Abstract
One of the urgent and popular research areas is wearable devices-based fall detection (FD). Over the past 20 years, researchers have conducted many experiments in which falls and activities of daily living were simulated. Researchers inferred that real-world fall data is in demand rather than simulated fall data, but this inference still lacks comparisons. In this study, an assessment of a simulated fall dataset and a real-world fall dataset is proposed. The assessment investigates the efficacy of simulated data for developing an FD solution. Comparisons were conducted between FD methods developed on simulated and real-world data to observe the effectiveness of simulated falls. The experiments showed that the method with real-world data offered similar performances to the method with simulated data. In contrast to existing solutions, the provided comparison revealed that accurate simulated data are beneficial for developing a real-world FD solution.
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© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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Sözer, A.T., Almohamad, T.A., Halim, Z.A. (2024). Assessment of Real-World Fall Detection Solution Developed on Accurate Simulated-Falls. In: Ahmad, N.S., Mohamad-Saleh, J., Teh, J. (eds) Proceedings of the 12th International Conference on Robotics, Vision, Signal Processing and Power Applications. RoViSP 2021. Lecture Notes in Electrical Engineering, vol 1123. Springer, Singapore. https://doi.org/10.1007/978-981-99-9005-4_72
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DOI: https://doi.org/10.1007/978-981-99-9005-4_72
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