Wearable sensors

Wearable sensors that detect and quantify biomarkers in retrievable biofluids (e.g., interstitial fluid, sweat, tears) provide information on human dynamic physiological and psychological states. This information can transform health and wellness by providing actionable feedback. Due to outdated and insufficiently sensitive technologies, current on-body sensing systems have capabilities limited to pH, and a few high-concentration electrolytes, metabolites, and nutrients. As such, wearable sensing systems cannot detect key low-concentration biomarkers indicative of stress, inflammation, metabo

Parkinson’s disease (PD) is associated with an increased risk of falls. For people living with PD, experiencing a fall can lead to multiple injuries and significant health decline. An accurate falls risk assessment is therefore needed for care planning and targeted fall prevention programmes. Traditional fall risk assessment relies on clinical assessment which may be subjective and time-consuming. More recent approaches utilised sensor data but so far offered only up to one year of follow-up. We tested whether a short sensor-based assessment can predict the first fall in up to five years. 104

…internet use, healthcare records, wearable sensors or participation in experimental research. This vast amount of data can be used to make predictions about people and systems: what is the probability this person will develop diabetes in the next year? Will commit a crime? Will be a good employee? Is of a particular ethnicity? Predictions are simply represented by a number, produced by an algorithm. A single number in itself is not biased. How that number was generated, interpreted and subse