8,468 research outputs found
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
Wearable Computing for Health and Fitness: Exploring the Relationship between Data and Human Behaviour
Health and fitness wearable technology has recently advanced, making it
easier for an individual to monitor their behaviours. Previously self generated
data interacts with the user to motivate positive behaviour change, but issues
arise when relating this to long term mention of wearable devices. Previous
studies within this area are discussed. We also consider a new approach where
data is used to support instead of motivate, through monitoring and logging to
encourage reflection. Based on issues highlighted, we then make recommendations
on the direction in which future work could be most beneficial
Fall Prediction and Prevention Systems: Recent Trends, Challenges, and Future Research Directions.
Fall prediction is a multifaceted problem that involves complex interactions between physiological, behavioral, and environmental factors. Existing fall detection and prediction systems mainly focus on physiological factors such as gait, vision, and cognition, and do not address the multifactorial nature of falls. In addition, these systems lack efficient user interfaces and feedback for preventing future falls. Recent advances in internet of things (IoT) and mobile technologies offer ample opportunities for integrating contextual information about patient behavior and environment along with physiological health data for predicting falls. This article reviews the state-of-the-art in fall detection and prediction systems. It also describes the challenges, limitations, and future directions in the design and implementation of effective fall prediction and prevention systems
360 Quantified Self
Wearable devices with a wide range of sensors have contributed to the rise of
the Quantified Self movement, where individuals log everything ranging from the
number of steps they have taken, to their heart rate, to their sleeping
patterns. Sensors do not, however, typically sense the social and ambient
environment of the users, such as general life style attributes or information
about their social network. This means that the users themselves, and the
medical practitioners, privy to the wearable sensor data, only have a narrow
view of the individual, limited mainly to certain aspects of their physical
condition.
In this paper we describe a number of use cases for how social media can be
used to complement the check-up data and those from sensors to gain a more
holistic view on individuals' health, a perspective we call the 360 Quantified
Self. Health-related information can be obtained from sources as diverse as
food photo sharing, location check-ins, or profile pictures. Additionally,
information from a person's ego network can shed light on the social dimension
of wellbeing which is widely acknowledged to be of utmost importance, even
though they are currently rarely used for medical diagnosis. We articulate a
long-term vision describing the desirable list of technical advances and
variety of data to achieve an integrated system encompassing Electronic Health
Records (EHR), data from wearable devices, alongside information derived from
social media data.Comment: QCRI Technical Repor
The Emerging Internet of Things Marketplace From an Industrial Perspective: A Survey
The Internet of Things (IoT) is a dynamic global information network
consisting of internet-connected objects, such as Radio-frequency
identification (RFIDs), sensors, actuators, as well as other instruments and
smart appliances that are becoming an integral component of the future
internet. Over the last decade, we have seen a large number of the IoT
solutions developed by start-ups, small and medium enterprises, large
corporations, academic research institutes (such as universities), and private
and public research organisations making their way into the market. In this
paper, we survey over one hundred IoT smart solutions in the marketplace and
examine them closely in order to identify the technologies used,
functionalities, and applications. More importantly, we identify the trends,
opportunities and open challenges in the industry-based the IoT solutions.
Based on the application domain, we classify and discuss these solutions under
five different categories: smart wearable, smart home, smart, city, smart
environment, and smart enterprise. This survey is intended to serve as a
guideline and conceptual framework for future research in the IoT and to
motivate and inspire further developments. It also provides a systematic
exploration of existing research and suggests a number of potentially
significant research directions.Comment: IEEE Transactions on Emerging Topics in Computing 201
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NoiseSPY: a real-time mobile phone platform for urban noise monitoring and mapping
In this paper we present the design, implementation, evaluation, and user experiences of the NoiseSpy application, our sound sensing system that turns the mobile phone into a low-cost data logger for monitoring environmental noise. It allows users to explore a city area while collaboratively visualizing noise levels in real-time. The software combines the sound levels with GPS data in order to generate a map of sound levels that were encountered during a journey. We report early findings from the trials which have been carried out by cycling couriers who were given Nokia mobile phones equipped with the NoiseSpy software to collect noise data around Cambridge city. Indications are that, not only is the functionality of this personal environmental sensing tool engaging for users, but aspects such as personalization of data, contextual information, and reflection upon both the data and its collection, are important factors in obtaining and retaining their interest
Adaptive sampling in context-aware systems: a machine learning approach
As computing systems become ever more pervasive, there is an increasing need for them to understand and adapt to the state of the environment around them: that is, their context. This understanding comes with considerable reliance on a range of sensors. However, portable devices are also very constrained in terms of power, and hence the amount of sensing must be minimised. In this paper, we present a machine learning architecture for context awareness which is designed to balance the sampling rates (and hence energy consumption) of individual sensors with the significance of the input from that sensor. This significance is based on predictions of the likely next context. The architecture is implemented using a selected range of user contexts from a collected data set. Simulation results show reliable context identification results. The proposed architecture is shown to significantly reduce the energy requirements of the sensors with minimal loss of accuracy in context identification
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