IWOAR

This paper proposes a real-time voice activity detection (VAD) system that utilizes a compressed convolutional neural network (CNN) model. On general-purpose computers, the system is capable of accurately classifying the presence of speech in audio with ...

In this paper, we present a Modality-Agnostic Transformer based Self-Supervised Learning (MATS2L) for emotion recognition using physiological signals. The proposed approach consists of two stages: a) Pretext stage, where the transformer model is pre-...

The absolute coverage WiFi networks is higher than it has ever been and WiFi sensing offers a device-free, contactless alternative to intrusive wearable devices for a variety of applications, in particular for ambient assisted living (AAL). However, a ...

This paper presents TongueMendous, an non-intrusive, pervasive tongue-gesture recognition interface for the general population and use cases. It uses an infrared sensor to detect tongue gestures when the tongue sticks in different directions. The ...

Assessing food intake is important for reasons of well-being, lifestyle, health, appearance, or fun. Particularly in the field of medicine, the intake of appropriate foods and quantities of food is considered elementary and always related to physical ...

Sedentary behavior in office environments has become a widespread concern due to its negative impact on individuals’ health and well-being. This study not only addresses this issue by providing details about the musculoskeletal disorders pertinent to ...

Pose-based anomaly detection is a video-analysis technique for detecting anomalous events or behaviors by examining human pose extracted from the video frames. Human anomaly detection plays a crucial role in various applications, such as smart cities and ...

This paper introduces a simple yet efficient method, PedView, for pedestrian collision warning in Advanced Driver Assistance Systems (ADAS). In contrast to existing approaches that rely on LiDAR and stereo cameras for pedestrian-vehicle distance ...

We present a novel lie detection approach based on a force plate. The COP (center of pressure) trajectory length from the interviewee is extracted to test honesty. The preliminary study shows that the COP trajectory length of honest statements is ...

In our society, displays are becoming increasingly prevalent. While it is nearly inconceivable to imagine a daily life without screens, scientific research indicates that our mental well-being is negatively affected by the increasing use and time spent ...

Human Activity Recognition (HAR) has been a popular research field due to the widespread of devices with sensors and computational power (e.g., smartphones and smartwatches). Applications for HAR systems have been extensively researched in recent ...

Executing a complex physical task according to an instruction or a checklist is typical for various fields, such as aviation or healthcare. It is possible that the person is inexperienced or under stress and therefore unable to promptly consult the ...

We present a footwear prototype that can detect activities, distinguish terrains, and estimate the user’s weight. The insole features two air chambers with pressure sensors and a 6-DOF IMU. A machine learning model, a decision tree was trained to ...

We present an approach to identify persons based on their movement on a sensor floor. Three types of deep learning neural networks were trained on five subjects’ sensor data collected during ordinary working days in a test room. A Transformer network ...

Signal quality assessment is essential for biomedical signal processing, analysis, and interpretation. Various methods exist, including averaged numerical values, thresholding, time- or frequency-domain analysis, and nonlinear approaches. This study ...

Understanding acceleration forces and making progress in learning Skateboarding is a process of trial and error. In our paper we are describing our preliminary experiments for describing the complex interactions while pushing for speed in ramps and pump ...

The cerebellum and its connections to the cerebrum can be modulated by noninvasive brain stimulation (NIBS) techniques, especially transcranial alternating current stimulation (tACS). This modulation may affect movements and muscle strength by increasing ...

Time-series classification is gaining significance in pattern recognition as time-series data becomes more abundant along with the increasing digitization of daily life and the rise of the Internet of Things (IoT). One of the biggest challenges lies in ...

Nowadays, in industrial applications, automatic human activity recognition plays a central role. Especially human-centered activity recognition methods using on-body devices (OBDs) address situations where the identity has to be protected. However, ...

Wearable Human Activity Recognition (HAR) is an important field of research in smart assistive technologies. Collecting the data needed to train reliable HAR classifiers is complex and expensive. As a way to mitigate data scarcity, Time Series Data ...

Sensor-based Human Activity Recognition facilitates unobtrusive monitoring of human movements. However, determining the most effective sensor placement for optimal classification performance remains challenging. This paper introduces a novel methodology ...

Parkinson’s disease is one of the most common neurodegenerative chronic diseases which can affect the patient’s quality of life by creating several motor and non-motor impairments. The freezing of gait is one such motor impairment which can cause the ...

The recognition of physiological reactions with the help of machine learning methods already plays a major role in many research areas, but is still little represented in the field of food hypersensitivity recognition. The present work addresses the ...

Seismocardiography is a method commonly used to monitor and prevent cardiovascular diseases. However, noise and artifacts in the signals often interfere with the assessment of cardiac health and the analysis of the signal morphology. Therefore, this ...