In recent years, there has been intensified development of cost-effective alternatives to conventional force plates, such as those of Kistler and AMTI, in clinical and scientific research. To our knowledge, the evidence for existing validity and reliability of the HUMAC Balance System has so far been lacking. Over the course of the study, the HUMAC Balance System, which is based on the WII Balance Board technology, was tested. 26 HUMAC Balance Systems in conjunction with 2 force plates, each from the manufacturers Kistler and AMTI, were examined and their properties under static and dynamic conditions were evaluated. Under static conditions, the HUMAC Balance System revealed a relatively constant and comparatively small margin of displacement error. However, tests of the force plates under dynamic conditions revealed contrasting results. The conventional force plates, with a margin of error of 2.5%, proved to have a smaller margin of error compared to the HUMAC Balance System, which had margins of error of 8.6% and 6.1% in x and y directions, respectively. A calibration method was developed to overcome these systematic deviations. After applying the calibration, the Balance Board margin of error was reduced to an acceptable maximum of 0.18%.

The center of pressure (CoP) is one of the most utilized quantitative measurements describing postural competency. Due to the complexity and biological variability of postural regulatory systems, a myriad of different methods and parameters have been established describing the CoP trajectory. Besides procedural variables, such as foot position, visual condition, and sampling duration, the method of data collection itself has a relevant effect on the result of the measurement. Furthermore, different methods for recording the measured data have been developed, which differ regarding the filters, frequencies, and test durations used. The goal of this study was the methodical comparison of various filters, measurement frequencies, and measurement duration, with respect to their effects on the CoP trajectory. Based on the results presented, we demonstrate that the Butterworth and Bessel filters can be recommended for analysis of CoP data, and at the very least, a second-order filter should be chosen for the process. For assessment of the cutoff frequency, a technical pendulum was used to show that a cutoff frequency of 13 Hz provided reliable data and it can be inferred that a 100 Hz sampling rate would be the minimum requirement.

The measurement of the center of pressure (CoP) is one of the most frequently used quantitative methods for quantifying postural performance. Due to the complexity and the high biological variability of the postural control loop, a large number of different methods and parameters have been established to describe the CoP process. Furthermore, the methodological conditions such as the foot position, visual condition, sampling duration, and the data processing also have a relevant influence on the measurement results. In addition, there are various methods for recording the pressure curve, which differ in particular with regard to the filters used, the frequencies, and measurement times. The aim of the present study was the methodical comparison between different digital filters, measurement frequencies and times, and their effects on the CoP process based on a healthy reference group. The data acquisition was done with LabVIEW and the data storage was organized in a subject oriented data structure. Based on the presented results it could be seen that with a different dominant frequency in the spectrum of the group of test persons, certain filter types are required for the processing of CoP data. In the sampling range from 300 Hz to 1 kHz in the bipedal stand and 600 Hz to 1 kHz in the monopedal stand, the choice of measurement frequency had no influence on the filter result.

Good postural control is considered to be a key component of an active lifestyle, and numerous studies have investigated the Center of Pressure (CoP) as a way of identifying motor deficits. However, the optimal frequency range for assessing CoP variables and the effect of filtering on the relationships between anthropometric variables and CoP are unclear. The aim of this work is to show the relationship between anthropometric variables and different ways of filtering the CoP data. CoP was measured in 221 healthy volunteers using a KISTLER force plate in four different test conditions, both mono and bipedal. The results show no significant changes in the existing correlations of the anthropometric variable values over different filter frequencies between 10 Hz and 13 Hz. Therefore, the findings with regard to anthropometric influences on CoP, with a reasonable but less than ideal filtering of the data, can be applied to other study settings.

Measuring of the center of pressure (CoP) is one of the most frequently used quantitative methods for quantifying postural performance. The aim of the study is to describe differentiation criteria in the CoP-track for the clinical picture of chronic unspecific back pain. In this study, dynamic models loaded with multi-variable controls are used to determine whether biomechanical questions for upright posture can be answered. These models are particularly well suited for investigating the kinematics and the influence of the influencing disturbance variables. These investigations are extended by power density spectrum (PSD) analyses of CoP measurements on 590 subjects with and without chronic non-specific back pain. Pain patients show an average of 0.5 Nm² more area under the spectrum than the pain-free reference group. In the power density spectrum different frequency ranges can be assigned to specific body oscillation. Among others, the frequency range between 0.5–0.8 Hz corresponds to the hip movement. In the range around 0.2 Hz, the movements are reflected in the upper body. Patients with back pain experience less activity in certain, individual areas.

In recent years, energy prices have become increasingly volatile, making it more challenging to predict them accurately. This uncertain market trend behavior makes it harder for market participants, e.g., power plant dispatchers, to make reliable decisions. Machine learning (ML) has recently emerged as a powerful artificial intelligence (AI) technique to get reliable predictions in particularly volatile and unforeseeable situations. This development makes ML models an attractive complement to other approaches that require more extensive human modeling effort and assumptions about market mechanisms. This study investigates the application of machine and deep learning approaches to predict day-ahead electricity prices for a 7-day horizon on the German spot market to give power plants enough time to ramp up or down. A qualitative and quantitative analysis is conducted, assessing model performance concerning the forecast horizon and their robustness depending on the selected hyperparameters. For evaluation purposes, three test scenarios with different characteristics are manually chosen. Various models are trained, optimized, and compared with each other using common performance metrics. This study shows that deep learning models outperform tree-based and statistical models despite or because of the volatile energy prices.