Diabetes mellitus (DB) is the most challenging and fastest-growing global public health challenge. An estimated 10.5% of the global adult population suffers from diabetes, and almost half of them are undiagnosed. The growing at-risk population exacerbated the shortage of health resources, with an estimated 10.6% and 6.2% of adults worldwide having impaired glucose tolerance (IGT) and impaired fasting glycemia (IFG), respectively. All the current diabetes screening methods are invasive and opportunistic and must be conducted in a hospital or a laboratory by trained professionals. At-risk subjects might remain undetected for years and miss the precious time window for early intervention in preventing or delaying the onset of diabetes and its complications. This study was conducted at KK Womenâ\euro™s and Childrenâ\euro™s Hospital of Singapore, and five hundred participants were recruited (mean age 38.73 ± 10.61 years; mean BMI 24.4 ± 5.1 kg/m2). The blood glucose levels, for most participants, were measured before and after 75g of sugary drink using both the conventional glucometer (Accu-Chek Performa) and the wrist-worn wearable. The results obtained from the glucometer were used as the ground truth measurements. We propose leveraging photoplethysmography (PPG) sensors and machine learning techniques to incorporate this into an affordable wrist-worn wearable device to detect elevated blood glucose levels (⩾ 7.8mmol/L) non-invasively. Multiple machine learning models were trained and assessed with 10-fold cross-validation using subject demographic data and critical features extracted from the PPG measurements as predictors. Support vector machine (SVM) with a radial basis function kernel has the best detection performance with an average accuracy of 84.7%, a sensitivity of 81.05%, a specificity of 88.3%, a precision of 87.51%, a geometric mean of 84.54% and F-score of 84.03%. Hence, PPG measurements can be utilized to identify subjects with elevated blood glucose measurements and assist in the screening of subjects for diabetes risk.

Freezing of Gait is the most disabling gait disturbance in Parkinson’s disease. For the past decade, there has been a growing interest in applying machine learning and deep learning models to wearable sensor data to detect Freezing of Gait episodes. In our study, we recruited sixty-seven Parkinson’s disease patients who have been suffering from Freezing of Gait, and conducted two clinical assessments while the patients wore two wireless Inertial Measurement Units on their ankles. We converted the recorded time-series sensor data into continuous wavelet transform scalograms and trained a Convolutional Neural Network to detect the freezing episodes. The proposed model achieved a generalisation accuracy of 89.2% and a geometric mean of 88.8%.