3,116 research outputs found
CARTA PARA SIMONE
Full text linkO presente manuscrito é uma carta para uma alma desencarnada muito importante nos estudos dos feminismos, Simone de Beauvoir. A autora faz um panorama de como conheceu a obra de Simone de Beauvoir entrelação o período do impeachment de Dilma Rousseff, pesquisa de doutorado em andamento e outros textos de autoras feministas que se utilizaram das reflexões iniciadas por Beauvoir, como Grada Kilomba (2019), Valeska Zanello (2019), bell hook (2018), entre outras
Design challenges for wearable EMG applications
No full textWearable technologies are changing the way we deal with health and fitness in our daily life. Nevertheless, while MEMS-enabled inertial sensors have conquered the consumer market, physiological monitoring has still to face barriers due to the complexity and costs of physical interfaces (e.g. electrodes), the degree of intuitiveness of the interaction and the processing required to reach satisfying performance. These limitations are mitigated by the embedded systems' growing integration of interfacing capabilities and efficient computing power. In this paper, we describe the main applications and the related technologies for the acquisition and processing of myoelectric (EMG) signals. Starting from well established active sensors and bench-top setups, we introduce a recent design based on the combination of an integrated Analog Front End (AFE) and embedded processing. This solution provides high quality signal acquisition and on-board digital processing capabilities with a contained power consumption. The system was tested within the prosthesis control application scenario, one of the most stringent EMG applications, achieving a 90% gesture recognition accuracy with real time on-board processing at a power consumption of 30 mW. Such promising results highlight the current trend in shifting EMG applications from dedicated analog solutions towards integrated digital devices, favouring the development of advanced, modular and low-power wearable solutions
Towards EMG control interface for smart garments
No full textWearable computing devices can greatly enhance the quality of life, helping interaction with smart environment, activity recognition and healthcare applications. Smart garments offer the opportunity to integrate sensors and electronics in unobtrusive wearable systems. The paper presents a case study of an embedded hand gesture recognition system, which uses EMG electrodes embeddable in smart clothes. We analyze the main challenges of a real-time system for pattern recognition and the results of the proposed experiment demonstrate the feasibility of a real-time system for pattern recognition, which can be integrated in smart clothes
Solving Non-smooth Dynamic Problems using the Alternating Direction Method of Multipliers
No full textFlexible, Scalable and Energy Efficient Bio-Signals Processing on the PULP Platform: A Case Study on Seizure Detection
Full text linkUltra-low power operation and extreme energy efficiency are strong requirements for a number of high-growth application areas requiring near-sensor processing, including elaboration of biosignals. Parallel near-threshold computing is emerging as an approach to achieve significant improvements in energy efficiency while overcoming the performance degradation typical of low-voltage operations. In this paper, we demonstrate the capabilities of the PULP (Parallel Ultra-Low Power) platform on an algorithm for seizure detection, representative of a wide range of EEG signal processing applications. Starting from the 28-nm FD-SOI (Fully Depleted Silicon On Insulator) technology implementation of the third embodiment of the PULP architecture, we analyze the energy-efficient implementation of the seizure detection algorithm on PULP. The proposed parallel implementation exploits the dynamic voltage and frequency scaling capabilities, as well as the embedded power knobs of the PULP platform, reducing energy consumption for a seizure detection by up to 10× with respect to a sequential implementation at the nominal supply voltage and by 4.2× with respect to a sequential implementation with voltage scaling. Moreover, we analyze the trans-precision optimization of the algorithm on PULP, by means of a hybrid fixed- and floating-point implementation. This approach reduces the energy consumption by up to 43% with respect to the plain fixed-point and floating-point implementations, leveraging the requirements in terms of the precision of the kernels composing the processing chain to improve energy efficiency. Thanks to the proposed architecture and system-level approach for optimization, we demonstrate that PULP reduces energy consumption by up to 140× with respect to commercial low-power microcontrollers, being able to satisfy the real-time constraints typical of bio-medical applications, breaking the barrier of microwatts for a 50-ms complete seizure detection and a few milliwatts for a 5-ms detection latency on a fully-programmable architecture
Exploring Arm Posture and Temporal Variability in Myoelectric Hand Gesture Recognition
Full text linkHand gesture recognition based on myoelectric (EMG) signals is an innovative approach for the development of intuitive interaction devices, ranging from poliarticulated prosthetic hands to intuitive robot and mobile interfaces. Their study and development in controlled environments provides promising results, but effective real-world adoption is still limited due to reliability problems, such as motion artifacts and arm posture, temporal variability and issues caused by the re-positioning of sensors at each use. In this work, we present an EMG dataset collected with the aim to explore postural and temporal variability in the recognition of arm gestures. Its collection of gestures executed in 4 arm postures over 8 days allows to evaluate the impact of such variability on classification performance. We implemented and tested State-of-the-Art (SoA) recognition approaches analyzing the impact of different training strategies. Moreover, we compared the computational and memory requirements of the considered algorithms, providing an additional evaluation criteria useful for real-time implementation. Results show a decrease in the recognition of inter-posture and inter-day gestures up to 20%. The provided dataset will allow further exploration of such effects and the development of effective training and recognition strategies
Rozpor ako východisko, láska ako smer u Simone Weilovej (Contradiction as base, Love as direction in writings of Simone Weil)
No full textArticle is explaining contradiction and love, Simone Weil‘s essential terms of hermeneutics of human Being. It introduces close relation of these terms with her understanding of God as well as with her overall concept of religion. Author also mentions Simone Weil‘s inspirations with philosophical and spiritual concepts of the East
Hybrid EMG classifier based on HMM and SVM for hand gesture recognition in prosthetics
Full text linkPattern recognition and classification algorithms are widely studied in natural gesture interfaces for upper limb prostheses. Robustness and accuracy of control systems are key challenge in such applications. To improve the classification performance, the conventional approach builds on classifier parameters tuning and/or feature extraction techniques. In this paper, we propose a complementary approach based on the combination of two heterogeneous classifiers: the Support Vector Machines and the Hidden Markov Models. This technique takes advantage of the robust time-independent classification of the SVM taking into account the information about history of the signal with the HMM. We demonstrate that, independently from the performance of the SVM, which can be further optimized with typical methods, the combined approach gains 12% recognition accuracy. We further comment on the applicability of this approach in resource constrained embedded implementations considering real-time requirements in the field of prosthesis control
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