Wearable sensor platforms for real-time monitoring and early warning of metabolic disorders in humans.
Ravikumar AyyanuAmutha ArulNinghui SongA Anand Babu ChristusXuesong LiG TamilselvanYuanqing BuS KavithaZhen ZhangNan LiuPublished in: The Analyst (2023)
Nowadays, the prevalence of metabolic syndromes (MSs) has attracted increasing concerns as it is closely related to overweight and obesity, physical inactivity and overconsumption of energy, making the diagnosis and real-time monitoring of the physiological range essential and necessary for avoiding illness due to defects in the human body such as higher risk of cardiovascular disease, diabetes, stroke and diseases related to artery walls. However, the current sensing techniques are inconvenient and do not continuously monitor the health status of humans. Alternatively, the use of recent wearable device technology is a preferable method for the prevention of these diseases. This can enable the monitoring of the health status of humans in different health domains, including environment and structure. The use wearable devices with the purpose of facilitating rapid treatment and real-time monitoring can decrease the prevalence of MS and long-time monitor the health status of patients. This review highlights the recent advances in wearable sensors toward continuous monitoring of blood pressure and blood glucose, and further details the monitoring of abnormal obesity, triglycerides and HDL. We also discuss the challenges and future prospective of monitoring MS in humans.
Keyphrases
- cardiovascular disease
- blood pressure
- blood glucose
- type diabetes
- heart rate
- healthcare
- public health
- end stage renal disease
- mass spectrometry
- multiple sclerosis
- chronic kidney disease
- risk factors
- mental health
- glycemic control
- physical activity
- metabolic syndrome
- ms ms
- insulin resistance
- ejection fraction
- skeletal muscle
- adipose tissue
- brain injury
- risk assessment
- patient reported outcomes
- health information
- cerebral ischemia
- sensitive detection