High-performance sensing and control systems have an important role in Industry 4.0. However, with the current solutions, the development effort is high and requires specialized skills in electronic engineering. Therefore, a model-based approach on control and signal processing systems using affordable heterogeneous hardware is proposed. In this work, a model-based code generator is developed to abstract the user from the actual software implementation. Starting from a combined model of a timing diagram and an embedded platform, a model transformation is used to automatically generate functional acquisition firmware. This firmware generator enables system engineers without deep software and hardware knowledge to set up complex control systems. Furthermore, it equips software engineers with a solid framework for faster development.
BACKGROUND: Repetitive actions while playing piano may overload forearm muscles and tendons, leading to playing-related musculoskeletal disorders (PRMDs), including lateral epicondylitis. METHODS: In this pilot study, surface electromyography (sEMG) activity of the extensor carpi radialis (ECR) was captured in 10 conservatory piano students while playing a fast and a slow music score selected from the individual’s repertoire, each 3 minutes long. Measurements were made at baseline and again after 2 hrs and 4 hrs of rehearsal time of the piano études. The amplitude of the sEMG signal was processed by a smoothing algorithm, and the frequency component with a non-orthogonal wavelets procedure. Amplitude of the sEMG was expressed in percent of maximal voluntary contraction (%MVC) at baseline. Statistical analysis encompassed 2-way repeated measures ANOVAs for the amplitude and frequency components of the sEMG signal (a set at 5%). The students also rated the intensity of rehearsals using a VAS. RESULTS: The ECR presented with a mean amplitude of 23%MVC for the slow scores, which increased significantly to 36%MVC for the fast scores. The sEMG signal presented a significant though small decrease of 1.9%MVC in amplitude between baseline and 4 hrs of rehearsal time and no shift in frequency, which may indicate that the rehearsals were held at a physiological steady-state and suggesting optimization or complementary muscle loading. CONCLUSIONS: These data accentuated that the loading of the ECR (as reflected in the amplitude component) was higher than that seen for computer keyboard workers. The augmented loading of the ECR and reduced blood flow to forearm muscles may be a factor in the development of PRMDs in pianists.
BACKGROUND: Repetitive piano play may overload neck and shoulder muscles and tendons, leading to playing-related musculoskeletal disorders (PRMDs). METHODS: In this pilot study (EMG data of the extensor carpi radialis have been published separately), surface electromyography (sEMG) activity of the upper trapezius (UT) was captured in 10 conservatory piano students while playing a fast and a slow music score selected from the individual’s repertoire, each 3 minutes long. Measurements were made at baseline and again after 2 hrs and 4 hrs of rehearsal time of the piano études. The amplitude of the sEMG signal was processed by a smoothing algorithm, and the frequency component with a non-orthogonal wavelets procedure. Amplitude of the sEMG was expressed in percent of maximal voluntary contraction (%MVC) at baseline, and the frequency component using median frequency based on the frequency band powers. Statistical analysis encompassed repeated measures ANOVAs for the amplitude and frequency components of the sEMG signal (set at 5%). The students also rated the intensity of rehearsals using a visual analog scale (VAS). RESULTS: The median values for the %MVC presented a global mean for the left trapezius of 5.86 (CI90% 4.71, 6.97) and 5.83 for the right trapezius (CI90% 4.64, 7.05). The rehearsals at moderate intensity increased the amplitude of %MVC of the upper trapezius by around 50% and decreased the median frequency. CONCLUSIONS: Playing faster presented higher magnitudes of activity of the upper trapezius. The decrease in the median frequency in response to long rehearsals may be a sign of muscle fatigue.
In this paper, a model-based firmware generator is presented towards complex sampling schemes. The framework is capable of automatically generating a fixed-rate Shannon-compliant acquisition scheme, as well as a variable-rate compressive sensing acquisition scheme. The generation starts from a model definition, which consists of two main components, namely an acquisition sequence to implement and the platform on which the sequence should be implemented. This model is then combined with the specifications to be transformed into a functional firmware. When generating firmware for compressive sensing (CS) purposes, the defined acquisition sequence is automatically generated to implement a pseudo-random sampling scheme in agreement with the defined undersampling factor. The evaluation of the generated firmware is done by means of an example use-case, including a proposed strategy for synchronization between CS setups. This research attempts to reduce the development complexity for embedded CS to lower the threshold towards effective usage in the field.
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