###### Abstract  
The objective of this study was to use machine learning to identify feelings of energy and fatigue using single-task walking gait. Participants (\(n=126\)) were recruited from a university community and completed a single protocol where current feelings of energy and fatigue were measured using the Profile of Moods Survey-Short Form approximately 2 min prior to participants completing a two-minute walk around a 6 m track wearing APDM mobility monitors. Gait parameters for upper and lower extremity, neck, lumbar and trunk movement were collected. Gradient boosting classifiers were the most accurate classifiers for both feelings of energy (74.3%) and fatigue (74.2%) and Random Forest Regressors were the most accurate regressors for both energy (0.005) and fatigue (0.007). ANCOVA analyses of gait parameters comparing individuals who were high or low energy or fatigue suggest that individuals who are low energy have significantly greater errors in walking gait compared to those who are high energy. Individuals who are high fatigue have more symmetrical gait patterns and have trouble turning when compared to their low fatigue counterparts. Furthermore, these findings support the need to assess energy and fatigue as two distinct unipolar moods as the signals used by the algorithms were unique to each mood. energy; fatigue; falls; machine learning; walking gait +
Footnote †: journal:  
Significant evidence exists on the use of machine learning to identify fatigue through the use of inertial movement unit (IMUs) sensors [11; 12; 13; 14; 15; 16]. However, the primary limitation of these studies [12; 13; 14; 15; 16] is that fatigue was measured as a perception of effort through using a Ratings of Perceived Exertion (RPE) scale. While many researchers use RPE as a measure of fatigue, exercise studies suggest that this scale may not be a good measure of subjective perceptions of fatigue [17; 18; 19; 20; 21]. Another limitation in current literature seeking to identifying fatigue through human movement is that these studies measured fatigue as the lack of energy [6; 7; 8; 9; 10], rather than measuring energy and fatigue as two separate unipolar moods. Significant evidence exists that energy and fatigue are distinct unipolar moods with their own unique biological [22; 23; 24; 25; 26; 27] and behavioral correlates [24; 28; 29]. Recently, studies that have examined energy and fatigue as two separate unipolar moods have also reported their distinct impact on gait and balance [30; 31; 32; 33; 34; 35]. For example, [PERSON] and colleagues [30] examined feelings of energy and fatigue as two separate unipolar moods, and the results of that study reported that declines in feelings of energy resulted in declines in Berg Balance Test (BBT) scores in a group of older adults (>65 years). Other findings suggest that feelings of energy are associated with performance on gait associated functional tests [33; 34], while feelings of fatigue were not. However, the gait and balance parameters reported in these studies [30; 33; 34] were gross measures that provided limited information for researchers interested in identifying feelings of fatigue or energy through human movement. Studies that have taken a more granular approach to identifying the influence of feelings of energy and fatigue as two distinct unipolar moods on gait and balance have primarily examined these two constructs in young adults. These studies provide evidence that the two moods influence gait and balance in very distinct ways [31; 32; 35]. For example, [PERSON] and colleagues [32] report feelings of energy being associated with upper extremity and lumbar movement variability, while feelings of fatigue were associated with lateral step variability and step length [31]. [PERSON] and colleagues [35] suggest that current feelings of energy were associated with gait patterns most often associated with increased risks of tripping, while current feelings of fatigue were associated with slowing of gait, most often associated with increased guarding.