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• A home study of type 1 diabetes adolescents in a free-living condition is conducted to collect tremor and blood glucose data. • Four machine learning algorithms (decision tree, support vector machine, k-nearest neighbor, and bagged trees ensemble classifier) are applied to find the optimal model that detects hypoglycemia using tremor. • The study demonstrates the feasibility of detecting and predicting the onset of hypoglycemia based on hand tremor. Diabetes is a chronic disease where blood glucose (BG) concentrations are consistently high. A dangerous diabetes-associated condition is hypoglycemia, where BG level drops below the normal threshold. Hypoglycemia is often accommodated by tremors, sweating, fatigue, anxiety, lightheadedness, disorientation, irritability, and tachycardia. Very few studies are focused on detecting enhanced tremor as a peripheral physiological response to declining BG concentrations. This study undertakes a machine-learning approach to predict hypoglycemia using hand tremors. Tremors were detected and characterized by frequency and amplitude in non-hypoglycemic and hypoglycemic conditions. Accelerometers and continuous glucose monitoring devices recorded the tremor and BG datasets. A home study of 32 T1D adolescents in a free-living condition was conducted. Simultaneously recorded accelerometer and continuous glucose monitor (CGM) data of 194.6 h were collected from 15 participants. These data were utilized for training and testing the predictive model. Four lengths of the sliding window (1, 2, 5, and 10 s) and four machine learning algorithms (decision tree, support vector machine, k-nearest neighbor, and bagged trees ensemble classifier) were applied to classify tremor as non-hypoglycemic or hypoglycemic. The greatest accuracy (86.65%) was achieved by the ensemble classifier (bagged trees based on the subspace k-NN) for 1 s window length. Multiple prediction windows are merged to generate aggregate sequential predictions. Prediction accuracy of 86.05% was achieved for a 15 s batch (3 s window length). This study demonstrates the feasibility of detecting and predicting the onset of hypoglycemia based on hand tremor data collected by wrist-worn accelerometer sensors. [ABSTRACT FROM AUTHOR]

Fatigue is defined as "a loss of force-generating capacity" in a muscle that can intensify tremor. Tremor quantification can facilitate early detection of fatigue onset so that preventative or corrective controls can be taken to minimize work-related injuries and improve the performance of tasks that require high-levels of accuracy. We focused on developing a system that recognizes and classifies voluntary effort and detects phases of fatigue. The experiment was designed to extract and evaluate hand-tremor data during the performance of both rest and effort tasks. The data were collected from the wrist and finger of the participant's dominant hand. To investigate tremor, time, frequency domain features were extracted from the accelerometer signal for segments of 45 and 90 samples/window. Analysis using advanced signal processing and machine-learning techniques such as decision tree, k-nearest neighbor, support vector machine, and ensemble classifiers were applied to discover models to classify rest and effort tasks and the phases of fatigue. Evaluation of the classifier's performance was assessed based on various metrics using 5-fold cross-validation. The recognition of rest and effort tasks using an ensemble classifier based on the random subspace and window length of 45 samples was deemed to be the most accurate (96.1%). The highest accuracy (~98%) that distinguished between early and late fatigue phases was achieved using the same classifier and window length. [ABSTRACT FROM AUTHOR]