408 research outputs found
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Real-Time Sensing of Upper Extremity Movement Diversity Using Kurtosis Implemented on a Smartwatch.
Wearable activity sensors typically count movement quantity, such as the number of steps taken or the number of upper extremity (UE) counts achieved. However, for some applications, such as neurologic rehabilitation, it may be of interest to quantify the quality of the movement experience (QOME), defined, for example, as how diverse or how complex movement epochs are. We previously found that individuals with UE impairment after stroke exhibited differences in their distributions of forearm postures across the day and that these differences could be quantified with kurtosis-an established statistical measure of the peakedness of distributions. In this paper, we describe further progress toward the goal of providing real-time feedback to try to help people learn to modulate their movement diversity. We first asked the following: to what extent do different movement activities induce different values of kurtosis? We recruited seven unimpaired individuals and evaluated a set of 12 therapeutic activities for their forearm postural diversity using kurtosis. We found that the different activities produced a wide range of kurtosis values, with conventional rehabilitation therapy exercises creating the most spread-out distribution and cup stacking the most peaked. Thus, asking people to attempt different activities can vary movement diversity, as measured with kurtosis. Next, since kurtosis is a computationally expensive calculation, we derived a novel recursive algorithm that enables the real-time calculation of kurtosis. We show that the algorithm reduces computation time by a factor of 200 compared to an optimized kurtosis calculation available in SciPy, across window sizes. Finally, we embedded the kurtosis algorithm on a commercial smartwatch and validated its accuracy using a robotic simulator that wore the smartwatch, emulating movement activities with known kurtosis. This work verifies that different movement tasks produce different values of kurtosis and provides a validated algorithm for the real-time calculation of kurtosis on a smartwatch. These are needed steps toward testing QOME-focused, wearable rehabilitation
An IoT Endpoint System-on-Chip for Secure and Energy-Efficient Near-Sensor Analytics
Near-sensor data analytics is a promising direction for IoT endpoints, as it
minimizes energy spent on communication and reduces network load - but it also
poses security concerns, as valuable data is stored or sent over the network at
various stages of the analytics pipeline. Using encryption to protect sensitive
data at the boundary of the on-chip analytics engine is a way to address data
security issues. To cope with the combined workload of analytics and encryption
in a tight power envelope, we propose Fulmine, a System-on-Chip based on a
tightly-coupled multi-core cluster augmented with specialized blocks for
compute-intensive data processing and encryption functions, supporting software
programmability for regular computing tasks. The Fulmine SoC, fabricated in
65nm technology, consumes less than 20mW on average at 0.8V achieving an
efficiency of up to 70pJ/B in encryption, 50pJ/px in convolution, or up to
25MIPS/mW in software. As a strong argument for real-life flexible application
of our platform, we show experimental results for three secure analytics use
cases: secure autonomous aerial surveillance with a state-of-the-art deep CNN
consuming 3.16pJ per equivalent RISC op; local CNN-based face detection with
secured remote recognition in 5.74pJ/op; and seizure detection with encrypted
data collection from EEG within 12.7pJ/op.Comment: 15 pages, 12 figures, accepted for publication to the IEEE
Transactions on Circuits and Systems - I: Regular Paper
Medical data processing and analysis for remote health and activities monitoring
Recent developments in sensor technology, wearable computing, Internet of Things (IoT), and wireless communication have given rise to research in ubiquitous healthcare and remote monitoring of human\u2019s health and activities. Health monitoring systems involve processing and analysis of data retrieved from smartphones, smart watches, smart bracelets, as well as various sensors and wearable devices. Such systems enable continuous monitoring of patients psychological and health conditions by sensing and transmitting measurements such as heart rate, electrocardiogram, body temperature, respiratory rate, chest sounds, or blood pressure. Pervasive healthcare, as a relevant application domain in this context, aims at revolutionizing the delivery of medical services through a medical assistive environment and facilitates the independent living of patients. In this chapter, we discuss (1) data collection, fusion, ownership and privacy issues; (2) models, technologies and solutions for medical data processing and analysis; (3) big medical data analytics for remote health monitoring; (4) research challenges and opportunities in medical data analytics; (5) examples of case studies and practical solutions
Practical and Rich User Digitization
A long-standing vision in computer science has been to evolve computing
devices into proactive assistants that enhance our productivity, health and
wellness, and many other facets of our lives. User digitization is crucial in
achieving this vision as it allows computers to intimately understand their
users, capturing activity, pose, routine, and behavior. Today's consumer
devices - like smartphones and smartwatches provide a glimpse of this
potential, offering coarse digital representations of users with metrics such
as step count, heart rate, and a handful of human activities like running and
biking. Even these very low-dimensional representations are already bringing
value to millions of people's lives, but there is significant potential for
improvement. On the other end, professional, high-fidelity comprehensive user
digitization systems exist. For example, motion capture suits and multi-camera
rigs that digitize our full body and appearance, and scanning machines such as
MRI capture our detailed anatomy. However, these carry significant user
practicality burdens, such as financial, privacy, ergonomic, aesthetic, and
instrumentation considerations, that preclude consumer use. In general, the
higher the fidelity of capture, the lower the user's practicality. Most
conventional approaches strike a balance between user practicality and
digitization fidelity.
My research aims to break this trend, developing sensing systems that
increase user digitization fidelity to create new and powerful computing
experiences while retaining or even improving user practicality and
accessibility, allowing such technologies to have a societal impact. Armed with
such knowledge, our future devices could offer longitudinal health tracking,
more productive work environments, full body avatars in extended reality, and
embodied telepresence experiences, to name just a few domains.Comment: PhD thesi
Integrating passive ubiquitous surfaces into human-computer interaction
Mobile technologies enable people to interact with computers ubiquitously. This dissertation investigates how ordinary, ubiquitous surfaces can be integrated into human-computer interaction to extend the interaction space beyond the edge of the display. It turns out that acoustic and tactile features generated during an interaction can be combined to identify input events, the user, and the surface. In addition, it is shown that a heterogeneous distribution of different surfaces is particularly suitable for realizing versatile interaction modalities. However, privacy concerns must be considered when selecting sensors, and context can be crucial in determining whether and what interaction to perform.Mobile Technologien ermöglichen den Menschen eine allgegenwärtige Interaktion mit Computern. Diese Dissertation untersucht, wie gewöhnliche, allgegenwärtige Oberflächen in die Mensch-Computer-Interaktion integriert werden können, um den Interaktionsraum über den Rand des Displays hinaus zu erweitern. Es stellt sich heraus, dass akustische und taktile Merkmale, die während einer Interaktion erzeugt werden, kombiniert werden können, um Eingabeereignisse, den Benutzer und die Oberfläche zu identifizieren. Darüber hinaus wird gezeigt, dass eine heterogene Verteilung verschiedener Oberflächen besonders geeignet ist, um vielfältige Interaktionsmodalitäten zu realisieren. Bei der Auswahl der Sensoren müssen jedoch Datenschutzaspekte berücksichtigt werden, und der Kontext kann entscheidend dafür sein, ob und welche Interaktion durchgeführt werden soll
Photoplethysmography based atrial fibrillation detection: an updated review from July 2019
Atrial fibrillation (AF) is a prevalent cardiac arrhythmia associated with
significant health ramifications, including an elevated susceptibility to
ischemic stroke, heart disease, and heightened mortality. Photoplethysmography
(PPG) has emerged as a promising technology for continuous AF monitoring for
its cost-effectiveness and widespread integration into wearable devices. Our
team previously conducted an exhaustive review on PPG-based AF detection before
June 2019. However, since then, more advanced technologies have emerged in this
field. This paper offers a comprehensive review of the latest advancements in
PPG-based AF detection, utilizing digital health and artificial intelligence
(AI) solutions, within the timeframe spanning from July 2019 to December 2022.
Through extensive exploration of scientific databases, we have identified 59
pertinent studies. Our comprehensive review encompasses an in-depth assessment
of the statistical methodologies, traditional machine learning techniques, and
deep learning approaches employed in these studies. In addition, we address the
challenges encountered in the domain of PPG-based AF detection. Furthermore, we
maintain a dedicated website to curate the latest research in this area, with
regular updates on a regular basis
INTELIGENTNY WÓZEK INWALIDZKI Z NAPĘDEM ELEKTRYCZNYM: PROBLEMY I WYZWANIA W PODEJŚCIU PRODUKTOWYM
This paper focuses on intelligent assistant for power wheelchair (PW) usage in home conditions. Especially in the context of PW intelligent assistant as a consumer product. The main problematic aspects and challenges of smart PW in real application are noted. The approach to formation of system requirements and their classification is offered. The research results proposed and implemented in the ongoing Mobilis project for smart PW. Further prospects of research and development are noted. Also, it is stated that the implementation of smart PW technology opens possibilities to effective integration with new control methods (including brain-computer interfaces).Niniejszy artykuł koncentruje się na omówieniu problemów i wyzwań dotyczących nowego produktu, jakim jest Smart Power Wheelchair (SPW), czyli inteligentny asystent używany w elektrycznych wózkach inwalidzkich w warunkach domowych. Zwrócono szczególnie uwagę na ukazanie SPW jako nowego produktu konsumenckiego na rynku dóbr. Przedstawione zostały główne problematyczne aspekty i wyzwania dla SPW, które mogą pojawić się w warunkach rzeczywistych. Artykuł zawiera również propozycje dotyczące tworzenia wymagań systemowych oraz ich klasyfikacji. W kolejnej części artykułu przedstawiono wyniki badań, zrealizowanych w ramach projektu Mobilis, dzięki którym wdrożono szereg zmian w produkcie. Ponadto autorzy zapewniają o planowanych dalszych badaniach nad rozwojem produktu. Należy zwrócić uwagę, że wprowadzenie technologii SPW otwiera możliwości efektywnej integracji z nowymi metodami komunikacji (w tym z interfejsami mózg-komputer, z ang. brain-computer interfaces – BCI), z których szczególną korzyść będą miały osoby z niepełnosprawnością ruchową
Wearable devices for health remote monitor system
It is feasible to see how communication and information technology have advanced at a rapid pace
in today’s world. The introduction and emergence of wearable technology is one aspect that contributes
to this advancement and has the potential to be an innovative solution to healthcare challenges,
since it may be used for illness prevention and maintenance, such as physical monitoring,
as well as patient management.
To address some of the healthcare challenges, this research thesis provides a research methodology,
research questions, and hypotheses for constructing an health remote monitoring system with
alerts and continuous monitoring employing wearable devices capable of collecting biometric data
on human health.
The concept was then proven by the development of a prototype using wearable devices connected
to a microcontroller, which transmits its data via MQTT Protocol to a Node-RED powered
dashboard that handles health metrics monitoring and where all monitoring performed, and alarms
generated can be viewed in real-time. All this data is delivered to a MongoDB database for further
analysis and visualization.
To demonstrate the effectiveness and capabilities of this prototype, it was used in the real world
and the results were acquired from two distinct users. The results were very favorable and conclusive,
demonstrating that the created prototype was satisfactory in providing data to support the
developed hypotheses and research questions.É possível observar como as tecnologias de comunicação e informação avançaram a um ritmo bastante
acelerado nos dias de hoje. A introdução e aparecimento da tecnologia ”wearable” representa
um aspeto que contribui para este progresso e tem o potencial de ser uma solução inovadora para
os desafios dos cuidados de saúde, uma vez que pode ser utilizada para a prevenção e manutenção
de doenças, tais como a monitorização física, bem como para a gestão de pacientes.
Para abordar alguns dos desafios dos cuidados de saúde, esta tese de investigação propõe uma
metodologia de investigação, questões de investigação, e hipóteses para o desenvolvimento de um
sistema inteligente de monitorização da saúde com alertas e monitorização contínua utilizando wearable
devices capazes de recolher dados biométricos de seres humanos.
O conceito foi então provado pelo desenvolvimento de um protótipo utilizando wearable devices
conectados a um microcontrolador, que transmite os seus dados através do Protocolo MQTT a um
painel de instrumentos alimentado por o Node-RED que lida com a monitorização de métricas de
saúde e onde toda a monitorização executada, e os alarmes gerados, podem ser visualizados em
tempo real e depois entregues numa base de dados MongoDB para posterior análise e visualização.
Para demonstrar a eficácia deste protótipo, este foi implementado no mundo real onde foram
adquiridos vários resultados através da utilização de dois utilizadores distintos. Os resultados foram
bastante favoráveis e conclusivos, demonstrando que o protótipo criado foi satisfatório no fornecimento
de dados para apoiar as hipóteses e questões de investigação desenvolvidas
Human Activity Recognition (HAR) Using Wearable Sensors and Machine Learning
Humans engage in a wide range of simple and complex activities. Human Activity Recognition (HAR) is typically a classification problem in computer vision and pattern recognition, to recognize various human activities. Recent technological advancements, the miniaturization of electronic devices, and the deployment of cheaper and faster data networks have propelled environments augmented with contextual and real-time information, such as smart homes and smart cities. These context-aware environments, alongside smart wearable sensors, have opened the door to numerous opportunities for adding value and personalized services to citizens. Vision-based and sensory-based HAR find diverse applications in healthcare, surveillance, sports, event analysis, Human-Computer Interaction (HCI), rehabilitation engineering, occupational science, among others, resulting in significantly improved human safety and quality of life.
Despite being an active research area for decades, HAR still faces challenges in terms of gesture complexity, computational cost on small devices, and energy consumption, as well as data annotation limitations. In this research, we investigate methods to sufficiently characterize and recognize complex human activities, with the aim to improving recognition accuracy, reducing computational cost and energy consumption, and creating a research-grade sensor data repository to advance research and collaboration. This research examines the feasibility of detecting natural human gestures in common daily activities. Specifically, we utilize smartwatch accelerometer sensor data and structured local context attributes and apply AI algorithms to determine the complex gesture activities of medication-taking, smoking, and eating.
This dissertation is centered around modeling human activity and the application of machine learning techniques to implement automated detection of specific activities using accelerometer data from smartwatches. Our work stands out as the first in modeling human activity based on wearable sensors with a linguistic representation of grammar and syntax to derive clear semantics of complex activities whose alphabet comprises atomic activities. We apply machine learning to learn and predict complex human activities. We demonstrate the use of one of our unified models to recognize two activities using smartwatch: medication-taking and smoking.
Another major part of this dissertation addresses the problem of HAR activity misalignment through edge-based computing at data origination points, leading to improved rapid data annotation, albeit with assumptions of subject fidelity in demarcating gesture start and end sections. Lastly, the dissertation describes a theoretical framework for the implementation of a library of shareable human activities. The results of this work can be applied in the implementation of a rich portal of usable human activity models, easily installable in handheld mobile devices such as phones or smart wearables to assist human agents in discerning daily living activities. This is akin to a social media of human gestures or capability models. The goal of such a framework is to domesticate the power of HAR into the hands of everyday users, as well as democratize the service to the public by enabling persons of special skills to share their skills or abilities through downloadable usable trained models
Home-based rehabilitation of the shoulder using auxiliary systems and artificial intelligence: an overview
Advancements in modern medicine have bolstered the usage of home-based rehabilitation services for patients, particularly those recovering from diseases or conditions that necessitate a structured rehabilitation process. Understanding the technological factors that can influence the efficacy of home-based rehabilitation is crucial for optimizing patient outcomes. As technologies continue to evolve rapidly, it is imperative to document the current state of the art and elucidate the key features of the hardware and software employed in these rehabilitation systems. This narrative review aims to provide a summary of the modern technological trends and advancements in home-based shoulder rehabilitation scenarios. It specifically focuses on wearable devices, robots, exoskeletons, machine learning, virtual and augmented reality, and serious games. Through an in-depth analysis of existing literature and research, this review presents the state of the art in home-based rehabilitation systems, highlighting their strengths and limitations. Furthermore, this review proposes hypotheses and potential directions for future upgrades and enhancements in these technologies. By exploring the integration of these technologies into home-based rehabilitation, this review aims to shed light on the current landscape and offer insights into the future possibilities for improving patient outcomes and optimizing the effectiveness of home-based rehabilitation programs.info:eu-repo/semantics/publishedVersio
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