Related papers: Emotion Recognition Using Wearables: A Systematic …
Wearable devices such as smartwatches, fitness trackers, and blood-pressure monitors process, store, and communicate sensitive and personal information related to the health, life-style, habits and interests of the wearer. This data is…
Despite the advent of wearable devices and the proliferation of smartphones, there still is no ideal platform that can continuously sense and precisely collect all available contextual information. Ideally, mobile sensing data collection…
Acoustic sensing has proved effective as a foundation for numerous applications in health and human behavior analysis. In this work, we focus on the problem of detecting in-person social interactions in naturalistic settings from audio…
Affective computing - combining sensor technology, machine learning, and psychology - have been studied for over three decades and is employed in AI-powered technologies to enhance emotional awareness in AI systems, and detect symptoms of…
Wearable devices are a trending topic in both commercial and academic areas. Increasing demand for innovation has led to increased research and new products, addressing new challenges and creating profitable opportunities. However, despite…
In Affective computing, recognizing users' emotions accurately is the basis of affective human-computer interaction. Understanding users' interoception contributes to a better understanding of individually different emotional abilities,…
In order to develop more precise and functional affective applications, it is necessary to achieve a balance between the psychology and the engineering applied to emotions. Signals from the central and peripheral nervous systems have been…
The increasing quality and affordability of consumer electroencephalogram (EEG) headsets make them attractive for situations where medical grade devices are impractical. Predicting and tracking cognitive states is possible for tasks that…
In recent years, WSNs are garnering lot of interest from research community because of their unique characteristics and potential for enormous range of applications. Envision for new class of applications are being emerged such as human…
Classification of human emotions can play an essential role in the design and improvement of human-machine systems. While individual biological signals such as Electrocardiogram (ECG) and Electrodermal Activity (EDA) have been widely used…
Machine learning is frequently used in affective computing, but presents challenges due the opacity of state-of-the-art machine learning methods. Because of the impact affective machine learning systems may have on an individual's life, it…
Wearable devices are increasingly used, thanks to the wide set of applications that can be deployed exploiting their ability to monitor physical activity and health-related parameters. Their usage has been recently proposed to perform…
We present WEARDA, the open source WEARable sensor Data Acquisition software package. WEARDA facilitates the acquisition of human activity data with smartwatches and is primarily aimed at researchers who require transparency, full control,…
Wearable devices are increasingly being used for monitoring health signals and for fitness purposes with typical uses being calorie tracker, workout assistant, and step counter. Even though these wearables can measure many health signals…
Previous menstrual health literature highlights a variety of signals not included in existing menstrual trackers because they are either difficult to gather or are not typically associated with menstrual health. Since it has become…
Consumer applications are becoming increasingly smarter and most of them have to run on device ecosystems. Potential benefits are for example enabling cross-device interaction and seamless user experiences. Essential for today's smart…
This article describes an approach to detect the wearing location of smart devices worn by pedestrians and cyclists. The detection, which is based solely on the sensors of the smart devices, is important context-information which can be…
Energy Autonomous Wearable Sensors (EAWS) have attracted a large interest due to their potential to provide reliable measurements and continuous bioelectric signals, which help to reduce health risk factors early on, ongoing assessment for…
Daily activity recognition has gained prominence due to its applications in context-aware computing. Current methods primarily rely on supervised learning for detecting simple, repetitive activities. This paper introduces LayeredSense, a…
Manufacturing industries strive to improve production efficiency and product quality by deploying advanced sensing and control systems. Wearable sensors are emerging as a promising solution for achieving this goal, as they can provide…