Search

Neuromuscular fatigue affects workers’ productivity and health, which is further deteriorated with chronic conditions such as type 1 diabetes (T1D). Enhanced physiological tremor, a key indicator of neuromuscular fatigue, shows great potential in detecting the onset of neuromuscular fatigue. This study aims to determine the feasibility of using a cost-effective wearable accelerometer-based microelectromechanical sensor to convey neuromuscular fatigue-related tremor information in healthy and T1D adults. 42 adults (22 healthy, 20 T1D), equipped with a finger and a wrist accelerometer, performed intermittent submaximal isometric handgrip fatigue exercises using a grip dynamometer. Motor variability feature, namely, Coefficient of Variation (CV), and motor complexity feature, namely approximate entropy (ApEn), were extracted from the force signal of dynamometer and from the finger and wrist tremor accelerometry signals and subjected to statistical analysis. First, significant positive correlations were found between tremor accelerometry and force signal in terms of motor variability and complexity features. Second, a three-way (fatigue phase: early, middle, late; gender: male, female; condition: healthy, T1D) analysis of variance resulted in a significant fatigue effect on both accelerometry and force measurements in terms of motor variability and complexity features. Apart from finger CV, no other features showed any gender or condition effects. These findings indicate that finger and wrist tremors measured by accelerometer-based sensors can retain the robustness of fatigue-related motor variability and complexity. Wrist tremor features were found to capture fatigue development across both healthy and diabetic males and females, thereby offering comparable fatigue detection and management in adults with chronic conditions. [ABSTRACT FROM AUTHOR]

Major depressive disorder (MDD) has shown to negatively impact physical recovery in a variety of medical events (e.g., stroke and spinal cord injuries). Yet depression assessments, which are typically subjective in nature, are seldom considered to develop or guide rehabilitation strategies. The present study developed a predictive depression assessment technique using functional near-infrared spectroscopy (fNIRS) that can be rapidly integrated or performed concurrently with existing physical rehabilitation tasks. Thirty-one volunteers, including 14 adults clinically diagnosed with MDD and 17 healthy adults, participated in the study. Brain oxy-hemodynamic (HbO) responses were recorded using a 16-channel wearable continuous-wave fNIRS device while the volunteers performed the Grasp and Release Test in four 16-minute blocks. Ten features, extracted from HbO signals, from each channel served as inputs to XGBoost and Random Forest algorithms developed for each block and combination of successive blocks. Top 5 common features resulted in a classification accuracy of 92.6%, sensitivity of 84.8%, and specificity of 91.7% using the XGBoost classifier. This study identified mean HbO, full width half maximum and kurtosis, as specific neuromarkers, for predicting MDD across specific depression-related regions of interests (i.e., dorsolateral and ventrolateral prefrontal cortex). Our results suggest that a wearable fNIRS head probe monitoring specific brain regions, and limiting extraction to few features, can enable quick setup and rapid assessment of depression in patients. The overarching goal is to embed predictive neurotechnology during post-stroke and post-spinal-cord-injury rehabilitation sessions to monitor patients’ depression symptomology so as to actively guide decisions about motor therapies. [ABSTRACT FROM AUTHOR]