Deployable Method Research Articles

A smartphone based AI model to detect left ventricular systolic dysfunction on 12-lead ECG

Abstract Background Despite echocardiography being essential for cardiac assessment, a point-of-care tool for rapid left ventricular ejection fraction (LVEF) evaluation is lacking in current clinical practice, highlighting the need for a widescale, easily deployable method to assess LV function. Purpose To develop and validate two artificial intelligence (AI) models that accurately detect reduced LVEF on a single 12-lead ECG using a smartphone, facilitating early identification of patients at risk for heart failure who may benefit from more detailed echocardiographic evaluation. Methods A large collection of ECGs and transthoracic echocardiograms (TTEs) was sourced from our hospital data vault from 2011 to 2021. ECGs were paired to TTEs within a 24h time window and the resulting pairs were randomly assigned to the model development dataset (50%) and validation dataset (50%). Two distinct AI-ECG models were developed: one to detect LVEF≤40% and another for LVEF<50%, derived from the ESC definition of heart failure. Both AI-ECG models were paired with smartphone based ECG digitization technology. Key performance metrics include area under the receiver operating characteristic curve (AUC), sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and F1-score. Results A total of 1,205,370 ECGs (198,274 patients) and 291,433 TTEs (105,849 patients) were collected and paired, resulting in 109,809 ECG-TTE pairs (56,236 patients). The validation dataset consisted of 25,510 distinct TTE-ECG pairs (25,510 patients). Prevalence of LVEF≤40% and LVEF<50% was 5.4% and 7.9% respectively. The LVEF≤40% model demonstrated an AUC of 0.963 (95% CI: 0.959-0.966), sensitivity 0.924 (95% CI: 0.91-0.937), specificity 0.887 (95% CI: 0.883-0.891), and F1-score of 0.474 (95% CI: 0.457-0.490). PPV and NPV were 0.318 (95% CI: 0.304-0.333) and 0.995 (95% CI: 0.994-0.996) respectively. Performance of the LVEF <50% model shows an AUC of 0.952 (95% CI: 0.947-0.956), with a slightly lower sensitivity of 0.899 (95% CI: 0.886-0.912), specificity of 0.875 (95% CI: 0.871-0.879), PPV of 0.382 (95% CI: 0.368-0.395), NPV of 0.99 (95% CI: 0.989-0.992), and F1-score of 0.536 (0.521-0.55). Conclusion The AI models exhibit the ability to identify reduced LVEF from standard 12-lead ECGs using a smartphone. Results suggest potential clinical utility as a preliminary screening tool to select patients for confirmatory echocardiography. This AI application could potentially offer a timely and cost-effective method for identifying patients at risk for future development of heart failure.

A deployable film method to enable replicable sampling of low-abundance environmental microbiomes

Urbanizing global populations spend over 90% of their time indoors where microbiome abundance and diversity are low. Chronic exposure to microbiomes with low abundance and diversity have demonstrated negative long-term impacts on human health. Sequencing-based analyses of environmental nucleic acids are critical to understanding the impact of the indoor microbiome on human health, however low DNA yields indoors, alongside sample collection and processing inconsistencies, currently challenge study replicability. This study presents a comparative assessment of a novel, passive, easily replicable sampling strategy using polydimethylsiloxane (PDMS) sheets alongside a representative swab-based collection protocol. Deployable, customizable PDMS films designed for whole-sample insertion into standardized extraction kits demonstrated 43% higher DNA yields per sample, and 76% higher yields per cm2 of sampler over swab-based protocols. These results indicate that this accessible, scalable method enables sufficient DNA collection to comprehensively evaluate indoor microbiome exposures and potential human health impacts using smaller, more space efficient samplers, representing an attractive alternative to swab-based collection. In addition, this process reduces the manual steps required for microbiome sampling which could address inter-study variability, transform the current microbiome sampling paradigm, and ultimately benefit the replicability and accessibility of microbiome exposure studies.

Investigation of experimental parameters in the electric field and mechanical vibration integrated AFM-based nanopatterning on PEDOT

Atomic force microscope (AFM)-based nanomanufacturing offers an affordable and easily deployable method for fabricating high-resolution nanopatterns. This study employs a comprehensive design of experiment (DOE) approach to investigate the effects of various parameters, such as voltage, speed, and vibration axis, on the width and depth of lithography patterns using electrical field and vibration-assisted lithography on PEDOT: PSS films. The DOE explores the effect of voltage and speed on the process of electrical field and vibration-assisted AFM-based nanopatterning in two vibration trajectories: a circular trajectory employing X and Y axis vibration and a reciprocating trajectory employing Y axis vibration. The results indicate that using circular XY-vibration with a low stiffness contact probe and optimized speed and voltage factors results in higher depth and width of the lithography patterns compared to Y-vibration alone at the same parameters as expected. In both cases, pattern width was dominantly controlled by the voltage. Regarding depth, in XY-vibration, the speed of the tip is the most significant factor, while for Y-vibration, voltage plays the most significant role. It is noteworthy that there is a minimum threshold of speed that can produce a pattern; for example, the high-speed level that produced patterns in the circular trajectory (XY-vibration) did not produce patterns in reciprocating motion (Y-vibration). In conclusion, the study demonstrates the significant impact of voltage, speed, and axis on the width and depth of the lithography patterns. These findings can be instrumental in developing and understanding AFM-based high-resolution nanofabrication techniques.

Easily deployable real-time detection method for small traffic signs

Traffic sign detection technology plays an important role in driver assistance systems and automated driving systems. This paper proposes DeployEase-YOLO, a real-time high-precision detection scheme based on an adaptive scaling channel pruning strategy, to facilitate the deployment of detectors on edge devices. More specifically, based on the characteristics of small traffic signs and complex background, this paper first of all adds a small target detection layer to the basic architecture of YOLOv5 in order to improve the detection accuracy of small traffic signs.Then, when capturing specific scenes with large fields of view, higher resolution and richer pixel information are preserved instead of directly scaling the image size. Finally, the network structure is pruned and compressed using an adaptive scaling channel pruning strategy, and the pruned network is subjected to a secondary sparse pruning operation. The number of parameters and computations is greatly reduced without increasing the depth of the network structure or the influence of the input image size, thus compressing the model to the minimum within the compressible range. Experimental results show that the model trained by Experimental results show that the model trained by DeployEase-YOLO achieves higher accuracy and a smaller size on TT100k, a challenging traffic sign detection dataset. Compared to existing methods, DeployEase-YOLO achieves an average accuracy of 93.3%, representing a 1.3% improvement over the state-of-the-art YOLOv7 network, while reducing the number of parameters and computations to 41.69% and 59.98% of the original, respectively, with a compressed volume of 53.22% of the previous one. This proves that the DeployEase-YOLO has a great deal of potential for use in the area of small traffic sign detection. The algorithm outperforms existing methods in terms of accuracy and speed, and has the advantage of a compressed network structure that facilitates deployment of the model on resource-limited devices.

A Survey on Speech Emotion Based Music Recommendation System

Psychological research has proven that music relieves stress, elevates mood, and is responsible for the release of “feel-good” chemicals like oxytocin, serotonin, and dopamine. It comes as no surprise that music has been a popular tool in rehabilitation centers and therapy for various disorders, thus with the interminably rising numbers of people facing mental health-related issues across the globe, addressing mental health concerns is more crucial than ever. Despite the existing music recommendation systems, there is a dearth of holistically curated algorithms that take care of the needs of users. Given that, an undeniable majority of people turn to music on a regular basis and that music has been proven to increase cognition, memory, and sleep quality while reducing anxiety, pain, and blood pressure, it is the need of the hour to fashion a product that extracts all the benefits of music in the most extensive and deployable method possible. Our project aims to ameliorate our users’ mental state by building a comprehensive mood-based music recommendation system called “Viby”. Keywords Language, Communication, Speech recognition, Interaction.

Field Deployable Mirror Soiling Detection Based on Polarimetric Imaging

Heliostat mirror soiling is one of the significant factors for reduced optical efficiency of Concentrated Solar Power (CSP) fields. However, it remains challenging to determine the mirror soiling patterns across the large area of the CSP field. Here we propose a fast and field deployable inspection method to measure the heliostat mirror soiling levels based on polarization images in regular daylight settings without additional light sources. Under sunny and clear sky conditions, we have demonstrated accurate measurement of reflection efficiency (error ~1%) for mirrors with different soiling levels.

The CombE-IDMS Alternate Potency Method for H5N1 and H5N8 Cell-Based Vaccines.

Assaying the potency of inactivated viral influenza vaccines is performed using single radial immunodiffusion, which is the globally accepted release method for potency. Under conditions of a rapidly emerging pandemic, such as the 2009 H1N1 influenza pandemic, a recognized obstacle in the delivery of vaccines to the public is the time needed for the distribution of calibrated SRID reagents (antisera and antigen standards) to vaccine manufacturers. Previously, we first described a novel streamlined MS-based assay, CombE-IDMS, which does not rely on antisera/antibodies or reference antigens, as a potential rapidly deployable alternate potency method through a comparison with SRID on adjuvanted seasonal quadrivalent vaccine cell-based (aQIVc) materials. In this report, we further demonstrate that the CombE-IDMS method can also be applied to measure the potency of pre-pandemic H5N1 and H5N8 monovalent vaccine materials, each subtype both unadjuvanted and adjuvanted, through a forced degradation study. Overall, CombE-IDMS results align with those of the gold standard SRID method on both H5N1 and H5N8 materials under conditions of thermal, pH, oxidative and freeze/thaw stress, lending further evidence for the CombE-IDMS method's suitability as an alternate assay for potency of both seasonal and pandemic influenza vaccines.

Hemorrhage Evaluation and Detector System for Underserved Populations: HEADS-UP

ObjectiveTo create a rapid, cloud-based, and deployable machine learning (ML) method named hemorrhage evaluation and detector system for underserved populations, potentially across the Mayo Clinic enterprise, then expand to involve underserved areas and detect the 5 subtypes of intracranial hemorrhage (IH). MethodsWe used Radiological Society of North America dataset for IH detection. We made 4 total iterations using Google Cloud Vertex AutoML. We trained an AutoML model with 2000 images, followed by 6000 images from both IH positive and negative classes. Pixel values were measured by the Hounsfield units, presenting a width of 80 Hounsfield and a level of 40 Hounsfield as the bone window. This was followed by a more detailed image preprocessing approach by combining the pixel values from each of the brain, subdural, and soft tissue window-based gray-scale images into R(red)-channel, G(green)-channel, and B(blue)-channel images to boost the binary IH classification performance. Four experiments with AutoML were applied to study the effects of training sample size and image preprocessing on model performance. ResultsOut of the 4 AutoML experiments, the best-performing model was the fourth experiment, where 95.80% average precision, 91.40% precision, and 91.40% recall were achieved. On the basis of this analysis, our binary IH classifier hemorrhage evaluation and detector system for underserved populations appeared both accurate and performed well. ConclusionHemorrhage evaluation and detector system for underserved populations is a rapid, cloud-based, deployable ML method to detect IH. This tool can help expedite the care of patients with IH in resource-limited hospitals.

Functional Distinctions of Endometrial Cancer-Associated Mutations in the Fibroblast Growth Factor Receptor 2 Gene.

Functional analysis of somatic mutations in tumorigenesis facilitates the development and optimization of personalized therapy for cancer patients. The fibroblast growth factor receptor 2 (FGFR2) gene is frequently mutated in endometrial cancer (EC), but the functional implications of FGFR2 mutations in cancer development remain largely unexplored. In this study, we introduced a reliable and readily deployable screening method to investigate the effects of FGFR2 mutations. We demonstrated that distinct mutations in FGFR2 can lead to differential downstream consequences, specifically affecting a disintegrin- and metalloprotease 17 (ADAM17)-dependent shedding of the epidermal growth factor receptor (EGFR) ligand heparin-binding EGF-like growth factor (HB-EGF) and phosphorylation of mitogen-activated protein kinases (MAPKs). Furthermore, we showed that the distribution of mutations within the FGFR2 gene can influence their oncogenic effects. Together, these findings provide important insights into the complex nature of FGFR2 mutations and their potential implications for EC. By unraveling the distinct effects of different mutations, our study contributes to the identification of personalized treatment strategies for patients with FGFR2-mutated cancers. This knowledge has the potential to guide the development of targeted therapies that specifically address the underlying molecular alterations associated with FGFR2 mutations, ultimately improving patient outcomes in EC and potentially other cancer types characterized by FGFR2 mutations.

Establishing SARS-CoV-2 membrane protein-specific antibodies as a valuable serological target via high-content microscopy

The prevalence and strength of serological responses mounted toward SARS-CoV-2 proteins other than nucleocapsid (N)and spike (S), which may be of use as additional serological markers, remains underexplored. Using high-content microscopy to assess antibody responses against full-length StrepTagged SARS-CoV-2 proteins, we found that 85% (166/196) of unvaccinated individuals with RT-PCR confirmed SARS-CoV-2 infections and 74% (31/42) of individuals infected after being vaccinated developed detectable IgG against the structural protein M, which is higher than previous estimates. Compared with N antibodies, M IgG displayed a shallower time-dependent decay and greater specificity. Sensitivity for SARS-CoV-2 seroprevalence was enhanced when N and M IgG detection was combined. These findings indicate that screening for M seroconversion may be a good approach for detecting additional vaccine breakthrough infections and highlight the potential to use HCM as a rapidly deployable method to identify the most immunogenic targets of newly emergent pathogens.

Material Circularity: A Novel Method for Biobased Carbon Quantification of Leather, Artificial Leather, and Trendy Alternatives

In the past, climate change led the United Nations to define the Sustainable Development Goals Statement “blueprint to achieve a better and more sustainable future” and the European Commission to promote the “bioeconomy” concept and to launch the Green Deal Policy. Accordingly, the COP26 conference proposed a drastic reduction of fossil-based fuels and materials, in favor of biobased materials which should ensure intrinsic carbon neutrality. Contextually, many startups and established materials suppliers proposed new, trendy materials claiming sustainability advantages but, in many cases, without robust scientific backing. The need for transparency in terms of circularity led us to exploit a fast, reliable and easily deployable analytical method for assessing the biogenic carbon fraction in a variety of industrial materials. Our research team identified a radiocarbon analysis based on Saturated-absorption CAvity Ring-down (SCAR) spectroscopy as a quick and effective method for such a scope. Here we demonstrate its use for the determination of biogenic/fossil carbon proportions of polymeric and coated materials such as leather, coated textiles and trendy alternatives used in the fashion industry, with the scope of defining their intrinsic renewable content. The reliability of the SCAR method is validated through a comparison with the results obtained by the benchmark technique.

UNLOG: A Deployable, Lightweight, and Bending-Active Timber Construction Method

UNLOG is a lightweight and easily deployable timber framing method that utilizes robotic kerfing techniques to transform emerald ash borer (EAB)-infested timbers into materially efficient leaf-spring components. We developed this bending-active structural system through geometric studies and physical prototypes. They contribute to ongoing research in robotic fabrication with natural log geometries, bending-active structural systems, deployable structures, minimum-waste fabrication, material circularity, and Mixed Reality (MR) applications for component assembly. Using only six logs, threaded rods, and custom recycled HPDE washers, the resulting pavilion demonstrates the feasibility of the UNLOG method to construct leaf-spring components through MR instruction and robotic kerfing techniques with EAB-infested ash wood.

A Fast Deployable Instance Elimination Segmentation Algorithm Based on Watershed Transform for Dense Cereal Grain Images

Cereal grains are a vital part of the human diet. The appearance quality and size distribution of cereal grains play major roles as deciders or indicators of market acceptability, storage stability, and breeding. Computer vision is popular in completing quality assessment and size analysis tasks, in which an accurate instance segmentation is a key step to guaranteeing the smooth completion of tasks. This study proposes a fast deployable instance segmentation method based on a generative marker-based watershed segmentation algorithm, which combines two strategies (one strategy for optimizing kernel areas and another for comprehensive segmentation) to overcome the problems of over-segmentation and under-segmentation for images with dense and small targets. Results show that the average segmentation accuracy of our method reaches 98.73%, which is significantly higher than the marker-based watershed segmentation algorithm (82.98%). To further verify the engineering practicality of our method, we count the size distribution of segmented cereal grains. The results keep a high degree of consistency with the manually sketched ground truth. Moreover, our proposed algorithm framework can be used as a great reference in other segmentation tasks of dense targets.

Inactivation strategies for SARS-CoV-2 on surgical masks using light-activated chemical dyes

BackgroundMethylene blue (MB) and riboflavin (RB) are light-activated dyes with demonstrated antimicrobial activity. They require no specialized equipment, making them attractive for widespread use. Due to COVID-19-related worldwide shortages of surgical masks, simple, safe, and effective decontamination methods for reusing masks have become desirable in clinical and public settings.Material and methodsWe examined the decontamination of SARS-CoV-2 Beta variant on surgical masks and Revolution-Zero Environmentally Sustainable (RZES) reusable masks using these photoactivated dyes. We pre-treated surgical masks with 2 MB concentrations, 2 RB concentrations, and 2 combinations of MB and RB. We also tested 7 MB concentrations on RZES masks.ResultsPhotoactivated MB consistently inactivated SARS-CoV-2 at >99.9% for concentrations of 2.6 µM or higher within 30 min on RZES masks and 5 µM or higher within 5 min on disposable surgical masks. RB alone showed a lower, yet still significant inactivation (∼93-99%) in these conditions.DiscussionMB represents a cost-effective, rapid, and widely deployable decontamination method for SARS-CoV-2. The simplicity of MB formulation makes it ideal for mask pre-treatment in low-resource settings.ConclusionsThe results demonstrate that MB effectively decontaminates SARS-CoV-2 at concentrations above 5 µM on surgical masks and above 10 µM on RZES masks.

Exploring inactivation of SARS-CoV-2, MERS-CoV, Ebola, Lassa, and Nipah viruses on N95 and KN95 respirator material using photoactivated methylene blue to enable reuse

BackgroundThe COVID-19 pandemic resulted in a worldwide shortage of N95 respirators, prompting the development of decontamination methods to enable limited reuse. Countries lacking reliable supply chains would also benefit from the ability to safely reuse PPE. Methylene blue (MB) is a light-activated dye with demonstrated antimicrobial activity used to sterilize blood plasma. Decontamination of respirators using photoactivated MB requires no specialized equipment, making it attractive for use in the field during outbreaks.MethodsWe examined decontamination of N95 and KN95 respirators using photoactivated MB and 3 variants of SARS-CoV-2, the virus that causes COVID-19; and 4 World Health Organization priority pathogens: Ebola virus, Middle East respiratory syndrome coronavirus, Nipah virus, and Lassa virus. Virus inactivation by pretreating respirator material was also tested.ResultsPhotoactivated MB inactivated all tested viruses on respirator material, albeit with varying efficiency. Virus applied to respirator material pre-treated with MB was also inactivated, thus MB pretreatment may potentially protect respirator wearers from virus exposure in real-time.ConclusionsThese results demonstrate that photoactivated MB represents a cost-effective, rapid, and widely deployable method to decontaminate N95 respirators for reuse during supply shortages.

English

Diagnosis of maize lethal necrosis (MLN)-causing viruses is key in MLN surveillance programs and in testing seed for zero tolerance of Maize chlorotic mottle virus (MCMV) in seed lots. This is crucial for MLN management in farmers’ fields and in commercial seed fields. A customized MCMV detection assay that is specific, sensitive, affordable, and portable is therefore important for this task. Reverse Transcriptase–Recombinase Polymerase Amplification (RT-RPA) meets those conditions earlier described. RPA is a rapid isothermal nucleic acid amplification and detection platform that is based on patented Recombinase Polymerase Amplification (RPA) technology. In this study, a real time endpoint analysis and field deployable RT-RPA diagnostic method for the detection of MCMV was developed. RPA primer sets with their complementary probes were designed, synthesized and tested through a series of primer set evaluations to determine the most efficient primer sets. The primer sets targeted the MCMV genome at position 2765-2948 bp (MCMV_gp2 replicase gene). The parameters evaluated were sensitivity, specificity and reproducibility for the assay with remarkable results. The assay discriminated against other maize infecting viruses hence specific to MCMV. The assay takes only 20 min and its detection limit of 10-4 is well comparable to RT-PCR and other molecular based detection assays. MCMV was also detected directly from leaf saps without the nucleic acid extraction step hence suitable for on-farm testing. RPA is a relatively inexpensive technique that requires minimal instrumentation. This assay is therefore suitable for the detection of MCMV in field surveys, routine MCMV testing for phytosanitary measures and in the seed certification procedures.   Key words: Maize lethal necrosis, maize chlorotic mottle virus, diagnostics, recombinase polymerase amplification.

Oddlab: fault-tolerant aware load-balancing framework for data center networks

Data center networks (DCNs) act as critical infrastructures for emerging technologies. In general, a DCN involves a multi-rooted tree with various shortest paths of equal length from end to end. The DCN fabric must be maintained and monitored to guarantee high availability and better QoS. Traditional traffic engineering (TE) methods frequently reroute large flows based on the shortest and least-congested paths to maintain high service availability. This procedure results in a weak link utilization with frequent packet reordering. Moreover, DCN link failures are typical problems. State-of-the-art approaches address such challenges by modifying the network components (switches or hosts) to discover and avoid broken connections. This study proposes Oddlab (Odds labels), a novel deployable TE method to guarantee the QoS of multi-rooted data center (DC) traffic in symmetric and asymmetric modes. Oddlab creatively builds a heuristic model for efficient flow scheduling and faulty link detection by exclusively using the gathered statistics from the DCN data plane, such as residual bandwidth and the number of installed elephant flows. Besides, the proposed method is implemented in an SDN-based DCN without altering the network components. Our findings indicate that Oddlab can minimize the flow completion time, maximize bisection bandwidth, improve network utilization, and recognize faulty links with sufficient accuracy to improve DC productivity.

Rapid Mapping of Hydrological Systems in Tanzania Using a Towed Transient Electromagnetic System.

Limited knowledge of local groundwater systems often results in the failure of boreholes to yield water of the required quantity and quality. This is particularly problematic in the developing world, where financial resources are often limited, and failed wells represent a significant financial burden. To enhance understanding of local hydrological systems, noninvasive geophysical methods can aid the understanding of hydrogeological structures and identification of groundwater sources needed to optimize siting of wells. Here, we highlight the utility of a relatively new towed-transient electromagnetic system, called tTEM. This system is a rapidly deployable mobile geophysical method well-suited to cost-efficient characterization of local-to-regional groundwater systems. Results from tTEM surveys conducted in two refugee camps and several host communities in western Tanzania demonstrate the capability of the method to characterize shallow aquifer systems with high lateral and vertical resolution, with data collection typically exceeding 15 to 20 line-kilometers (km) per day. This work focuses on tTEM's ability to provide semiquantitative insights into regional hydrogeological settings when supporting data required for more rigorous interpretation/modeling is lacking. The system provided useful data within communities with low density of electrification and near buildings with metal roofs and walls. tTEM-derived resistivity profiles were correlated with limited local borehole lithologic information to develop conceptual models of the local groundwater systems. These models were used to successfully guide the siting of a production well and to identify future drilling targets in the refugee camps and surrounding communities.

Quantitative Fit Evaluation of N95 Filtering Facepiece Respirators and Coronavirus Inactivation Following Heat Treatment.

Reuse of filtering facepiece respirators (FFRs, commonly referred to as N95s) normally meant for single use has become common in healthcare facilities due to shortages caused by the COVID-19 pandemic. Here, we report that murine hepatitis coronavirus initially seeded on FFR filter material is inactivated (6 order of magnitude reduction as measured by median tissue culture infective dose, TCID50) after dry heating at 75°C for 30 min. We also find that the quantitative fit of FFRs after heat treatment at this temperature, under dry conditions or at 90% relative humidity, is not affected by single or 10 heating cycles. Previous studies have reported that the filtration efficiency of FFRs is not negatively impacted by these heating conditions. These results suggest that thermal inactivation of coronaviruses is a potentially rapid and widely deployable method to reuse N95 FFRs in emergency situations where reusing FFRs is a necessity and broad-spectrum sterilization is unavailable. However, we also observe that a radiative heat source (e.g. an exposed heating element) results in rapid qualitative degradation of the FFR. Finally, we discuss differences in the results reported here and other recent studies investigating heat as a means to recycle FFRs. These differences suggest that while our repeated decontamination cycles do not affect FFR fit, overall wear time and the number of donning/doffing cycles are important factors that likely degrade FFR fit and must be investigated further.

3-D Gait Abnormality Detection Employing Contactless IR-UWB Sensing Phenomenon

Gait disorder diagnosis and rehabilitation is one area where human perception and observation are highly integrated. Predominantly, gait evaluation comprises technological devices for gait analysis, such as dedicated force sensors, cameras, and wearable sensor-based solutions; however, they are limited by insufficient gait parameter recognition, postprocessing, installation costs, mobility, and skin irritation issues. Thus, the proposed study concentrates on the creation of a widely deployable, noncontact and nonintrusive gait recognition method from impulse radio ultrawideband (IR-UWB) sensing phenomenon, where a standalone IR-UWB system can detect gait problems with less human intervention. A 3-D human motion model for gait identification from IR-UWB has been proposed with embracing spherical trigonometry and vector algebra to determine knee angles. Subsequently, normal and abnormal walking subjects were involved in this study. Abnormal gait subjects belong to the spastic gait category only. The prototype has been tested in both anechoic and multipath environments. The outcomes have been corroborated with a simultaneously deployed Kinect Xbox sensor and supported by a statistical graphical approach Bland and Altman (B&A) analysis.