest compression (CC) depth is associated with improved CPR outcome. CCs provided in hospital are often conducted on a compliant mattress. The objective was to quantify the effect of mattress compression on the assessment of CPR quality in children. Methods: A force and deflection sensor (FDS) was used during CPR in the Pediatric Intensive Care Unit and Emergency Department of a children's hospital. The sensor was interposed between the chest of the patient and hands of the rescuer and measured CC depth. Following CPR event, each event was reconstructed with a manikin and an identical mattress/backboard/patient configuration. CCs were performed using FDS on the sternum and a reference accelerometer attached to the spine of the manikin, providing a means to Calculate the mattress deflection. Results: Twelve CPR events with 14,487 CC (11 patients, median age 14.9 years) were recorded and reconstructed: 9 on ICU beds (9296 CC), 3 on stretchers (5191 CC). Measured mean CC depth during CPR was 47 +/- 8 mm on ICU beds, and 45 +/- 7 mm on stretcher beds with overestimation of 13 +/- 4 mm and 4 +/- 1 mm, respectively, due to mattress compression. After adjusting for this, the proportion of CC that met the CPR guidelines decreased from 88.4 to 31.8% on ICU beds (p < 0.001), and 86.3 to 64.7% on stretcher (p < 0.001 The proportion of appropriate depth CC was significantly smaller on ICU beds (p < 0.001). Conclusion: CC conducted on a non-rigid surface may not be deep enough. FDS may overestimate CC depth by 28% on ICU beds, and 10% on stretcher beds

Monitoring agricultural ecosystems

The world’s population is predicted to reach nearly 10 billion by 2050, and at the same time, economic growth and improving living standards in developing countries are driving up food consumption. To accommodate these increasing demands for food, the agricultural sector will need to produce at least 50% more food by 2050. The increasing food production will need to be accomplished not only on degrading soils, with depleting freshwater resources and while experiencing climate change but also sustainably to ensure long-term food and water security. With little existing space to expand current agricultural extents, the increased food production needs to be realized within existing farms through sustainable intensification of farming and by ensuring increased yield (FAO, 2017; Hunter et al., 2017; Karthikeyan et al. 2020). Through the history of agriculture, we have witnessed three major revolutions, that is, transitioning from hunting and gathering to planting; increasing productivity of farming through mechanization; and introducing genetic engineering, hybrid plants, and application of fertilizers and pesticides. To meet the growing global demand for food, a new revolution is required to further increase food production. This new revolution is considered by many to be digital agriculture (Shepherd et al., 2018). Digital agriculture is focusing on management nd decision support infrastructure, including new sensing systems installed in situ or on robotics and unmanned aerial system (UAS). With an increasing number of miniaturized devices and sensors, data collection is becoming quicker, easier, and more accurate. As an integral part of digital agriculture, artificial intelligence and other improved data processing and intelligent software solutions are assisting in analyzing and making sense of an ever-increasing amount of data for agricultural production. With improvements in information and communication technology and increased connectivity, real-time or near real-time information is becoming available to improve decision-making and farm management, all of which can help enhance food production efforts (Fountas et al., 2020). Here we will focus on one aspect of this digital resolution in green farming: the procurement of spatially rich and temporally dense records of on-farm behavior via the use of UAS-based sensing technologies. UAS-based data collection has a unique advantage over other sensing systems due to the flexibility of deployment, the ability to cover specified spatial extents, ease of access, and the provision of consistent information. As such, UAS-based sensing technologies are playing a key role in advancing the promise of digital agriculture to facilitate data collection and actionable information useful for farm management and increased food production