(1) Background: This study aims to investigate the correlation between heart rate variability (HRV) during exercise and recovery periods and the levels of anxiety and depression among college students. Additionally, the study assesses the accuracy of a multilayer perceptron-based HRV analysis in predicting these emotional states. (2) Methods: A total of 845 healthy college students, aged between 18 and 22, participated in the study. Participants completed self-assessment scales for anxiety and depression (SAS and PHQ-9). HRV data were collected during exercise and for a 5-min period post-exercise. The multilayer perceptron neural network model, which included several branches with identical configurations, was employed for data processing. (3) Results: Through a 5-fold cross-validation approach, the average accuracy of HRV in predicting anxiety levels was 89.3% for no anxiety, 83.6% for mild anxiety, and 74.9% for moderate to severe anxiety. For depression levels, the average accuracy was 90.1% for no depression, 84.2% for mild depression, and 82.1% for moderate to severe depression. The predictive R-squared values for anxiety and depression scores were 0.62 and 0.41, respectively. (4) Conclusions: The study demonstrated that HRV during exercise and recovery in college students can effectively predict levels of anxiety and depression. However, the accuracy of score prediction requires further improvement. HRV related to exercise can serve as a non-invasive biomarker for assessing psychological health.

(1) Background: The objective of this study was to recognize tai chi movements using inertial measurement units (IMUs) and temporal convolutional neural networks (TCNs) and to provide precise interventions for elderly people. (2) Methods: This study consisted of two parts: firstly, 70 skilled tai chi practitioners were used for movement recognition; secondly, 60 elderly males were used for an intervention study. IMU data were collected from skilled tai chi practitioners performing Bafa Wubu, and TCN models were constructed and trained to classify these movements. Elderly participants were divided into a precision intervention group and a standard intervention group, with the former receiving weekly real-time IMU feedback. Outcomes measured included balance, grip strength, quality of life, and depression. (3) Results: The TCN model demonstrated high accuracy in identifying tai chi movements, with percentages ranging from 82.6% to 94.4%. After eight weeks of intervention, both groups showed significant improvements in grip strength, quality of life, and depression. However, only the precision intervention group showed a significant increase in balance and higher post-intervention scores compared to the standard intervention group. (4) Conclusions: This study successfully employed IMU and TCN to identify Tai Chi movements and provide targeted feedback to older participants. Real-time IMU feedback can enhance health outcome indicators in elderly males.

The purpose of this study was to investigate the effects of surface slope and body posture (i.e., seated and standing) on lower extremity joint kinetics during cycling. Fourteen participants cycled at 250 watts power in three cycling conditions: level seated, uphill seated and uphill standing at a 14% slope. A motion analysis system and custom instrumented pedal were used to collect the data of fifteen consecutive cycles of kinematics and pedal reaction force. One crank cycle was equally divided into four phases (90° for each phase). A two-factor repeated measures MANOVA was used to examine the effects of the slope and posture on the selected variables. Results showed that both slope and posture influenced joint moments and mechanical work in the hip, knee and ankle joints (p < 0.05). Specifically, the relative contribution of the knee joint to the total mechanical work increased when the body posture changed from a seated position to a standing position. In conclusion, both surface slope and body posture significantly influenced the lower extremity joint kinetics during cycling. Besides the hip joint, the knee joint also played the role as the power source during uphill standing cycling in the early downstroke phase. Therefore, adopting a standing posture for more power output during uphill cycling is recommended, but not for long periods, in view of the risk of knee injury.