Classification of Fatigue Phases in Healthy and Diabetic Adults Using Wearable Sensor

Abstract

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.

Machine Learning in Tremor Analysis: Critique and Directions

“…The exact placement of sensors does influence accelerometer signal characteristics. 73,79 Sensor placement in the aforementioned studies was not uniform, ranging from, for example, wrist, 34,36,37,40,44 dorsal middle metacarpal, 38,39,52,54,82 middle phalangeal, 42 distal phalangeal, 32 and middle of the lower arm 49,58 to combinations of, for example, wrist and finger 43,51 or wrist and ankle. 47 Traditionally, sensors have been-with some center-to-center differences-placed on the back of the hand.…”

“…74 Cross-correlation and autocorrelation features are generally accepted as good representatives of tremor characteristics. 89 Most of the reviewed accelerometer publications engineered 1 to 10 features from both time and frequency domains, 34,39,42,47 representing amplitude and regularity, 32,51 spectral power, 51 fast Fourier transform coefficients, 32 and spectrograms. 59 Transitioning from a hypothesis-driven toward a data-driven approach, a selection of studies increased the number of features that they engineered (Fig.…”

A Machine Learning Approach for Walking Classification in Elderly People with Gait Disorders

“…Thus, the performance of ensemble learning models is generally higher than single classification algorithms [ 27 ]. There are various applications in which ensemble learning methods are utilized such as cyber security [ 28 , 29 , 30 , 31 , 32 , 33 ], energy [ 34 , 35 , 36 , 37 ], and health informatics [ 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 ].…”

Hypoglycemia Detection Using Hand Tremors: Home Study of Patients With Type 1 Diabetes

“…Monitoring tremors may provide a cost-effective and nonintrusive method to detect the onset of hypoglycemic events. Accelerometer sensors are validated devices to measure motion and have been used in various applications such as assessing physical activity [25][26][27][28][29][30][31], aiding in the management of Parkinson disease [32][33][34], and gait analysis [35,36]. However, outside of conceptual framework development efforts [37], the only study that attempted to detect hypoglycemia using accelerometer data was our recent work on adolescents with T1DM [38].…”