1,446 research outputs found
Smart Computing and Sensing Technologies for Animal Welfare: A Systematic Review
Animals play a profoundly important and intricate role in our lives today.
Dogs have been human companions for thousands of years, but they now work
closely with us to assist the disabled, and in combat and search and rescue
situations. Farm animals are a critical part of the global food supply chain,
and there is increasing consumer interest in organically fed and humanely
raised livestock, and how it impacts our health and environmental footprint.
Wild animals are threatened with extinction by human induced factors, and
shrinking and compromised habitat. This review sets the goal to systematically
survey the existing literature in smart computing and sensing technologies for
domestic, farm and wild animal welfare. We use the notion of \emph{animal
welfare} in broad terms, to review the technologies for assessing whether
animals are healthy, free of pain and suffering, and also positively stimulated
in their environment. Also the notion of \emph{smart computing and sensing} is
used in broad terms, to refer to computing and sensing systems that are not
isolated but interconnected with communication networks, and capable of remote
data collection, processing, exchange and analysis. We review smart
technologies for domestic animals, indoor and outdoor animal farming, as well
as animals in the wild and zoos. The findings of this review are expected to
motivate future research and contribute to data, information and communication
management as well as policy for animal welfare
How will the Internet of Things enable Augmented Personalized Health?
Internet-of-Things (IoT) is profoundly redefining the way we create, consume,
and share information. Health aficionados and citizens are increasingly using
IoT technologies to track their sleep, food intake, activity, vital body
signals, and other physiological observations. This is complemented by IoT
systems that continuously collect health-related data from the environment and
inside the living quarters. Together, these have created an opportunity for a
new generation of healthcare solutions. However, interpreting data to
understand an individual's health is challenging. It is usually necessary to
look at that individual's clinical record and behavioral information, as well
as social and environmental information affecting that individual. Interpreting
how well a patient is doing also requires looking at his adherence to
respective health objectives, application of relevant clinical knowledge and
the desired outcomes.
We resort to the vision of Augmented Personalized Healthcare (APH) to exploit
the extensive variety of relevant data and medical knowledge using Artificial
Intelligence (AI) techniques to extend and enhance human health to presents
various stages of augmented health management strategies: self-monitoring,
self-appraisal, self-management, intervention, and disease progress tracking
and prediction. kHealth technology, a specific incarnation of APH, and its
application to Asthma and other diseases are used to provide illustrations and
discuss alternatives for technology-assisted health management. Several
prominent efforts involving IoT and patient-generated health data (PGHD) with
respect converting multimodal data into actionable information (big data to
smart data) are also identified. Roles of three components in an evidence-based
semantic perception approach- Contextualization, Abstraction, and
Personalization are discussed
Pursuing precision in medicine and nutrition: the rise of electrochemical biosensing at the molecular level
In the era that we seek personalization in material things, it is becoming increasingly clear that the individualized management of medicine and nutrition plays a key role in life expectancy and quality of life, allowing participation to some extent in our welfare and the use of societal resources in a rationale and equitable way. The implementation of precision medicine and nutrition are highly complex challenges which depend on the development of new technologies able to meet important requirements in terms of cost, simplicity, and versatility, and to determine both individually and simultaneously, almost in real time and with the required sensitivity and reliability, molecular markers of different omics levels in biofluids extracted, secreted (either naturally or stimulated), or circulating in the body. Relying on representative and pioneering examples, this review article critically discusses recent advances driving the position of electrochemical bioplatforms as one of the winning horses for the implementation of suitable tools for advanced diagnostics, therapy, and precision nutrition. In addition to a critical overview of the state of the art, including groundbreaking applications and challenges ahead, the article concludes with a personal vision of the imminent roadmap.The financial support of PID2019-103899RBI00 (Spanish Ministerio de Ciencia e Innovación), and PMP22/00084, PI17CIII/00045, PI20CIII/00019 and PI22/00727 (AES-ISCIII) cofounded with FEDER funds Research Projects and the TRANSNANOAVANSENS-CM Program from the Comunidad de Madrid (Grant S2018/NMT-4349) are gratefully acknowledged. Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature.S
Use of nonintrusive sensor-based information and communication technology for real-world evidence for clinical trials in dementia
Cognitive function is an important end point of treatments in dementia clinical trials. Measuring cognitive function by standardized tests, however, is biased toward highly constrained environments (such as hospitals) in selected samples. Patient-powered real-world evidence using information and communication technology devices, including environmental and wearable sensors, may help to overcome these limitations. This position paper describes current and novel information and communication technology devices and algorithms to monitor behavior and function in people with prodromal and manifest stages of dementia continuously, and discusses clinical, technological, ethical, regulatory, and user-centered requirements for collecting real-world evidence in future randomized controlled trials. Challenges of data safety, quality, and privacy and regulatory requirements need to be addressed by future smart sensor technologies. When these requirements are satisfied, these technologies will provide access to truly user relevant outcomes and broader cohorts of participants than currently sampled in clinical trials
Embedding a Grid of Load Cells into a Dining Table for Automatic Monitoring and Detection of Eating Events
This dissertation describes a “smart dining table” that can detect and measure consumption events. This work is motivated by the growing problem of obesity, which is a global problem and an epidemic in the United States and Europe. Chapter 1 gives a background on the economic burden of obesity and its comorbidities. For the assessment of obesity, we briefly describe the classic dietary assessment tools and discuss their drawback and the necessity of using more objective, accurate, low-cost, and in-situ automatic dietary assessment tools. We explain in short various technologies used for automatic dietary assessment such as acoustic-, motion-, or image-based systems. This is followed by a literature review of prior works related to the detection of weights and locations of objects sitting on a table surface. Finally, we state the novelty of this work.
In chapter 2, we describe the construction of a table that uses an embedded grid of load cells to sense the weights and positions of objects. The main challenge is aligning the tops of adjacent load cells to within a few micrometer tolerance, which we accomplish using a novel inversion process during construction. Experimental tests found that object weights distributed across 4 to 16 load cells could be measured with 99.97±0.1% accuracy. Testing the surface for flatness at 58 points showed that we achieved approximately 4.2±0.5 um deviation among adjacent 2x2 grid of tiles. Through empirical measurements we determined that the table has a 40.2 signal-to-noise ratio when detecting the smallest expected intake amount (0.5 g) from a normal meal (approximate total weight is 560 g), indicating that a tiny amount of intake can be detected well above the noise level of the sensors.
In chapter 3, we describe a pilot experiment that tests the capability of the table to monitor eating. Eleven human subjects were video recorded for ground truth while eating a meal on the table using a plate, bowl, and cup. To detect consumption events, we describe an algorithm that analyzes the grid of weight measurements in the format of an image. The algorithm segments the image into multiple objects, tracks them over time, and uses a set of rules to detect and measure individual bites of food and drinks of liquid. On average, each meal consisted of 62 consumption events. Event detection accuracy was very high, with an F1-score per subject of 0.91 to 1.0, and an F1 score per container of 0.97 for the plate and bowl, and 0.99 for the cup. The experiment demonstrates that our device is capable of detecting and measuring individual consumption events during a meal.
Chapter 4 compares the capability of our new tool to monitor eating against previous works that have also monitored table surfaces. We completed a literature search and identified the three state-of-the-art methods to be used for comparison. The main limitation of all previous methods is that they used only one load cell for monitoring, so only the total surface weight can be analyzed. To simulate their operations, the weights of our grid of load cells were summed up to use the 2D data as 1D. Data were prepared according to the requirements of each method. Four metrics were used to evaluate the comparison: precision, recall, accuracy, and F1-score. Our method scored the highest in recall, accuracy, and F1-score; compared to all other methods, our method scored 13-21% higher for recall, 8-28% higher for accuracy, and 10-18% higher for F1-score. For precision, our method scored 97% that is just 1% lower than the highest precision, which was 98%.
In summary, this dissertation describes novel hardware, a pilot experiment, and a comparison against current state-of-the-art tools. We also believe our methods could be used to build a similar surface for other applications besides monitoring consumption
A Critical Overview of Enzyme-Based Electrochemical Biosensors for L-Dopa Detection in Biological Samples
L-Dopa is an intermediate amino acid in the biosynthesis of endogenous catecholamines,
such as dopamine. It is currently considered to be the optimal dopaminergic treatment for Parkinson’s
disease, a neurodegenerative disorder affecting around 1% of the population. In an advanced stage of
the disease, complications such as dyskinesia and psychosis are caused by fluctuations in plasma drug
levels. Real-time monitoring of L-Dopa levels would be advantageous for properly adjusting drug
dosing, thus improving therapeutic efficacy. Electrochemical methods have advantages such as easyto-
use instrumentation, fast response time, and high sensitivity, and are suitable for miniaturization,
enabling the fabrication of implantable or wearable devices. This review reports on research papers of
the past 20 years (2003–2023) dealing with enzyme-based biosensors for the electrochemical detection
of L-Dopa in biological samples. Specifically, amperometric and voltammetric biosensors, whose
output signal is a measurable current, are discussed. The approach adopted includes an initial study
of the steps required to assemble the devices, i.e., electrode modification and enzyme immobilization.
Then, all issues related to their analytical performance in terms of sensitivity, selectivity, and capability
to analyze real samples are critically discussed. The paper aims to provide an assessment of recent
developments while highlighting limitations such as poor selectivity and long-term stability, and the
laborious and time-consuming fabrication protocol that needs to be addressed from the perspective
of the integrated clinical management of Parkinson’s disease
Transforming Personal Healthcare through Technology - A Systematic Literature Review of Wearable Sensors for Medical Application
Wearable Sensor Health Technology (WSHT) captures, analyzes and aggregates physiological data to improve personal well-being. Recently the technology market is flooded with wearable sensors that measure health-related data and have a high user adoption. Nevertheless, these devices are almost exclusively used for fitness purposes and the healthcare sector still faces the challenge of constantly increasing costs. To respond to the necessary but rare use of WSHT in professional healthcare, we aim to identify the most promising areas for future medical implementation. Therefore, we performed a systematic literature search and reviewed 97 papers with regard to disease treatment, application area, vital parameter measurement and target patient. As a result, we could identify five potential areas for further research: (RA1) concentration on widespread diseases, (RA2) expansion of WSHT’s functionality, (RA3) diversity of vital parameter measurements, (RA4) proactive analysis of sensor data for preventive purposes and (RA5) promoting patient adoption through enhanced usability
Design and Validation of a Wearable, Continuous, and Non-Invasive Hydration Monitor that uses Ultrasonic Pulses to Detect Changes in Tissue Hydration Status
Chronic dehydration is an endemic problem for many population groups. Current methods of monitoring hydration status are invasive, time consuming, cannot be performed while exercising, and require lab resources. A proposed solution is a wearable, continuous, and non-invasive device that uses harm-free ultrasonic pulses to detect changes in tissue hydration status over time. Customer and engineering requirements were defined and used to guide the design process. Literature reviews were performed to identify essential information on dehydration, assess current methods, discover state of the art devices, and describe ultrasonic theory. Market research was performed to identify athletes as the target population group. An adjustable elastic nylon bicep band prototype was manufactured and the integration of more advanced components was proposed. The theoretical signal processing method used to detect hydration status was validated through initial tests with a prototype electrical system composed of a Teensy 3.1 board, two 18 kHz piezoceramic disc elements, and an Arduino/LabVIEW interface. Tests with aluminum, rubber, and sponge materials were performed to compare the signal response to propagation through materials with different acoustic properties and water contents. Finally, tests performed with dehydrated bovine muscle tissue revealed a statistically significant difference between hydrated and dehydrated tissue, a promising indication for future device refinement
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