Force-vector training

Quantization approximates a deep network model with floating-point numbers by the model with low bit width numbers, thereby accelerating inference and reducing computation. Zero-shot quantization, which aims to quantize a model without access to the original data, can be achieved by fitting the real data distribution through data synthesis. However, it has been observed that zero-shot quantization leads to inferior performance compared to post-training quantization with real data for two primary reasons: 1) a normal generator has difficulty obtaining a high diversity of synthetic data since it lacks long-range information to allocate attention to global features, and 2) synthetic images aim to simulate the statistics of real data, which leads to weak intra-class heterogeneity and limited feature richness...

In recent years, the analysis of movement patterns has increasingly focused on the individuality of movements. After long speculations about weak individuality, strong individuality is now accepted, and the first situation-dependent fine structures within it are already identified. Methodologically, however, only signals of the same movements have been compared so far. The goal of this work is to detect cross-movement commonalities of individual walking, running, and handwriting patterns using data augmentation...

BACKGROUND: According to the principle of specific adaptations to imposed demands, training induces specific adaptations that predominantly transfer towards performance tasks of similar physiological and/or biomechanical characteristics. Functional performance improvements secondary to resistance and plyometric training have been hypothesized to be force-vector specific; however, the literature pertaining to this matter appears somewhat equivocal. OBJECTIVE: The objective of the present systematic review with meta-analysis was to synthesize the available body of literature regarding the performance implications of vertically and horizontally oriented resistance- and plyometric training...

Defective wheels pose a significant challenge in railway transportation, impacting operational performance and safety. Excessive traction and braking forces give rise to deviations from the intended conical tread shape, resulting in amplified vibrations and noise. Moreover, these deviations contribute to the accelerated damage of track components. Detecting wheel defects at an early stage is crucial to ensure safe and comfortable operation, as well as to minimize maintenance costs. However, the presence of various vibrations, such as those induced by the track, traction motors, and other rolling stock subsystems, poses a significant challenge for onboard detection techniques...

Mechanical stresses generated at the cell-cell level and cell-substrate level have been suggested to be important in a host of physiological and pathological processes. However, the influence various chemical compounds have on the mechanical stresses mentioned above is poorly understood, hindering the discovery of novel therapeutics, and representing a barrier in the field. To overcome this barrier, we implemented two approaches: 1) Monolayer Boundary Predictor and 2) Discretized Window Predictor utilizing either the Stepwise Linear Regression (SLR) or Quadratic Support Vector Machine (QSVM) machine learning (ML) model to predict the dose-dependent response of tractions and intercellular stresses to chemical perturbation...

Due to the high labor cost of physicians, it is difficult to collect a rich amount of manually-labeled medical images for developing learning-based computer-aided diagnosis (CADx) systems or segmentation algorithms. To tackle this issue, we reshape the image segmentation task as an image-to-image (I2I) translation problem and propose a retinal vascular segmentation network, which can achieve good cross-domain generalizability even with a small amount of training data. We devise primarily two components to facilitate this I2I-based segmentation method...

Visible and subvisible particles are a quality attribute in sterile pharmaceutical samples. A common method for characterizing and quantifying pharmaceutical samples containing particulates is imaging many individual particles using high-throughput instrumentation and analyzing the populations data. The analysis includes conventional metrics such as the particle size distribution but can be more sophisticated by interpreting other visual/morphological features. To avoid the hurdles of building new image analysis models capable of extracting such relevant features from scratch, we propose using well-established pretrained deep learning image analysis models such as EfficientNet...

This work provides an innovative endodontic instrument fault detection methodology during root canal treatment (RCT). Sometimes, an endodontic instrument is prone to fracture from the tip, for causes uncertain the dentist's control. A comprehensive assessment and decision support system for an endodontist may avoid several breakages. This research proposes a machine learning and artificial intelligence-based approach that can help to diagnose instrument health. During the RCT, force signals are recorded using a dynamometer...

Hepatocellular carcinoma is a high-risk malignant tumor. Hepatoma cells are transformed from normal cells and have unique surface nanofeatures in addition to the characteristics of the original cells. In this paper, atomic force microscopy was used to extract the three-dimensional morphology and mechanical information of HL-7702 human hepatocytes and SMMC-7721 and HepG2 hepatoma cells in culture, such as the elastic modulus and viscoelasticity. The characteristics of different cells were compared and analyzed...

BACKGROUND: Acute kidney injury (AKI) is a common complication in critically ill patients with sepsis and is often associated with a poor prognosis. We aimed to construct and validate an interpretable prognostic prediction model for patients with sepsis-associated AKI (S-AKI) using machine learning (ML) methods. METHODS: Data on the training cohort were collected from the Medical Information Mart for Intensive Care IV database version 2.2 to build the model, and data of patients were extracted from Hangzhou First People's Hospital Affiliated to Zhejiang University School of Medicine for external validation of model...

PURPOSE: Qualitative and quantitative assessment of interventional performance is a vital component in the evaluation of endovascular surgery skill training. We established a custom simulator with qualitative and quantitative metrics for endovascular performance training. METHODS: The simulator included an in vitro silicone phantom, mock circulation loop, visual module, force-sensing module, and custom software for image and force data postprocessing. Two tasks to deliver the guidewire to the target location of the carotid artery were conducted by the expert (n=4), novice (n=6), and test (n=4) groups...

BACKGROUND: Uroflowmetry is a non-invasive study used in the diagnosis and monitoring of treatment response for lower urinary tract disorders. For optimal clinical utility, uroflow studies rely on careful clinical interpretation by a trained provider, but currently there is a lack of accepted standardized normal values for the measured uroflow parameters in children. The International Children's Continence Society proposed standardizing the terminology for uroflow curve shapes. However, the patterning of curves is largely at the physician's subjective discretion...

The Coronavirus Disease 2019 (COVID-19) has had a profound impact on global health and economy, making it crucial to build accurate and interpretable data-driven predictive models for COVID-19 cases to improve public policy making. The extremely large scale of the pandemic and the intrinsically changing transmission characteristics pose a great challenge for effectively predicting COVID-19 cases. To address this challenge, we propose a novel hybrid model in which the interpretability of the Autoregressive model (AR) and the predictive power of the long short-term memory neural networks (LSTM) join forces...

BACKGROUND AND OBJECTIVES: The quality of healthcare delivery depends directly on the skills of clinicians. For patients on hemodialysis, medical errors or injuries caused during cannulation can lead to adverse outcomes, including potential death. To promote objective skill assessment and effective training, we present a machine learning approach, which utilizes a highly-sensorized cannulation simulator and a set of objective process and outcome metrics. METHODS: In this study, 52 clinicians were recruited to perform a set of pre-defined cannulation tasks on the simulator...

The clinical success of vascular interventional surgery relies heavily on a surgeon's catheter/guidewire manipulation skills and strategies. An objective and accurate assessment method plays a critical role in evaluating the surgeon's technical manipulation skill level. Most of the existing evaluation methods incorporate the use of information technology to find more objective assessment models based on various metrics. However, in these models, sensors are often attached to the surgeon's hands or to interventional devices for data collection, which constrains the surgeon's operational movements or exerts an influence on the motion trajectory of interventional devices...

Craniotomy is a fundamental component of neurosurgery that involves the removal of the skull bone flap. Simulation-based training of craniotomy is an efficient method to develop competent skills outside the operating room. Traditionally, an expert surgeon evaluates the surgical skills using rating scales, but this method is subjective, time-consuming, and tedious. Accordingly, the objective of the present study was to develop an anatomically accurate craniotomy simulator with realistic haptic feedback and objective evaluation of surgical skills...

BACKGROUND: The prosthetic alignment procedure considers biomechanical, anatomical and comfort characteristics of the amputee to achieve an acceptable gait. Prosthetic malalignment induces long-term disease. The assessment of alignment is highly variable and subjective to the experience of the prosthetist, so the use of machine learning could assist the prosthetist during the judgment of optimal alignment. RESEARCH OBJECTIVE: To assist the prosthetist during the assessment of prosthetic alignment using a new computational protocol based on machine learning...

BACKGROUND: As the landscapes of war have evolved, so too has the role of the military medical officer (MMO). Colonel (Retired) Barry Wolcott developed a "vector" model in the 1990s, illustrating the dual professional role of the MMO. Since then, propelled by the War on Terror, MMOs have adapted to treating patients in volatile, uncertain, complex, and ambiguous operational environments. This study, therefore, aimed to explore modern-day aspects of the MMO's role in order to enhance Wolcott's depiction of the MMO's professional identity in contemporary operational environments...

One of the fundamental limitations in human biomechanics is that we cannot directly obtain joint moments during natural movements without affecting the motion. However, estimating these values is feasible with inverse dynamics computation by employing external force plates, which can cover only a small area of the plate. This work investigated the Long Short-Term Memory (LSTM) network for the kinetics and kinematics prediction of human lower limbs when performing different activities without using force plates after the learning...

Surface electromyography (sEMG) is a signal consisting of different motor unit action potential trains and records from the surface of the muscles. One of the applications of sEMG is the estimation of muscle force. We proposed a new real-time convex and interpretable model for solving the sEMG-force estimation. We validated it on the upper limb during isometric voluntary flexions-extensions at 30%, 50%, and 70% Maximum Voluntary Contraction in five subjects, and lower limbs during standing tasks in thirty-three volunteers, without a history of neuromuscular disorders...