Athlete’s respiratory frequency and the physical energy consumption model based on speech recognition technology is presented in this paper. We use the series of rotation angles reflects changes in the electrical axis of the heart caused by breathing, and then power spectrum analysis of the breathing signal is used to then estimate the breathing rate. The novelties of the paper are summarized as three aspects. (1) Gaussian mixture model is used to model the speaker. This system has the better noise robustness than the traditional feature parameters at low signal-to-noise ratio. (2) We use the photoelectric sensor technology to measure the heart rate of the human body, which can detect weak pulse signals. (3) The collected respiratory sound signals are processed by the dedicated data compression module and then displayed on the entire screen, as the system will show the analytic results. The experimental results have proven the effectiveness of the proposed methodology.
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This research was supported by the Application Form for General Projects of Philosophy and Social Science Research in Colleges and Universities in Jiangsu Province, Title: Research on the Dilemma and Management Path of Tennis Professionalization in China. Applicant: Yin Shulai 2019SJA0083.
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Yin, S., Fang, H. & Hou, X. Athlete’s respiratory frequency and physical energy consumption model based on speech recognition technology. Int J Speech Technol (2020). https://doi.org/10.1007/s10772-020-09685-z
- Speech recognition
- IoT devices
- Respiratory frequency
- Energy consumption
- Optimization and monitoring