Browsing by Author "Soares, Salviano Pinto"
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- Automatic Fall Detection with Thermal CameraPublication . Kalbermatter, Rebeca B.; Franco, Tiago; Pereira, Ana I.; Valente, António; Soares, Salviano Pinto; Lima, JoséPeople are living longer, promoting new challenges in healthcare. Many older adults prefer to age in their own homes rather than in healthcare institutions. Portugal has seen a similar trend, and public and private home care solutions have been developed. However, age-related pathologies can affect an elderly person’s ability to perform daily tasks independently. Ambient Assisted Living (AAL) is a domain that uses information and communication technologies to improve the quality of life of older adults. AI-based fall detection systems have been integrated into AAL studies, and posture estimation tools are important for monitoring patients. In this study, the OpenCV and the YOLOv7 machine learning framework are used to develop a fall detection system based on posture analysis. To protect patient privacy, the use of a thermal camera is proposed to prevent facial recognition. The developed system was applied and validated in the real scenario.
- Comparative Analysis of Windows for Speech Emotion Recognition Using CNNPublication . Teixeira, Felipe; Soares, Salviano Pinto; Abreu, J.L. Pio; Oliveira, Paulo M.; Teixeira, João PauloThe paper presents the comparison of accuracy in the Speech Emotion Recognition task using the Hamming and Hanning windows for framing the speech and determining the spectrogram to be used as input of a convolutional neural network. The detection of between 4 and 10 emotional states was tested for both windows. The results show significant differences in accuracy between the two window types and provide valuable insights for the development of more efficient emotional state detection systems. The best accuracy between 4 and 10 emotions was 64.1% (4 emotions), 57.8% (5 emotions), 59.8% (6 emotions), 48.4% (7 emotions), 47.8% (8 emotions), 51.4% (9 emotions), and 45.9% (10 emotions). These accuracy is at the state-of-the art level.
- A performance comparison between different industrial real-time indoor localization systems for mobile platformsPublication . Rebelo, Paulo M.; Lima, José; Soares, Salviano Pinto; Oliveira, Paulo; Sobreira, Héber; Costa, PedroThe flexibility and versatility associated with autonomous mobile robots (AMR) have facilitated their integration into different types of industries and tasks. However, as the main objective of their implementation on the factory floor is to optimize processes and, consequently, the time associated with them, it is necessary to take into account the environment and congestion to which they are subjected. Localization, on the shop floor and in real time, is an important requirement to optimize the AMRs’ trajectory management, thus avoiding livelocks and deadlocks during their movements in partnership with manual forklift operators and logistic trains. Threeof the most commonly used localization techniques in indoor environments (time of flight, angle of arrival, and time difference of arrival), as well as two of the most commonly used indoor localization methods in the industry (ultra-wideband, and ultrasound), are presented and compared in this paper. Furthermore, it identifies and compares three industrial indoor localization solutions: Qorvo, Eliko Kio, and Marvelmind, implemented in an industrial mobile platform, which is the main contribution of this paper. These solutions can be applied to both AMRs and other mobile platforms, such as forklifts and logistic trains. In terms of results, the Marvelmind system, which uses an ultrasound method, was the best solution.