An Activity Classifier based on Heart Rate and Accelerometer Data Fusion

The European project ProeTEX realized a novel set of prototypes based on smart garments that integrate sensors for the real-time monitoring of physiological, activity-related and environmental parameters of the emergency operators during their interventions. The availability of these parameters and the emergency scenario suggest the implementation of novel classification methods aimed at detecting dangerous status of the rescuer automatically, and based not only on the classical activityrelated signals, rather on a combination of these data with the physiological status of the subject. Here we propose a heart rate and accelerometer data fusion algorithm for the activity classification of rescuers in the emergency context

Study design and rationale for biomedical shirt-based electrocardiography monitoring in relevant clinical situations: ECG-shirt study

  Background: Today, the main challenge for researchers is to develop new technologies which may help to improve the diagnoses of cardiovascular disease (CVD), thereby reducing healthcare costs and improving the quality of life for patients. This study aims to show the utility of biomedical shirt-based electrocardiography (ECG) monitoring of patients with CVD in different clinical situations using the Nuubo® ECG (nECG) system. Methods: An investigator-initiated, multicenter, prospective observational study was carried out in a cardiology (adult and pediatric) and cardiac rehabilitation wards. ECG monitoring was used with the biomedical shirt in the following four independent groups of patients: 1) 30 patients after pulmonary vein isolation (PVI), 2) 30 cardiac resynchronization therapy (CRT) recipients, 3) 120 patients during cardiac rehabilitation after myocardial infarction, and 4) 40 pediatric patients with supraventricular tachycardia (SVT) before electrophysiology study. Approval for all study groups was obtained from the institutional review board. The biomedical shirt captures the electrocardiographic signal via textile electrodes integrated into a garment. The software allows the visualization and analysis of data such as ECG, heart rate, arrhythmia detecting algorithm and relative position of the body is captured by an electronic device. Discussion: The major advantages of the nECG system are continuous ECG monitoring during daily activities, high quality of ECG recordings, as well as assurance of a proper adherence due to adequate comfort while wearing the shirt. There are only a few studies that have examined wearable systems, especially in pediatric populations. Trial registration: This study is registered in ClinicalTrials.gov: Identifier NCT03068169. (Cardiol J 2018; 25, 1: 52–59

Using Type-2 Fuzzy Models to Detect Fall Incidents and Abnormal Gaits Among Elderly

— June 2012, 11% of the overall population in Taiwan was over the age of 65. This ratio is higher than the average figure for the United Nations (8%) . Critical issues concerning elderly in healthcare include fall detection, loneliness prevention and retard of obliviousness. In this study we design type-2 fuzzy models that utilize smart phone tri-axial accelerometer signals to detect fall incidents and identify abnormal gaits among elderly. Once a fall incident is detected an alarm is sent to notify the medical staff for taking any necessary treatment. When the proposed system is used as a pedometer, all the tri-axial accelerometer signals are used to identify the gaits during walking. Based on the proposed type-2 fuzzy models, the walking gaits can be identified as normal, left-tilted, and right-tilted. Experimental results from type-2 fuzzy models reveal that the accuracy rates in identifying normal walking and fall over are 92.3% and 100%, respectively, exceeding what are obtained using type-1 fuzzy models

A Wearable Platform for Patient Monitoring during Mass Casualty Incidents

Based on physiological data, intelligent algorithms can assist with the classification and recognition of the most severely impaired victims. This book presents a new sensorbased triage platform with the main proposal to join different sensor and communications technologies into a portable device. This new device must be able to assist the rescue units along with the tactical planning of the operation. This work discusses the implementation and the evaluation of the platform

A Wearable Platform for Patient Monitoring during Mass Casualty Incidents

Based on physiological data, intelligent algorithms can assist with the classification and recognition of the most severely impaired victims. This dissertation presents a new sensorbased triage platform with the main proposal to join different sensor and communications technologies into a portable device. This new device must be able to assist the rescue units along with the tactical planning of the operation. This dissertation discusses the implementation and the evaluation of the platform

A context-aware model for human activity prediction and risk inference in actions

Even though human activities may result in injuries, there is not much discussion in the academy of how ubiquitous computing could assess such risks. So, this paper proposes a model for the Activity Manager layer of the Activity Project, which aims to predict and infer risks in activities. The model uses the Activity Theory for the composition and prediction of activities. It also infers the risk in actions based on changes in the user’s physiological context caused by the actions, and such changes are modeled according to the Hyperspace Analogue to Context model. Tests were conducted and the developed models outperformed proposals found for action prediction, with an accuracy of 78.69%, as well as for risk situation detection, with an accuracy of 98.94%, showing the efficiency of the proposed solution.Keywords: activities of daily living, Activity Theory, activity recognition, activity prediction, risk in actions

Lifelogging Data Validation Model for Internet of Things enabled Personalized Healthcare

The rapid advance of the Internet of Things (IoT) technology offers opportunities to monitor lifelogging data by a variety of IoT assets, like wearable sensors, mobile apps, etc. But due to heterogeneity of connected devices and diverse life patterns in an IoT environment, lifelogging personal data contains much uncertainty and are hardly used for healthcare studies. Effective validation of lifelogging personal data for longitudinal health assessment is demanded. In this paper, it takes lifelogging physical activity as a target to explore the possibility of improving validity of lifelogging data in an IoT based healthcare environment. A rule based adaptive lifelogging physical activity validation model, LPAV-IoT, is proposed for eliminating irregular uncertainties and estimating data reliability in IoT healthcare environments. In LPAV-IoT, a methodology specifying four layers and three modules is presented for analyzing key factors impacting validity of lifelogging physical activity. A series of validation rules are designed with uncertainty threshold parameters and reliability indicators and evaluated through experimental investigations. Following LPAV-IoT, a case study on an IoT enabled personalized healthcare platform MHA [38] connecting three state-of-the-art wearable devices and mobile apps are carried out. The results reflect that the rules provided by LPAV-IoT enable efficiently filtering at least 75% of irregular uncertainty and adaptively indicating the reliability of lifelogging physical activity data on certain condition of an IoT personalized environment

A Wireless Sensor Network for Monitoring Physical Activity, Physiological Response, and Environmental Conditions

Understanding movement is an important area of research for improving comfort and safety. Data collected from accelerometers and gyros attached to multiple parts of a body can be used to determine the type of physical activity in which an individual is engaging. Correlating this data with the individual\u27s heart rate allows for the verification of the type of activity as well as observation of the amount of physical strain experienced during the activity. Additionally, data on environmental stimuli can be gathered in order to determine their effect on physical strain. Current research in the field has been limited by both the portability and flexibility of current sensor systems. This thesis focuses on the development of a flexible wireless sensor network framework for collecting data on an individual\u27s physical activity, the corresponding strain, and relevant environmental factors. Successful implementation of this system has been completed and results are reported