Unobtrusive detection of poor sitting and eating behaviour in a healthy population
Musculoskeletal disorders and metabolic syndrome are two common health issues in the general population. Office workers are often affected due to their sedentary lifestyle. Poor sitting, eating habits, and lack of exercise are among the main risk factors that trigger the development of these two health issues. People are often unaware of the impact these behaviours have on their health because the development of these health problems into chronic diseases takes years.
To support prevention, Linda Ong developed unobtrusive technology for the detection of the main risk factors for metabolic syndrome and musculoskeletal disorders. These include poor sitting and eating behaviour. Ong tested the technology in semi-controlled lab studies and uncontrolled studies. She demonstrates that machine learning-based algorithms that use pressure mat data can predict sitting positions that are related to back pain. Furthermore, Ong demonstrates that continuous glucose measurements have a correlation with carbohydrates and other macronutrients consumed in healthy users. This insight can be used to further develop machine learning prediction algorithms that allow the user to keep track of their food consumption effortlessly. In addition to nutritional content, portion size is an important aspect of diet monitoring. Ong found that the combination of PPG and IMU provides promising results. This combination of sensors is especially interesting because they are commonly integrated in smartwatches and do not interfere with the privacy of the user.
By having these technologies integrated into the consumer device, they have the potential to promote adherence to a healthy lifestyle, which could ultimately contribute to a reduction in the loss of quality-adjusted life years.
See also: Let’s get rid of those inconvenient food diaries for diabetes patients