6,049 research outputs found
Breathing feedback system with wearable textile sensors
Breathing exercises form an essential part of the treatment for respiratory illnesses such as cystic fibrosis. Ideally these exercises should be performed on a daily basis. This paper presents an interactive system using a wearable textile sensor to monitor breathing patterns. A graphical user interface provides visual real-time feedback to patients. The aim of the system is to encourage the correct performance of prescribed breathing exercises by monitoring the rate and the depth of breathing. The system is
straightforward to use, low-cost and can be installed easily within a clinical setting or in the home. Monitoring the user with a wearable sensor gives real-time feedback to the user as they perform the exercise, allowing them
to perform the exercises independently. There is also potential for remote monitoring where the user’s overall performance over time can be assessed by a clinician
Alien Registration- Coyle, Susan E. (Calais, Washington County)
https://digitalmaine.com/alien_docs/1215/thumbnail.jp
Using colocation to support human memory
The progress of health care in the western world has been
marked by an increase in life expectancy. Advances in life
expectancy have meant that more people are living with
acute health problems, many of which are related to impairment
of memory. This paper describes a pair of scenarios
that use RFID to assist people who may suffer frommemory
defects to extend their capability for independent living. We
present our implementation of an RFID glove, describe its
operation, and show how it enables the application scenarios
Salem for All Ages: An age-friendly action plan
The City of Salem is dedicated to being an ideal place for people of all ages and abilities to live, work, learn and play. Towards this goal the City applied, and was accepted, to the World Health Organization’s Network of Age-Friendly Communities in 2015. Almost entirely directed by passionate resident leaders from Salem and with the support and enthusiasm of Mayor Kimberly Driscoll and participating City Departments, a series of activities were undertaken to assess the needs of Salem’s older adult population. In June 2016, the City of Salem invited collaboration from the Center for Social & Demographic Research on Aging in the Gerontology Institute at the University of Massachusetts Boston to guide the development of an Age-Friendly action plan. The contents of these collaborative planning efforts are described in detail in this report.
The contents of this report are formed by results of a community needs assessment effort taken up by the City of Salem, the results of which can be found in a separate document (see Additional Files below). Elements of this needs assessment include a demographic profile of Salem, a series of three focus groups, and a systematic review of existing documents in Salem, all of which were conducted by researchers at the Center for Social & Demographic Research on Aging within the Gerontology Institute at the University of Massachusetts Boston. In addition, the results of six public listening sessions and a web-based community survey are included in the needs assessment document. These two efforts were facilitated by leaders in the community and, together, included input from over 500 residents of Salem
Identification of sleep apnea events using discrete wavelet transform of respiration, ECG and accelerometer signals
Sleep apnea is a common sleep disorder in which patient sleep patterns are disrupted due to recurrent pauses
in breathing or by instances of abnormally low breathing.
Current gold standard tests for the detection of apnea events are costly and have the addition of long waiting times. This paper investigates the use of cheap and easy to use sensors for the identification of sleep apnea events. Combinations of respiration, electrocardiography (ECG) and acceleration signals were analysed. Results show that using features, formed using the discrete wavelet transform (DWT), from the ECG and acceleration signals provided the highest classification accuracy, with an F1 score of 0.914. However, the novel employment of just the accelerometer signal during classification provided a comparable F1 score of 0.879. By employing one or a combination of the analysed sensors a preliminary test for sleep apnea, prior to the requirement for gold standard testing, can be performed
Cross-Modal Health State Estimation
Individuals create and consume more diverse data about themselves today than
any time in history. Sources of this data include wearable devices, images,
social media, geospatial information and more. A tremendous opportunity rests
within cross-modal data analysis that leverages existing domain knowledge
methods to understand and guide human health. Especially in chronic diseases,
current medical practice uses a combination of sparse hospital based biological
metrics (blood tests, expensive imaging, etc.) to understand the evolving
health status of an individual. Future health systems must integrate data
created at the individual level to better understand health status perpetually,
especially in a cybernetic framework. In this work we fuse multiple user
created and open source data streams along with established biomedical domain
knowledge to give two types of quantitative state estimates of cardiovascular
health. First, we use wearable devices to calculate cardiorespiratory fitness
(CRF), a known quantitative leading predictor of heart disease which is not
routinely collected in clinical settings. Second, we estimate inherent genetic
traits, living environmental risks, circadian rhythm, and biological metrics
from a diverse dataset. Our experimental results on 24 subjects demonstrate how
multi-modal data can provide personalized health insight. Understanding the
dynamic nature of health status will pave the way for better health based
recommendation engines, better clinical decision making and positive lifestyle
changes.Comment: Accepted to ACM Multimedia 2018 Conference - Brave New Ideas, Seoul,
Korea, ACM ISBN 978-1-4503-5665-7/18/1
Web-based sensor streaming wearable for respiratory monitoring applications.
This paper presents a system for remote monitoring of respiration of individuals that can detect respiration rate, mode of breathing and identify coughing events. It comprises a series of polymer fabric-sensors incorporated into a sports vest, a wearable data acquisition platform and a novel rich internet application (RIA) which together enable remote real-time monitoring of untethered wearable systems for respiratory rehabilitation. This system will, for the first time, allow therapists to monitor and guide the respiratory efforts of patients in real-time through a web browser. Changes in abdomen expansion and contraction associated with respiration are detected by the fabric sensors and transmitted wirelessly via a Bluetooth-based solution to a standard computer. The respiratory signals are visualized locally through the RIA and subsequently published to a sensor streaming cloud-based server. A web-based signal streaming protocol makes the signals available as real-time streams to authorized subscribers over standard browsers. We demonstrate real-time streaming of a six-sensor shirt rendered remotely at 40 samples/s per sensor with perceptually acceptable latency (<0.5s) over realistic
network conditions
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