38,834 research outputs found
"Reading Between the Heat": Co-Teaching Body Thermal Signatures for Non-intrusive Stress Detection
Stress impacts our physical and mental health as well as our social life. A
passive and contactless indoor stress monitoring system can unlock numerous
important applications such as workplace productivity assessment, smart homes,
and personalized mental health monitoring. While the thermal signatures from a
user's body captured by a thermal camera can provide important information
about the "fight-flight" response of the sympathetic and parasympathetic
nervous system, relying solely on thermal imaging for training a stress
prediction model often lead to overfitting and consequently a suboptimal
performance. This paper addresses this challenge by introducing ThermaStrain, a
novel co-teaching framework that achieves high-stress prediction performance by
transferring knowledge from the wearable modality to the contactless thermal
modality. During training, ThermaStrain incorporates a wearable electrodermal
activity (EDA) sensor to generate stress-indicative representations from
thermal videos, emulating stress-indicative representations from a wearable EDA
sensor. During testing, only thermal sensing is used, and stress-indicative
patterns from thermal data and emulated EDA representations are extracted to
improve stress assessment. The study collected a comprehensive dataset with
thermal video and EDA data under various stress conditions and distances.
ThermaStrain achieves an F1 score of 0.8293 in binary stress classification,
outperforming the thermal-only baseline approach by over 9%. Extensive
evaluations highlight ThermaStrain's effectiveness in recognizing
stress-indicative attributes, its adaptability across distances and stress
scenarios, real-time executability on edge platforms, its applicability to
multi-individual sensing, ability to function on limited visibility and
unfamiliar conditions, and the advantages of its co-teaching approach.Comment: 29 page
Bio-sensing textile based patch with integrated optical detection system for sweat monitoring
Sensors, which can be integrated into clothing and used to measure biochemical changes in body fluids,
such as sweat, constitute a major advancement in the area of wearable sensors. Initial applications for
such technology exist in personal health and sports performance monitoring. However, sample collection
is a complicated matter as analysis must be done in real-time in order to obtain a useful examination
of its composition. This work outlines the development of a textile-based fluid handling platform which
uses a passive pump to gather sweat and move it through a pre-defined channel for analysis. The system
is tested both in vitro and in vivo. In addition, a pH sensor, which depends on the use of a pH sensitive dye
and paired emitter-detector LEDs to measure colour changes, has been developed. In vitro and on-body
trials have shown that the sensor has the potential to record real-time variations in sweat during exercise
Who Wears Me? Bioimpedance as a Passive Biometric
Mobile and wearable systems for monitoring health are becoming common. If such an mHealth system knows the identity of its wearer, the system can properly label and store data collected by the system. Existing recognition schemes for such mobile applications and pervasive devices are not particularly usable â they require ıt active engagement with the person (e.g., the input of passwords), or they are too easy to fool (e.g., they depend on the presence of a device that is easily stolen or lost). \par We present a wearable sensor to passively recognize people. Our sensor uses the unique electrical properties of a person\u27s body to recognize their identity. More specifically, the sensor uses ıt bioimpedance â a measure of how the body\u27s tissues oppose a tiny applied alternating current â and learns how a person\u27s body uniquely responds to alternating current of different frequencies. In this paper we demonstrate the feasibility of our system by showing its effectiveness at accurately recognizing people in a household 90% of the time
On the Deployment of Healthcare Applications over Fog Computing Infrastructure
Fog computing is considered as the most promising enhancement of the traditional cloud computing paradigm in order to handle potential issues introduced by the emerging Interned of Things (IoT) framework at the network edge. The heterogeneous nature, the extensive distribution and the hefty number of deployed IoT nodes will disrupt existing functional models, creating confusion. However, IoT will facilitate the rise of new applications, with automated healthcare monitoring platforms being amongst them. This paper presents the pillars of design for such applications, along with the evaluation of a working prototype that collects ECG traces from a tailor-made device and utilizes the patient's smartphone as a Fog gateway for securely sharing them to other authorized entities. This prototype will allow patients to share information to their physicians, monitor their health status independently and notify the authorities rapidly in emergency situations. Historical data will also be available for further analysis, towards identifying patterns that may improve medical diagnoses in the foreseeable future
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