VivaLNK IoT platform captures human vitals
February 19, 2019
VivaLNK, a California-based provider of connected healthcare, has announced an IoT-enabled medical wearable sensor platform with a range of sensors, edge computing technologies and data cloud.
This platform captures human vitals and biometrics, and delivers data from the patient to edge computing devices, as well as to the cloud, for application integration and analysis.
Available through the firm’s developer programme, the sensor platform lets IoT partners capture streams of patient data such as heart and respiratory rates, temperature, ECG rhythms, activity and more. Partners such as Vitalic Medical, a digital health innovator in the early detection of patient health deterioration and potential falls, is developing a bedside monitor using the platform.
"Our growing aging patient population, rising complex health conditions and increasing staff workloads make it challenging for medical professionals to detect early signs of patient deterioration and prevent falls," said Sue Dafnias, CEO of Vitalic Medical. "By leveraging VivaLNK wearable sensor within the Vitalic platform, Vitalic Medical can offer an early trigger system that helps nurses identify early signs of patient deterioration and fall-risk patients."
IoT has the potential to change healthcare significantly for the better, and the key starts with data. Much of the machine learning and intelligence will come from user generated data that currently do not exist or are not easily accessible. This is where wearable devices collecting medical-grade data that can easily connect to networked applications become crucial.
"The launch of our sensor platform is instrumental in helping partners accelerate medical and healthcare innovation to market, especially within crucial therapeutic areas such as cardiology, cancer, chronic disease and more," said Jiang Li, CEO at VivaLNK. "To have a more complete picture of patient health, applications and algorithms not only need access to data, but need a variety of relevant data that can be used to correlate and accurately assess and predict health situations."