Search By Subject

The SHL recognition challenge 2020 was an open competition in activity recognition where the participants were tasked with recognizing eight different modes of locomotion and transportation with smartphone sensors. The main challenges were that the ...

We introduce a footstep-induced floor vibration sensing system that enables us to quantify the gait pattern of individuals with Muscular Dystrophy (MD) in non-clinical settings. MD is a neuromuscular disorder causing progressive loss of muscle, which ...

In the wake of recent COVID-19 pandemic, contact tracing has turned out to be an indispensable technique to help administrative authorities contain localized infections efficiently. In the absence of a definitive and an official vaccine for the ...

Sensing occupant presence and their trajectories of movement in buildings enable new types of analysis and building operation strategies. However, obtaining such information in a cost-efficient and non-intrusive manner is a challenge. This paper ...

Extracting valuable information from sensor data concerning group sizes and human-centered interactions will play a crucial role in balancing building functions according to user needs and preferences. Based on a newly constructed university building in ...

Thermal comfort has a significant influence on a building occupant's overall well-being, productivity, and satisfaction. Due to its subjectivity, thermal comfort cannot be achieved with common building control strategies, such as defining temperature ...

Telemedicine using information technology (IT) and communication networks is becoming common. Often, the medical doctor and the patient can discuss the problem by video teleconference and, if necessary, the patient's physiological data can be sent to ...

The Cognitive Load Monitoring Challenge organized in the UbiTtention 2020 workshop tasked the research community with the problem of inferring a user's cognitive load from physiological measurements recorded by a low-cost wearable. This is challenging ...

Push-notifications are a design tool used by mobile and web apps to alert subscribers to new information. In recent years, due to widespread adoption of the technology and the shrinking level of user attention available, marketing techniques have been ...

In this work, we use machine learning (ML) to detect the cognitive load of a user based on sensor data from a smart wrist-band, sampled during 30 seconds. The data is provided by a challenge at the UbiTtention 2020 workshop of UbiComp 2020; in this ...

In this work we present an indoor emergency context monitoring system based on ground vibration caused by persons in the target area. The system is designed for production plants and large buildings to perceive the safety status of this area. Our ...

We designed and developed DOOM (Adversarial-DRL based Opcode level Obfuscator to generate Metamorphic malware), a novel system that uses adversarial deep reinforcement learning to obfuscate malware at the op-code level for the enhancement of IDS. The ...

Respiratory related events (RE) during nocturnal sleep disturb the natural physiological pattern of sleep. This events may include all types of apnea and hypopnea, respiratory-event-related arousals and snoring. The particular importance of breath ...

Behavioral health conditions such as depression and anxiety are a global concern, and there is growing interest in employing speech technology to screen and monitor patients remotely. Language modeling approaches require automatic speech recognition (...

Label Scarcity and Data Augmentation have long been challenging problems in the research of Human-oriented Artificial Intelligence. Following the trends of Deep Learning, Human Activity Recognition (HAR) tasks have been significantly optimized in the ...

Reliably labeled datasets are crucial to the performance of supervised learning methods. Time-series data pose additional challenges. Data points lying on borders between classes can be mislabeled due to perception limitations of human labelers. Sensor ...

Deep neural networks consisting of a combination of convolutional feature extractor layers and Long Short Term Memory (LSTM) recurrent layers are widely used models for activity recognition from wearable sensors ---referred to as DeepConvLSTM ...

This paper targets the SHL recognition challenge, which focuses on the location-independent and user-independent activity recognition using smartphone sensors. To address this long-range temporal problem with periodic nature, we propose a new approach (...

This paper describes our submission as Team-Petrichor to the competition that was organized by the SHL recognition challenge dataset authors. We compared multiple machine learning approach for classifying eight different activities (Still, Walk, Run, ...

We present a generative adversarial network (GAN) approach to recognising modes of transportation from smartphone motion sensor data, as part of our contribution to the Sussex-Huawei Locomotion-Transportation (SHL) recognition challenge 2020 as team ...