Existing accelerometer-based human activity recognition (HAR) benchmark datasets that
were recorded during free living suffer from non-fixed sensor placement, the usage of only …

Multivariate time series data are becoming increasingly common in numerous real world
applications, eg, power plant monitoring, health care, wearable devices, automobile, etc. As …

This paper presents a continuous delivery/continuous verifiability (CD/CV) method for IoT
dataflows in edge–fog–cloud. A CD model based on extraction, transformation, and load …

At the SHL recognition challenge 2018, Team Tesaguri developed a human activity
recognition method. First, we obtained the FFT spectrogram from 60-second acceleration …

Traditional machine learning approaches for recognizing modes of transportation rely
heavily on hand-crafted feature extraction methods which require domain knowledge. So …

In this paper, we have used a smartphone sensor-based benchmark Sussex-Huawei
Locomotion-Transportation (SHL) dataset for rich locomotion and transportation analytics …

Motion capture generates data which are often more accurate than those captured by
multiple of accelerometer sensors by their physical specification. Based on the observation …

For high precision estimation with SHL recognition challenge, we use a deep learning
framework based on convolutional layers and LSTM recurrent units (ConvLSTM). We …

Recognition of daily human activities in various locomotion and transportation modes has
numerous applications like coaching users for behavior modification and maintaining a …

The dynamics of body movements are often driven by large and intricate low-level
interactions involving various body parts. These dynamics are part of an underlying data …