Using artificial intelligence and machine learning techniques in healthcare applications has
been actively researched over the last few years. It holds promising opportunities as it is …

Sensors in everyday devices, such as our phones, wearables, and computers, leave a
stream of digital traces. Personal sensing refers to collecting and analyzing data from …

Introduction Smart watches have the potential to support health in everyday living by:
enabling self-monitoring of personal activity; obtaining feedback based on activity measures; …

The Internet of Things (IoT) has grown over decades to encompass many forms of sensing
modalities, and continues to improve in terms of sophistication and lower costs. The trend of …

Feature extraction is crucial for human activity recognition (HAR) using body-worn
movement sensors. Recently, learned representations have been used successfully, offering …

The lack of large-scale, labeled data sets impedes progress in developing robust and
generalized predictive models for on-body sensor-based human activity recognition (HAR) …

Chronic and widespread diseases such as obesity, diabetes, and hypercholesterolemia
require patients to monitor their food intake, and food journaling is currently the most …

Emotional eating has traditionally been defined as (over) eating in response to negative
emotions. Such overeating can impact general health because of excess energy intake and …

Wearable devices monitoring food intake through passive sensing is slowly emerging to
complement self-reporting of users' caloric intake and eating behaviors. Though the ultimate …

The number of mobile devices, such as smartphones and smartwatches, is relentlessly
increasing, to almost 6.8 billion by 2022, and along with it, the amount of personal and …