1 Contributo su Rivista

1 Contributo su Rivista: [36185] Home page macrotipologia

Sfoglia

Prodotti recenti

The Need Satisfaction and Frustration Scale (NSFS): An in-depth psychometric validation among Italian workers

Studies have suggested that basic psychological needs are associated with a variety of workplace dimensions across different cultures such as psychological well-being, burnout, motivation, and job design. Consequently, validated psychometric scales could facilitate researchers and counsellors to better understand the processes of employees’ basic psychological needs. The present paper evaluated the psychometric properties of the Italian version of the Need Satisfaction and Frustration Scale (...

Feature fusion using deep learning for smartphone based human activity recognition

Identification of human physical activities is an active research area since long due to its application in personalized health and fitness monitoring. The performance accuracy of human activity recognition (HAR) models mainly depend on the features which are extracted from domain knowledge. The features are the input of the classification algorithm to efficiently identify human physical activities. Manually extracted features (handcrafted) need expert domain knowledge. Thus these features ha...

Subsampled Randomized Hadamard Transformation-based Ensemble Extreme Learning Machine for Human Activity Recognition

Extreme Learning Machine (ELM) is becoming a popular learning algorithm due to its diverse applications, including Human Activity Recognition (HAR). In ELM, the hidden node parameters are generated at random, and the output weights are computed analytically. However, even with a large number of hidden nodes, feature learning using ELM may not be efficient for natural signals due to its shallow architecture. Due to noisy signals of the smartphone sensors and high dimensional data, substantial ...

Guided regularized random forest feature selection for smartphone based human activity recognition

Human activity recognition (HAR) is an eminent area of research due to its extensive scope of applications in remote health monitoring, sports, smart home, and many more. Smartphone-based HAR systems use high-dimensional sensor data to infer human physical activities. Researchers continuously endeavor to select pertinent and non-redundant features without compromising the classification accuracy. In this work, our aim is to build an efficient HAR model that not only extracts the most relevant...

Attention-Based Deep Learning Framework for Hemiplegic Gait Prediction With Smartphone Sensors

This research revealed a reliable hemiplegia gait monitoring strategy to help medical practitioners in keeping track of a patient's status. Although numerous technologies have been utilized in the past to collect motion data from patients, the high costs and huge spaces required make them challenging to use in a home setting for rehabilitation. A telemedicine protocol requires a reliable patient monitoring technique that can automatically record and classify patient movements. To achieve this...

Scopri

Accesso al fulltext

no fulltext35898
open228
reserved45
restricted13
partially open1