相关论文: Automatic Stress Detection in Working Environments…
Human activity recognition has grown in popularity with its increase of applications within daily lifestyles and medical environments. The goal of having efficient and reliable human activity recognition brings benefits such as accessible…
Recent advances in remote health monitoring systems have significantly benefited patients and played a crucial role in improving their quality of life. However, while physiological health-focused solutions have demonstrated increasing…
An Android-based smart Television (TV) must reliably run its applications in an embedded program environment under diverse hardware resource conditions. Owing to the diverse hardware components used to build numerous TV models, TV…
Smartphones are equipped with sensors such as accelerometers, gyroscope, and GPS in one cost-effective device with an acceptable level of accuracy. There have been some research studies carried out in terms of using smartphones to measure…
This study delves into the increasingly pertinent issue of technostress in the workplace and its multifaceted impact on job performance. Technostress, emerging from the rapid integration of technology in professional settings, is identified…
Chronic stress can significantly affect physical and mental health. The advent of wearable technology allows for the tracking of physiological signals, potentially leading to innovative stress prediction and intervention methods. However,…
Recent research has focused on the risks associated with poor sitting posture and the impact of sitting on biological parameters, such as heart rate because prolonged sitting is common across all ages and professions. In this work, we…
Loneliness is a critical mental health issue among university students, yet traditional monitoring methods rely primarily on retrospective self-reports and often lack real-time behavioral context. This study explores the use of passive…
This study provides evidence that personality can be reliably predicted from activity data collected through mobile phone sensors. Employing a set of well informed indicators calculable from accelerometer records and movement patterns, we…
This paper describes a data collection campaign and the resulting dataset derived from smartphone sensors characterizing the daily life activities of 3 volunteers in a period of two weeks. The dataset is released as a collection of CSV…
Purpose The purpose of the research is to conduct an exploratory investigation of the material handling activities of an Italian logistics hub. Wearable sensors and other smart tools were used for collecting human and environmental features…
While acute stress has been shown to have both positive and negative effects on performance, not much is known about the impacts of stress on students grades during examinations. To answer this question, we examined whether a correlation…
We develop a data-driven co-segmentation algorithm of passively sensed and self-reported active variables collected through smartphones to identify emotionally stressful states in middle-aged and older patients with mood disorders…
Automatic detection and tracking of emotional states has the potential for helping individuals with various mental health conditions. While previous studies have captured physiological signals using wearable devices in laboratory settings,…
Smartphone-based measurement platforms can collect data on human sensory function in an accessible manner. We developed a smartphone app that measures vibration perception thresholds by commanding vibrations with varying amplitudes and…
In this paper, a new Smartphone sensor based algorithm is proposed to detect accurate distance estimation. The algorithm consists of two phases, the first phase is for detecting the peaks from the Smartphone accelerometer sensor. The other…
Background and Objectives: This paper focuses on using AI to assess the cognitive function of older adults with mild cognitive impairment or mild dementia using physiological data provided by a wearable device. Cognitive screening tools are…
Accurately recognizing human context from smartphone sensor data remains a significant challenge, especially in sedentary settings where activities such as studying, attending lectures, relaxing, and eating exhibit highly similar inertial…
The Experience Sampling Method (ESM) introduces in-situ sampling of human behaviour, and provides researchers and behavioural therapists with ecologically valid and timely assessments of a person's psychological state. This, in turn, opens…
The software is often produced under significant time constraints. Our idea is to understand the effects of various software development practices on the performance of developers working in stressful environments, and identify the best…