Static balance has a relevant influence on athletic performance as well as on reducing the risk of injury. The main goal of this study was to assess soccer athlete versus non-athlete balance performance via displacement and velocity parameters extracted from the center-of-pressure (COP) position time series. In order to accomplish our goal, we investigated standing balance in two male groups with unimpaired balance: non-athletes (n = 12) and collegiate varsity soccer athletes (n = 12). In order to make the standing balancing task more or less difficult, we altered participant base-of-support, as well as vision, yielding static (quiet stance) test conditions increasing in difficulty. From the COP position time series, displacement and velocity parameters were computed and plotted as a function of increasing test condition difficulty level. COP parameters showed steeper increases with increased test difficulty in non-athletes compared to athletes; this demonstrated athletes’ better ability to control their balance. We concluded that balance performance could be characterized via COP displacement and velocity response curves. This study lends new insights into how COP parameters can be utilized to determine and characterize improvements in balance between un-impaired subject populations (athletes versus non-athletes).

Increasing balance confidence in older individuals is important towards improving their quality of life and reducing activity avoidance. Here, we investigated if balance confidence (perceived ability) and balance performance (ability) in older adults were related to one another and would improve after balance training. The relationship of balance confidence in conjunction with balance performance for varied conditions (such as limiting vision, modifying somatosensory cues, and also base of support) was explored. We sought to determine if balance confidence and ability, as well as their relationship, could change after several weeks of training. Twenty-seven healthy participants were trained for several weeks during standing and walking exercises. In addition, seven participants with a higher risk of imbalance leading to falls (survivors of stroke) were also trained. Prior to and after training, balance ability and confidence were assessed via the Balance Error Scoring System (BESS) and Activities Specific Balance Confidence (ABC) Scale, respectively. Both groups showed improvements in balance abilities (i.e., BESS errors significantly decreased after training). Balance confidence was significantly higher in the healthy group than in the stroke group; however, ABC results reflected that balance confidence did not significantly increase after training for each. The correlations between balance ability and balance confidence were explored. Encouragingly, healthy participants displayed a negative correlation between BESS errors and ABC (i.e., enhancements in balance confidence (increases in ABC Scale results) were related to improvements in balance ability (decreases in BESS errors)). For the stroke participants, despite improvements in balance ability, our results showed that there was no relation to balance confidence (i.e., no correlation between BESS errors and ABC) in this group.

Hyperspectral imaging is an area of active research with many applications in remote sensing, mineral exploration, and environmental monitoring. Deep learning and, in particular, convolution‐based approaches are the current state‐of‐the‐art classification models. However, in the presence of noisy hyperspectral datasets, these deep convolutional neural networks underperform. In this paper, we proposed a feature augmentation approach to increase noise resistance in imbalanced hyperspectral classification. Our method calculates context‐based features, and it uses a deep convolutional neuronet (DCN). We tested our proposed approach on the Pavia datasets and compared three models, DCN, PCA + DCN, and our context‐based DCN, using the original datasets and the datasets plus noise. Our experimental results show that DCN and PCA + DCN perform well on the original datasets but not on the noisy datasets. Our robust context‐based DCN was able to outperform others in the presence of noise and was able to maintain a comparable classification accuracy on clean hyperspectral images.

This paper proposes an advanced approach to land cover classification by integrating hyperspectral and LiDAR data to leverage their complementary strengths. Hyperspectral sensors capture detailed reflectance across numerous spectral bands, providing unique spectral signatures that differentiate various land cover types. On the other hand, LiDAR technology offers high-resolution elevation data, enabling accurate characterization of surface structures. This paper details the process of converting unorganized LiDAR point clouds into organized formats for effective analysis, followed by a robust data fusion technique that combines spectral and spatial information. Utilizing a land cover classification methodology on the MUUFL Gulfport dataset, collected in November 2010, we evaluate the effectiveness of our approach by comparing classification accuracies of multiple machine learning algorithms, including kernel classification model, support vector machines, and others. Our results demonstrate significant improvements in classification accuracy when hyperspectral and LiDAR data are fused, highlighting the efficacy of this integrated methodology for complex land cover analysis.

For the rapidly growing aging demographic worldwide, robotic training methods could be impactful towards improving balance critical for everyday life. Here, we investigated the hypothesis that non-bodyweight supportive (nBWS) overground robotic balance training would lead to improvements in balance performance and balance confidence in older adults. Sixteen healthy older participants (69.7 ± 6.7 years old) were trained while donning a harness from a distinctive NaviGAITor robotic system. A control group of 11 healthy participants (68.7 ± 5.0 years old) underwent the same training but without the robotic system. Training included 6 weeks of standing and walking tasks while modifying: (1) sensory information (i.e., with and without vision (eyes-open/closed), with more and fewer support surface cues (hard or foam surfaces)) and (2) base-of-support (wide, tandem and single-leg standing exercises). Prior to and post-training, balance ability and balance confidence were assessed via the balance error scoring system (BESS) and the Activities specific Balance Confidence (ABC) scale, respectively. Encouragingly, results showed that balance ability improved (i.e., BESS errors significantly decreased), particularly in the nBWS group, across nearly all test conditions. This result serves as an indication that robotic training has an impact on improving balance for healthy aging individuals.

Each year, over 25% of adults aged sixty-five years old or older suffer a fall, and three million are treated for fall-related injuries due to lack of balance. Here, we aimed to investigate how virtual reality (VR)-based training affects balance performance and confidence in older adults. To accomplish this goal, we studied 21 healthy, older individuals between 60 and 85 years old, both pre- and post-training (6 weeks of training, twice per week (or 12 sessions)). The VR group donned an Oculus VR headset and consisted of nine participants (aged 75.9 $\pm$ 3.7 years old), while the control group (aged 75.1 $\pm$ 6.7 years old) performed training without a headset and consisted of eight participants that completed our study. To assess balance ability, we utilized the Balance Error Scoring System (BESS) and the Timed Up and Go (TUG) test. To assess balance confidence, we implemented the Activities-Specific Balance Confidence (ABC) Scale and, to assess fear of falling, the Tinetti Falls Efficacy Scale (FES). Further, we assessed depression (via the Geriatric Depression Scale (GDS)) and cognitive ability (via the Mini-Mental State Examination (MMSE)). The post-training results showed improvements in balance ability for both the VR and control groups, as well as changes in the relationship between balance confidence and balance ability for the VR group only. Further, improvements in cognitive ability were seen in the control group. This study is an indication that older individuals’ balance ability may benefit from several weeks of targeted training.