Mobile and wearable devices have enabled numerous applications, including activity
tracking, wellness monitoring, and human–computer interaction, that measure and improve …

Human activity recognition (HAR) has multifaceted applications due to its worldly usage of
acquisition devices such as smartphones, video cameras, and its ability to capture human …

Abstract We introduce TensorFlow (TF) Micro, an open-source machine learning inference
framework for running deep-learning models on embedded systems. TF Micro tackles the …

Human activity recognition (HAR) technology that analyzes data acquired from various types
of sensing devices, including vision sensors and embedded sensors, has motivated the …

Human Activity Recognition (HAR) is a field that infers human activities from raw time-series
signals acquired through embedded sensors of smartphones and wearable devices. It has …

In the past years, traditional pattern recognition methods have made great progress.
However, these methods rely heavily on manual feature extraction, which may hinder the …

The vast proliferation of sensor devices and Internet of Things enables the applications of
sensor-based activity recognition. However, there exist substantial challenges that could …

Continuous growth in software, hardware and internet technology has enabled the growth of
internet-based sensor tools that provide physical world observations and data …

Deep convolutional neural networks (CNNs) achieve state-of-the-art performance in
wearable human activity recognition (HAR), which has become a new research trend in …

Sensor-based activity recognition seeks the profound high-level knowledge about human
activities from multitudes of low-level sensor readings. Conventional pattern recognition …