Computing technologies have opened up a myriad of possibilities for expanding the sonic capabilities of acoustic musical instruments. Musicians nowadays employ a variety of rather inexpensive, wireless sensor-based systems to obtain refined control of interactive musical performances in actual musical situations like live music concerts. It is essential though to clearly understand the capabilities and limitations of such acquisition systems and their potential influence on high-level control of musical processes. In this study, we evaluate one such system composed of an inertial sensor (MetaMotionR) and a hexaphonic nylon guitar for capturing strumming gestures. To characterize this system, we compared it with a high-end commercial motion capture system (Qualisys) typically used in the controlled environments of research laboratories, in two complementary tasks: comparisons of rotational and translational data. For the rotations, we were able to compare our results with those that are found in the literature, obtaining RMSE below 10° for 88% of the curves. The translations were compared in two ways: by double derivation of positional data from the mocap and by double integration of IMU acceleration data. For the task of estimating displacements from acceleration data, we developed a compensative-integration method to deal with the oscillatory character of the strumming, whose approximative results are very dependent on the type of gestures and segmentation; a value of 0.77 was obtained for the average of the normalized covariance coefficients of the displacement magnitudes. Although not in the ideal range, these results point to a clearly acceptable trade-off between the flexibility, portability and low cost of the proposed system when compared to the limited use and cost of the high-end motion capture standard in interactive music setups.
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http://dx.doi.org/10.3390/s20195722 | DOI Listing |
J Electromyogr Kinesiol
January 2025
Research Academy of Grand Health, Faculty of Sports Sciences, Ningbo University, Ningbo, China. Electronic address:
Objective: We investigated the characteristics of hip, knee, and ankle joint reaction forces (JRFs) in stroke patients with spastic hemiplegia during sit-to-stand (Si-St) and stand-to-sit (St-Si) movements and explored the relationship between JRFs and joint moments.
Methods: Thirteen stroke patients with spastic hemiplegia and thirteen age-matched healthy subjects were recruited in this study. Three-dimensional motion capture system and force plates were employed to collect kinematic data and ground reaction forces during Si-St and St-Si tasks.
Sci Total Environ
January 2025
Department of Earth Resources and Environmental Engineering, Hanyang University, Seoul 04763, Republic of Korea. Electronic address:
The CO adsorption capacity of biochar depends on the type of biomass used and its physicochemical properties; various sorption parameters including temperature, CO concentration, and humidity affect the CO adsorption capacity. Biochar derived from defatted black soldier fly larvae (BSFL) biomass was investigated for direct CO capture and exhibited a hydrophilic/mesoporous structure that contained high concentrations of alkali and alkaline metals (>10 wt%), which contribute to CO chemisorption. The CO adsorption efficiency was higher at 25 °C compared with that at 30 °C and 35 °C, probably due to reduced Brownian motion of CO molecules at lower temperatures.
View Article and Find Full Text PDFPLoS One
January 2025
School of Physical Education, Jinjiang College, Sichuan University, Chengdu, Sichuan Province, People's Republic of China.
In athletes' competitions and daily training, in order to further strengthen the athletes' sports level, it is usually necessary to analyze the athletes' sports actions at a specific moment, in which it is especially important to quickly and accurately identify the categories and positions of the athletes, sports equipment, field boundaries and other targets in the sports scene. However, the existing detection methods failed to achieve better detection results, and the analysis found that the reasons for this phenomenon mainly lie in the loss of temporal information, multi-targeting, target overlap, and coupling of regression and classification tasks, which makes it more difficult for these network models to adapt to the detection task in this scenario. Based on this, we propose for the first time a supervised object detection method for scenarios in the field of motion management.
View Article and Find Full Text PDFBehav Res Methods
January 2025
Neuroscience of Perception and Action Lab, Italian Institute of Technology (IIT), Viale Regina Elena 291, 00161, Rome, Italy.
Clin Orthop Relat Res
December 2024
Department of Orthopedics, University of Colorado School of Medicine, Aurora, CO, USA.
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