Wearable health devices have been an emerging technology that enables an ambulatory acquisition of physiological signals to monitor health status over a long time (hours/days/weeks/years) inside and outside clinical environments. Big data and deep learning, in particular, are receiving a lot of attention in this rapidly growing digital health community. A key benefit of deep learning is to analyze and learn massive amounts of data, which makes it especially valuable in healthcare since raw data is largely gathered from personalized wearable health devices. A wide range of users may benefit from unobstructed and even remote monitoring of pertinent or vital signs, which makes it easier to detect life-threatening diseases early, track the progression of pathologies and stress levels, evaluate the efficacy of therapies, provide low-cost and reliable diagnoses, etc.
Today’s personal health devices have provided an amazing insight into people’s health and wellness, which allow clinicians to use these smart wearables to collect and analyze measuring data like electroencephalogram (EEG), electrocardiogram (ECG or EKG), respiration, heart rate, temperature level, blood oxygen, and blood pressure for health monitoring or clinical trials. This Research Topic mainly focuses on the technical revolution in wearable health systems, which aims to design more smart and useful wearables, contributing to a substantial change in the methodologies, applications, and algorithms of machine learning for wearable health devices. With the help of deep learning and sensor fusion capabilities from wearable health platforms, this data will be used more effectively, which can help to construct smart, novel, specific solutions to improve the quality of healthcare and capabilities of utilizing new deep learning technologies.
The primary aim of the Research Topic is to seek high-quality contributions that focus on new methodologies, applications, and algorithms of deep learning for wearable health devices. Comprehensive surveys on the state-of-the-art in this field are also encouraged. The article types including Original Research, Reviews, and Mini Review are welcome. We strongly encourage the submissions focusing on topics of interest that include but are not limited to the following (also see the keywords listed):
a. Wearable technology in health care;
b. Artificial intelligence in wearable health;
c. Health monitoring systems for assisted living;
d. Intelligent algorithms that are used to infer information from biomedical data;
e. Statistical classification methods;
f. Neural networks;
g. Deep learning;
h. Sensor fusion technology for health data.
Ming Zeng Ph.D. is a research scientist in Facebook company (is now Meta), a commercial producer of social networking. This should not pose any conflict for this project, as they will maintain objectivity.
Wearable health devices have been an emerging technology that enables an ambulatory acquisition of physiological signals to monitor health status over a long time (hours/days/weeks/years) inside and outside clinical environments. Big data and deep learning, in particular, are receiving a lot of attention in this rapidly growing digital health community. A key benefit of deep learning is to analyze and learn massive amounts of data, which makes it especially valuable in healthcare since raw data is largely gathered from personalized wearable health devices. A wide range of users may benefit from unobstructed and even remote monitoring of pertinent or vital signs, which makes it easier to detect life-threatening diseases early, track the progression of pathologies and stress levels, evaluate the efficacy of therapies, provide low-cost and reliable diagnoses, etc.
Today’s personal health devices have provided an amazing insight into people’s health and wellness, which allow clinicians to use these smart wearables to collect and analyze measuring data like electroencephalogram (EEG), electrocardiogram (ECG or EKG), respiration, heart rate, temperature level, blood oxygen, and blood pressure for health monitoring or clinical trials. This Research Topic mainly focuses on the technical revolution in wearable health systems, which aims to design more smart and useful wearables, contributing to a substantial change in the methodologies, applications, and algorithms of machine learning for wearable health devices. With the help of deep learning and sensor fusion capabilities from wearable health platforms, this data will be used more effectively, which can help to construct smart, novel, specific solutions to improve the quality of healthcare and capabilities of utilizing new deep learning technologies.
The primary aim of the Research Topic is to seek high-quality contributions that focus on new methodologies, applications, and algorithms of deep learning for wearable health devices. Comprehensive surveys on the state-of-the-art in this field are also encouraged. The article types including Original Research, Reviews, and Mini Review are welcome. We strongly encourage the submissions focusing on topics of interest that include but are not limited to the following (also see the keywords listed):
a. Wearable technology in health care;
b. Artificial intelligence in wearable health;
c. Health monitoring systems for assisted living;
d. Intelligent algorithms that are used to infer information from biomedical data;
e. Statistical classification methods;
f. Neural networks;
g. Deep learning;
h. Sensor fusion technology for health data.
Ming Zeng Ph.D. is a research scientist in Facebook company (is now Meta), a commercial producer of social networking. This should not pose any conflict for this project, as they will maintain objectivity.