The Stress Phase Angle Measurement Using Spectral Domain Optical Coherence Tomography

Zhao, Yuqian; Zhu, Zhibo; Jiang, Huiwen; Yu, Yao; Liu, Jian; Luan, Jingmin; Wang, Yi; Ma, Zhenhe

doi:10.3390/s23177597

Cited by 1 publication

(1 citation statement)

References 37 publications

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“…where n is the sample size, x i are the sample observations, x i is the sample mean, and s is the sample standard deviation. Kurtosis [53], on the other hand, measures the tailedness of a probability distribution. It describes the height and sharpness of the central peak relative to that of a standard bell curve.…”

Section: Skewness and Kurtosismentioning

confidence: 99%

A Wearable Inertial Sensor Approach for Locomotion and Localization Recognition on Physical Activity

Khan,

Al Mudawi,

Abdelhaq

et al. 2024

Sensors

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Advancements in sensing technology have expanded the capabilities of both wearable devices and smartphones, which are now commonly equipped with inertial sensors such as accelerometers and gyroscopes. Initially, these sensors were used for device feature advancement, but now, they can be used for a variety of applications. Human activity recognition (HAR) is an interesting research area that can be used for many applications like health monitoring, sports, fitness, medical purposes, etc. In this research, we designed an advanced system that recognizes different human locomotion and localization activities. The data were collected from raw sensors that contain noise. In the first step, we detail our noise removal process, which employs a Chebyshev type 1 filter to clean the raw sensor data, and then the signal is segmented by utilizing Hamming windows. After that, features were extracted for different sensors. To select the best feature for the system, the recursive feature elimination method was used. We then used SMOTE data augmentation techniques to solve the imbalanced nature of the Extrasensory dataset. Finally, the augmented and balanced data were sent to a long short-term memory (LSTM) deep learning classifier for classification. The datasets used in this research were Real-World Har, Real-Life Har, and Extrasensory. The presented system achieved 89% for Real-Life Har, 85% for Real-World Har, and 95% for the Extrasensory dataset. The proposed system outperforms the available state-of-the-art methods.

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Section: Skewness and Kurtosismentioning

confidence: 99%