Related papers: Personalized Step Counting Using Wearable Sensors:…
Actigraphic measurements are an important part of research in different disciplines, yet the procedure of determining activity values is unexpectedly not standardized in the literature. Although the measured raw acceleration signal can be…
Traditional approaches to measurement in upper-limb therapy have gaps that electronic sensing and recording can help fill. We highlight shortcomings in current kinematic recording devices, and we introduce a wrist sensing device that…
The sedentary lifestyle increases individuals' risks of developing chronic diseases. To support individuals to be more physically active, we propose a mobile system, MotionShift, that presents users with step count data alongside contextual…
This paper introduces a novel method for real-time exercise classification using a Bidirectional Long Short-Term Memory (BiLSTM) neural network. Existing exercise recognition approaches often rely on synthetic datasets, raw coordinate…
User-specific future activity prediction in the healthcare domain based on previous activities can drastically improve the services provided by the nurses. It is challenging because, unlike other domains, activities in healthcare involve…
Human Activity Recognition (HAR) from devices like smartphone accelerometers is a fundamental problem in ubiquitous computing. Machine learning based recognition models often perform poorly when applied to new users that were not part of…
Physical activity patterns can be informative about a patient's health status. Traditionally, activity data have been gathered using patient self-report. However, these subjective data can suffer from bias and are difficult to collect over…
Continuous monitoring of behavior and physiology via wearable devices offers a novel, objective method for the early detection of worsening depression and anxiety. In this study, we present an explainable anomaly detection framework that…
In the paper a ballistocardiographic sensor for remote monitoring of activity and vital parameters is presented. The sensor is mainly intended for use in monitoring systems supporting care of older people. It allows to detect occupancy of a…
We present a new framework to generate human-like lower-limb trajectories in periodic and non-periodic walking conditions. In our method, walking dynamics is encoded in 3LP, a linear simplified model composed of three pendulums to model…
Low physical activity levels in the intensive care units (ICU) patients have been linked to adverse clinical outcomes. Therefore, there is a need for continuous and objective measurement of physical activity in the ICU to quantify the…
The past decade has seen an increased interest in human activity recognition based on sensor data. Most often, the sensor data come unannotated, creating the need for fast labelling methods. For assessing the quality of the labelling, an…
In applications such as autonomous driving, it is important to understand, infer, and anticipate the intention and future behavior of pedestrians. This ability allows vehicles to avoid collisions and improve ride safety and quality. This…
In this paper, we propose a method that estimates a gait index for a sequence of skeletons. Our system is a stack of an encoder and a decoder that are formed by Long Short-Term Memories (LSTMs). In the encoding stage, the characteristics of…
Wearable technologies are today on the rise, becoming more common and broadly available to mainstream users. In fact, wristband and armband devices such as smartwatches and fitness trackers already took an important place in the consumer…
Based on the same databases, we computationally address two seemingly highly related, in fact drastically distinct, questions via computational data-driven algorithms: 1) how to precisely achieve the big task of differentiating gait…
Modern wearable devices can conveniently record various biosignals in the many different environments of daily living, enabling a rich view of individual health. However, not all biosignals are the same: high-fidelity biosignals, such as…
Remote monitoring of motor functions is a powerful approach for health assessment, especially among the elderly population or among subjects affected by pathologies that negatively impact their walking capabilities. This is further…
Human activity recognition (HAR) holds immense potential for transforming health and fitness monitoring, yet challenges persist in achieving personalized outcomes and sustainability for on-device continuous inferences. This work introduces…
Human activity recognition (HAR) by wearable sensor devices embedded in the Internet of things (IOT) can play a significant role in remote health monitoring and emergency notification, to provide healthcare of higher standards. The purpose…