Human Activity Recognition from Environmental Background Sounds for Wireless Sensor Networks (2007)

Sound feature extraction (MFCC) and classification dynamic time warping (DTW) algorithms are applied to recognizing the background sounds in the human daily activities. Applying these algorithms to typical daily activity sounds, average recognition accuracy of 92.5% can be achieved. (id: 4239009)

A software architecture for activity recognition from smartphone sensor data (2017)

Smartphone-based Human Activity Recognition (HAR) systems can exploit the full set of embedded sensors beside the accelerometer in order to increase the accuracy of the detection process. At the same time, the practical deployment of such systems can result highly challenging since it must cope with the limited computational resources and the battery constraints of the mobile devices. (id: 8078368)

Wi-Wri: Fine-Grained Writing Recognition Using Wi-Fi Signals (2016)

As our major contributions, we propose a written detection algorithm for extracting the written activity from Waveform of Channel State Information (CSI) signal, a small scale motion recognition framework for rough letter input and a dictionary-based rectification to improve the input accuracy. The experimental results based on our implementation show that Wi-Wri can achieve more than 98.1% detection accuracy for detecting the motion of writing letters and more than 82.7% recognition accuracy for recognizing written letters. (id: 7847099)

Developers Failing to Use Secure Open Source Components (2018)

Only half of developers using open source components in their software update them to use the most secure version, according to CA Veracode. (Dark Reading)

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