Background Physical activity has not been objectively measured in prospective cohorts with
sufficiently large numbers to reliably detect associations with multiple health outcomes …

With the widespread proliferation of (miniaturized) sensing facilities and the massive growth
and popularity of the field of machine learning (ML) research, new frontiers in automated …

Mobile sensing and computing applications usually require time-series inputs from sensors,
such as accelerometers, gyroscopes, and magnetometers. Some applications, such as …

The widespread presence of motion sensors on users' personal mobile devices has
spawned a growing research interest in human activity recognition (HAR). However, when …

Recent years have seen significant advances in the sensing capabilities of smartphones,
enabling them to collect rich contextual information such as location, device usage, and …

Adopting deep learning methods for human activity recognition has been effective in
extracting discriminative features from raw input sequences acquired from body-worn …

Recently, deep learning (DL) methods have been introduced very successfully into human
activity recognition (HAR) scenarios in ubiquitous and wearable computing. Especially the …

A major bottleneck in training robust Human-Activity Recognition models (HAR) is the need
for large-scale labeled sensor datasets. Because labeling large amounts of sensor data is …

To facilitate data-driven and informed decision making, a novel deep neural network
architecture for human activity recognition based on multiple sensor data is proposed in this …

Despite the widespread installation of accelerometers in almost all mobile phones and
wearable devices, activity recognition using accelerometers is still immature due to the poor …