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Institute
Background
This is a systematic review protocol to identify automated features, applied technologies, and algorithms in the electronic early warning/track and triage system (EW/TTS) developed to predict clinical deterioration (CD).
Methodology
This study will be conducted using PubMed, Scopus, and Web of Science databases to evaluate the features of EW/TTS in terms of their automated features, technologies, and algorithms. To this end, we will include any English articles reporting an EW/TTS without time limitation. Retrieved records will be independently screened by two authors and relevant data will be extracted from studies and abstracted for further analysis. The included articles will be evaluated independently using the JBI critical appraisal checklist by two researchers.
Discussion
This study is an effort to address the available automated features in the electronic version of the EW/TTS to shed light on the applied technologies, automated level of systems, and utilized algorithms in order to smooth the road toward the fully automated EW/TTS as one of the potential solutions of prevention CD and its adverse consequences.
Recognizing Human Activity of Daily Living Using a Flexible Wearable for 3D Spine Pose Tracking
(2023)
The World Health Organization recognizes physical activity as an influencing domain on quality of life. Monitoring, evaluating, and supervising it by wearable devices can contribute to the early detection and progress assessment of diseases such as Alzheimer’s, rehabilitation, and exercises in telehealth, as well as abrupt events such as a fall. In this work, we use a non-invasive and non-intrusive flexible wearable device for 3D spine pose measurement to monitor and classify physical activity. We develop a comprehensive protocol that consists of 10 indoor, 4 outdoor, and 8 transition states activities in three categories of static, dynamic, and transition in order to evaluate the applicability of the flexible wearable device in human activity recognition. We implement and compare the performance of three neural networks: long short-term memory (LSTM), convolutional neural network (CNN), and a hybrid model (CNN-LSTM). For ground truth, we use an accelerometer and strips data. LSTM reached an overall classification accuracy of 98% for all activities. The CNN model with accelerometer data delivered better performance in lying down (100%), static (standing = 82%, sitting = 75%), and dynamic (walking = 100%, running = 100%) positions. Data fusion improved the outputs in standing (92%) and sitting (94%), while LSTM with the strips data yielded a better performance in bending-related activities (bending forward = 49%, bending backward = 88%, bending right = 92%, and bending left = 100%), the combination of data fusion and principle components analysis further strengthened the output (bending forward = 100%, bending backward = 89%, bending right = 100%, and bending left = 100%). Moreover, the LSTM model detected the first transition state that is similar to fall with the accuracy of 84%. The results show that the wearable device can be used in a daily routine for activity monitoring, recognition, and exercise supervision, but still needs further improvement for fall detection.
Sleep is essential to existence, much like air, water, and food, as we spend nearly one-third of our time sleeping. Poor sleep quality or disturbed sleep causes daytime solemnity, which worsens daytime activities' mental and physical qualities and raises the risk of accidents. With advancements in sensor and communication technology, sleep monitoring is moving out of specialized clinics and into our everyday homes. It is possible to extract data from traditional overnight polysomnographic recordings using more basic tools and straightforward techniques. Ballistocardiogram is an unobtrusive, non-invasive, simple, and low-cost technique for measuring cardiorespiratory parameters. In this work, we present a sensor board interface to facilitate the communication between force sensitive resistor sensor and an embedded system to provide a high-performing prototype with an efficient signal-to-noise ratio. We have utilized a multi-physical-layer approach to locate each layer on top of another, yet supporting a low-cost, compact design with easy deployment under the bed frame.
Sleep disorders can impact daily life, affecting physical, emotional, and cognitive well-being. Due to the time-consuming, highly obtrusive, and expensive nature of using the standard approaches such as polysomnography, it is of great interest to develop a noninvasive and unobtrusive in-home sleep monitoring system that can reliably and accurately measure cardiorespiratory parameters while causing minimal discomfort to the user’s sleep. We developed a low-cost Out of Center Sleep Testing (OCST) system with low complexity to measure cardiorespiratory parameters. We tested and validated two force-sensitive resistor strip sensors under the bed mattress covering the thoracic and abdominal regions. Twenty subjects were recruited, including 12 males and 8 females. The ballistocardiogram signal was processed using the 4th smooth level of the discrete wavelet transform and the 2nd order of the Butterworth bandpass filter to measure the heart rate and respiration rate, respectively. We reached a total error (concerning the reference sensors) of 3.24 beats per minute and 2.32 rates for heart rate and respiration rate, respectively. For males and females, heart rate errors were 3.47 and 2.68, and respiration rate errors were 2.32 and 2.33, respectively. We developed and verified the reliability and applicability of the system. It showed a minor dependency on sleeping positions, one of the major cumbersome sleep measurements. We identified the sensor under the thoracic region as the optimal configuration for cardiorespiratory measurement. Although testing the system with healthy subjects and regular patterns of cardiorespiratory parameters showed promising results, further investigation is required with the bandwidth frequency and validation of the system with larger groups of subjects, including patients.
BACKGROUND:
Future of digital public health and smart cities is interwoven and deeply linked. Citizen's and pet's conditions in their urban environment are critical for managing urbanization challenges and digital transformation. Inter- and Intra-connectivity of humans and animals take place in a dynamic space. In this environment, each one can share feelings and news over social media, and report an event happening at any time passively or actively via sensors or multimedia channels, respectively. One Digital Health (ODH) proposes a framework for collecting, managing, analyzing data, and supporting health-oriented policy development and implementation. Accident and Emergency Informatics gives tools to identify and manage overtime hazards and disruptive events, their victims and collaterals.
OBJECTIVE:
We aim to show how ODH framework, through implementing dynamic point of perceptions, supports the analysis of a use case involving a human and an animal in a technological environment-based urban, i.e., smart environment.
METHODS:
We describe an example of One Digital Health intervention wherein Accident and Emergency Informatics mechanisms run in the background. A One Digital Health Intervention is the implementation of a set of digital functionalities designed and deployed to (1) support specific initiatives that address human, animal, and environmental systems' needs and challenges; (2) assess and study these systems' outcomes and effects, and collect related data; (3) select timely metrics for the outcomes of multi-criteria decision analyses. This example intervention is based on the daily journey of two personas: Tracy (a human) and Mego (Tracy's dog). They live in a metropolis, and their activities are monitored and analyzed with IoT sensors, devices, and tools for preventing and managing any health-related abnormality.
RESULTS:
We built an example of an ODH Intervention summary table showing examples of "how to" analyze activities of daily living as part of an ODH Intervention. For each activity, its relations to the ODH dimensions are scored, and the relevant technical fields are evaluated in the light of the FAIR (Findable, Accessible, Interoperable, Reusable) principles prism.
CONCLUSIONS:
The example showcased of ODH intervention provides the basics to build real-world data-based research in a FAIR (Findable, Accessible, Interoperable, Reusable) context to improve continuous health monitoring policies and systems, also for enhancing emergency management. One Digital Health framework provides medical and environmental informaticians, decision-makers, and citizens with tools for improving their daily actions. The additional, integrating Accident and Emergency Informatics layer allows them to better set forth their preparedness and response to potentially acute health-related events. The whole data management cycle must also be processed in a FAIRness way.
