AIMC Topic: Humans

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Optimization of Torque-Control Model for Quasi-Direct-Drive Knee Exoskeleton Robots Based on Regression Forecasting.

Sensors (Basel, Switzerland)
The choice of torque curve in lower-limb enhanced exoskeleton robots is a key problem in the control of lower-limb exoskeleton robots. As a human-machine coupled system, mapping from sensor data to joint torque is complex and non-linear, making it di...

Assisted Robots in Therapies for Children with Autism in Early Childhood.

Sensors (Basel, Switzerland)
Children with autism spectrum disorder (ASD) have deficits that affect their social relationships, communication, and flexibility in reasoning. There are different types of treatment (pharmacological, educational, psychological, and rehabilitative). ...

Predicting extremely low body weight from 12-lead electrocardiograms using a deep neural network.

Scientific reports
Previous studies have successfully predicted overweight status by applying deep learning to 12-lead electrocardiogram (ECG); however, models for predicting underweight status remain unexplored. Here, we assessed the feasibility of deep learning in pr...

Fully automated deep learning based auto-contouring of liver segments and spleen on contrast-enhanced CT images.

Scientific reports
Manual delineation of liver segments on computed tomography (CT) images for primary/secondary liver cancer (LC) patients is time-intensive and prone to inter/intra-observer variability. Therefore, we developed a deep-learning-based model to auto-cont...

Shaping high-performance wearable robots for human motor and sensory reconstruction and enhancement.

Nature communications
Most wearable robots such as exoskeletons and prostheses can operate with dexterity, while wearers do not perceive them as part of their bodies. In this perspective, we contend that integrating environmental, physiological, and physical information t...

Predicting the incidence of infectious diarrhea with symptom surveillance data using a stacking-based ensembled model.

BMC infectious diseases
BACKGROUND: Infectious diarrhea remains a major public health problem worldwide. This study used stacking ensemble to developed a predictive model for the incidence of infectious diarrhea, aiming to achieve better prediction performance.

Artificial intelligence methods for modeling gasification of waste biomass: a review.

Environmental monitoring and assessment
Gasification is a highly promising thermochemical process that shows considerable potential for the efficient conversion of waste biomass into syngas. The assessment of the feasibility and comparative advantages of different biomass and waste gasific...

Stratification of tumour cell radiation response and metabolic signatures visualization with Raman spectroscopy and explainable convolutional neural network.

The Analyst
Reprogramming of cellular metabolism is a driving factor of tumour progression and radiation therapy resistance. Identifying biochemical signatures associated with tumour radioresistance may assist with the development of targeted treatment strategie...

Artificial intelligence to predict bed bath time in Intensive Care Units.

Revista brasileira de enfermagem
OBJECTIVES: to assess the predictive performance of different artificial intelligence algorithms to estimate bed bath execution time in critically ill patients.