Indicators on firm level innovation activities from web scraped data.

This article presents data on companies' innovative behavior measured at the firm-level based on web scraped firm-level data derived from medium-high and high-technology companies in the European Union and the United Kingdom. The data are retrieved from individual company websites and contains in total data on 96,921 companies. The data provide information on various aspects of innovation, most significantly the research and development orientation of the company at the company and product level, the company's collaborative activities, company's products, and use of standards.

Connecting firm's web scraped textual content to body of science: Utilizing microsoft academic graph hierarchical topic modeling.

This paper demonstrates a method to transform and link textual information scraped from companies' websites to the scientific body of knowledge. The method illustrates the benefit of Natural Language Processing (NLP) in creating links between established economic classification systems with novel and agile constructs that new data sources enable. Therefore, we experimented on the European classification of economic activities (known as NACE) on sectoral and company levels.

Using a Motion Sensor to Categorize Nonspecific Low Back Pain Patients: A Machine Learning Approach.

Nonspecific low back pain (NSLBP) constitutes a critical health challenge that impacts millions of people worldwide with devastating health and socioeconomic consequences. In today's clinical settings, practitioners continue to follow conventional guidelines to categorize NSLBP patients based on subjective approaches, such as the STarT Back Screening Tool (SBST). This study aimed to develop a sensor-based machine learning model to classify NSLBP patients into different subgroups according to quantitative kinematic data, i.

A novel approach to spinal 3-D kinematic assessment using inertial sensors: Towards effective quantitative evaluation of low back pain in clinical settings.

This paper presents a novel approach for evaluating LBP in various settings. The proposed system uses cost-effective inertial sensors, in conjunction with pattern recognition techniques, for identifying sensitive classifiers towards discriminate identification of LB patients. 24 healthy individuals and 28 low back pain patients performed trunk motion tasks in five different directions for validation.

A model for flexi-bar to evaluate intervertebral disc and muscle forces in exercises.

This study developed and validated a lumped parameter model for the FLEXI-BAR, a popular training instrument that provides vibration stimulation. The model which can be used in conjunction with musculoskeletal-modeling software for quantitative biomechanical analyses, consists of 3 rigid segments, 2 torsional springs, and 2 torsional dashpots. Two different sets of experiments were conducted to determine the model's key parameters including the stiffness of the springs and the damping ratio of the dashpots.

A reliability study of the new sensors for movement analysis (SHARIF-HMIS).

Aim: SHARIF-HMIS is a new inertial sensor designed for movement analysis. The aim of the present study was to assess the inter-tester and intra-tester reliability of some kinematic parameters in different lumbar motions making use of this sensor.

Materials And Methods: 24 healthy persons and 28 patients with low back pain participated in the current reliability study.

A Mechanical model for flexible exercise bars to study the influence of the initial position of the bar on lumbar discs and muscles forces.

A single-degree-of-freedom model is considered for flexible exercise bars based on the lumped-element approach. By considering the side segment of a flexible bar as a cantilever beam with an equivalent mass at the free end, its free-vibration response, as well as the forced response under the excitation of the grip, are expressed parametrically. Experiments are performed on a particular flexible bar (FLEXI_BAR) in order to obtain numerical values for quantifying the model's parameters.