AIMC Topic: Humans

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Machine learning in primary care: potential to improve public health.

Journal of medical engineering & technology
It is estimated that missed opportunities for diagnosis occur in 1 in 20 primary care appointments. This is not only detrimental to individual patients, but also to the healthcare system as health outcomes are affected and healthcare expenditure inev...

A Process Evaluation Examining the Performance, Adherence, and Acceptability of a Physical Activity and Diet Artificial Intelligence Virtual Health Assistant.

International journal of environmental research and public health
Artificial intelligence virtual health assistants are a promising emerging technology. This study is a process evaluation of a 12-week pilot physical activity and diet program delivered by virtual assistant "Paola". This single-arm repeated measures ...

A Robust Feature Extraction Model for Human Activity Characterization Using 3-Axis Accelerometer and Gyroscope Data.

Sensors (Basel, Switzerland)
Human Activity Recognition (HAR) using embedded sensors in smartphones and smartwatch has gained popularity in extensive applications in health care monitoring of elderly people, security purpose, robotics, monitoring employees in the industry, and o...

A Framework of Combining Short-Term Spatial/Frequency Feature Extraction and Long-Term IndRNN for Activity Recognition.

Sensors (Basel, Switzerland)
Smartphone-sensors-based human activity recognition is attracting increasing interest due to the popularization of smartphones. It is a difficult long-range temporal recognition problem, especially with large intraclass distances such as carrying sma...

Effect of the Hybrid Assistive Limb on the Gait Pattern for Cerebral Palsy.

Medicina (Kaunas, Lithuania)
Cerebral palsy (CP) is the most frequent childhood motor disability. Achieving ambulation or standing in children with CP has been a major goal of physical therapy. Recently, robot-assisted gait training using the Hybrid Assistive Limb (HAL) has bee...

Identification of Sleep Apnea Severity Based on Deep Learning from a Short-term Normal ECG.

Journal of Korean medical science
BACKGROUND: This paper proposes a novel method for automatically identifying sleep apnea (SA) severity based on deep learning from a short-term normal electrocardiography (ECG) signal.

Unobtrusive detection of Parkinson's disease from multi-modal and in-the-wild sensor data using deep learning techniques.

Scientific reports
Parkinson's Disease (PD) is the second most common neurodegenerative disorder, affecting more than 1% of the population above 60 years old with both motor and non-motor symptoms of escalating severity as it progresses. Since it cannot be cured, treat...

Phase imaging with computational specificity (PICS) for measuring dry mass changes in sub-cellular compartments.

Nature communications
Due to its specificity, fluorescence microscopy has become a quintessential imaging tool in cell biology. However, photobleaching, phototoxicity, and related artifacts continue to limit fluorescence microscopy's utility. Recently, it has been shown t...