Wearable monitoring system for assistance dogs

Despite the recent progresses in robotics, autonomous robots still have too many limitations to reliably help people with disabilities. On the other hand, animals, and especially dogs, have already demonstrated great skills in assisting people in many daily situations. However, dogs also have their own set of limitations. For example, they need to rest periodically, to be healthy (physically and psychologically), and it is difficult to control them remotely. This project aims to “augment” the Assistance dog, by developing a system that compensates some of the dog weaknesses through a robotic device mounted on the dog harness. This specific study, involved in the COCHISE project, focuses on the development of a system for the monitoring of dogs activity and physiological parameters

Understanding Design Preferences and Expectations on Wearable Monitoring Systems for Diabetes

The purpose of this research was to understand preferences and expectations on wearable e-nose system designs to develop a wearable monitoring system integrated into clothing to measure breath and skin gases from wearers for real-time health monitoring. The results of this research are expected to be beneficial for apparel designers and engineers when developing these wearable monitoring systems

Complexity index from a personalized wearable monitoring system for assessing remission in mental health

This study discusses a personalized wearable monitoring system, which provides information and communication technologies to patients with mental disorders and physicians managing such diseases. The system, hereinafter called the PSYCHE system, is mainly comprised of a comfortable t-shirt with embedded sensors, such as textile electrodes, to monitor electrocardiogram-heart rate variability (HRV) series, piezoresistive sensors for respiration activity, and triaxial accelerometers for activity recognition. Moreover, on the patient-side, the PSYCHE system uses a smartphone-based interactive platform for electronic mood agenda and clinical scale administration, whereas on the physician-side provides data visualization and support to clinical decision. The smartphone collects the physiological and behavioral data and sends the information out to a centralized server for further processing. In this study, we present experimental results gathered from ten bipolar patients, wearing the PSYCHE system, with severe symptoms who exhibited mood states among depression (DP), hypomania(HM), mixed state (MX), and euthymia (EU), i.e., the good affective balance. In analyzing more than 400 h of cardiovascular dynamics, we found that patients experiencing mood transitions from a pathological mood state (HM, DP, or MX - where depressive and hypomanic symptoms are simultaneously present) to EU can be characterized through a commonly used measure of entropy. In particular, the SampEn estimated on long-term HRV series increases according to the patients' clinical improvement. These results are in agreement with the current literature reporting on the complexity dynamics of physiological systems and provides a promising and viable support to clinical decision in order to improve the diagnosis and management of psychiatric disorders

Data mining for autonomous wearable sensors used for elderly healthcare monitoring.

Proceedings of the First PhD Symposium on Sustainable Ultrascale Computing Systems (NESUS PhD 2016) Timisoara, Romania. February 8-11, 2016.The paper presents some aspects regarding data mining used modeling and prediction of the patients’ health state parameters. The proposed wearable device integrated by using wireless personal networks (WPNs) can sense, process and communicate vital signs through internet for healthcare monitoring. These WPNs are fitted for medical applications and offer continuous ambulatory health monitoring by using non-invasive methods. Generally, the body sensor network (BSN) for medical applications are based on big data fusion and cloud computing technologies (PaaS, SaaS - for data storage and sharing solutions). The big data fusion includes preprocessing (filter the noise), feature extraction (data abstraction), data fusion computation (modeling different information type and fusion), and data compression (reducing the information stored in memory and transmitted by the transceiver). The fusion between wearable wireless body sensor network (WWBSN), IoT and Cloud Computing will allow doctors, emergency stations or caregivers to track and receive data from BSNs about patients in different places. By using biomedical sensors can be studied the human behavior and physiology, the body's response physiologically and emotionally to various physical and mental diseases. The WWBSN can cover monitoring for cardiovascular, diabetic problems or mental disorders (Alzheimer).European Cooperation in Science and Technology. COS

Clinical evaluation of a wearable sensor for mobile monitoring of respiratory rate on hospital wards

Publisher Copyright: © 2021, The Author(s).A wireless and wearable system was recently developed for mobile monitoring of respiratory rate (RR). The present study was designed to compare RR mobile measurements with reference capnographic measurements on a medical-surgical ward. The wearable sensor measures impedance variations of the chest from two thoracic and one abdominal electrode. Simultaneous measurements of RR from the wearable sensor and from the capnographic sensor (1 measure/minute) were compared in 36 ward patients. Patients were monitored for a period of 182 ± 56 min (range 68–331). Artifact-free RR measurements were available 81% of the monitoring time for capnography and 92% for the wearable monitoring system (p 20 (tachypnea) with a sensitivity of 81% and a specificity of 93%. In ward patients, the wearable sensor enabled accurate and precise measurements of RR within a relatively broad range (6–36 b/min) and the detection of tachypnea with high sensitivity and specificity.Peer reviewe

A Wearable Brain-Computer Interface Instrument for Augmented Reality-Based Inspection in Industry 4.0

This paper proposes a wearable monitoring system for inspection in the framework of Industry 4.0. The instrument integrates augmented reality (AR) glasses with a noninvasive single-channel brain-computer interface (BCI), which replaces the classical input interface of AR platforms. Steady-state visually evoked potentials (SSVEP) are measured by a single-channel electroencephalography (EEG) and simple power spectral density analysis. The visual stimuli for SSVEP elicitation are provided by AR glasses while displaying the inspection information. The real-time metrological performance of the BCI is assessed by the receiver operating characteristic curve on the experimental data from 20 subjects. The characterization was carried out by considering stimulation times from 10.0 down to 2.0 s. The thresholds for the classification were found to be dependent on the subject and the obtained average accuracy goes from 98.9% at 10.0 s to 81.1% at 2.0 s. An inspection case study of the integrated AR-BCI device shows encouraging accuracy of about 80% of lab values

Wearable System for Daily Activity Recognition Using Inertial and Pressure Sensors of a Smart Band and Smart Shoes

Human Activity Recognition (HAR) is a challenging task in the field of human-related signal processing. Owing to the development of wearable sensing technology, an emerging research approach in HAR is to identify user-performed tasks by using data collected from wearable sensors. In this paper, we propose a novel system for monitoring and recognizing daily living activities using an off-the-shelf smart band and two smart shoes. The system aims at providing a useful tool for solving problems regarding body part placement, fusion of multimodal sensors and feature selection for a specific set of activities. The system collects inertial and plantar pressure data at wrist and foot to analyze and then, extract, select important features for recognition. We construct and compare two predictive models of classifying activities from the reduced feature set. A comparison of the classification for each wearable device and a fusion scheme is provided to identify the best body part for activity recognition: either the wrist or the feet. This comparison also demonstrated the effective HAR performance of the proposed system