2,132 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
A Novel Framework for Software Defined Wireless Body Area Network
Software Defined Networking (SDN) has gained huge popularity in replacing
traditional network by offering flexible and dynamic network management. It has
drawn significant attention of the researchers from both academia and
industries. Particularly, incorporating SDN in Wireless Body Area Network
(WBAN) applications indicates promising benefits in terms of dealing with
challenges like traffic management, authentication, energy efficiency etc.
while enhancing administrative control. This paper presents a novel framework
for Software Defined WBAN (SDWBAN), which brings the concept of SDN technology
into WBAN applications. By decoupling the control plane from data plane and
having more programmatic control would assist to overcome the current lacking
and challenges of WBAN. Therefore, we provide a conceptual framework for SDWBAN
with packet flow model and a future direction of research pertaining to SDWBAN.Comment: Presented on 8th International Conference on Intelligent Systems,
Modelling and Simulatio
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mHealth Research Applied to Regulated and Unregulated Behavioral Health Sciences
Behavioral scientists are developing new methods and frameworks that leverage mobile health technologies to optimize individual level behavior change. Pervasive sensors and mobile apps allow researchers to passively observe human behaviors “in the wild” 24/7 which supports delivery of personalized interventions in the real-world environment. This is all possible because these technologies contain an incredible array of sensors that allow applications to constantly record user location and can contextualize current environmental conditions through barometers, thermometers, and ambient light sensors and can also capture audio and video of the user and their surroundings through multiple integrated high-definition cameras and microphones. These tools are a game changer in behavioral health research and, not surprisingly, introduce new ethical, regulatory/legal and social implications described in this article
Ultra-Efficient On-Device Object Detection on AI-Integrated Smart Glasses with TinyissimoYOLO
Smart glasses are rapidly gaining advanced functionality thanks to
cutting-edge computing technologies, accelerated hardware architectures, and
tiny AI algorithms. Integrating AI into smart glasses featuring a small form
factor and limited battery capacity is still challenging when targeting
full-day usage for a satisfactory user experience. This paper illustrates the
design and implementation of tiny machine-learning algorithms exploiting novel
low-power processors to enable prolonged continuous operation in smart glasses.
We explore the energy- and latency-efficient of smart glasses in the case of
real-time object detection. To this goal, we designed a smart glasses prototype
as a research platform featuring two microcontrollers, including a novel
milliwatt-power RISC-V parallel processor with a hardware accelerator for
visual AI, and a Bluetooth low-power module for communication. The smart
glasses integrate power cycling mechanisms, including image and audio sensing
interfaces. Furthermore, we developed a family of novel tiny deep-learning
models based on YOLO with sub-million parameters customized for
microcontroller-based inference dubbed TinyissimoYOLO v1.3, v5, and v8, aiming
at benchmarking object detection with smart glasses for energy and latency.
Evaluations on the prototype of the smart glasses demonstrate TinyissimoYOLO's
17ms inference latency and 1.59mJ energy consumption per inference while
ensuring acceptable detection accuracy. Further evaluation reveals an
end-to-end latency from image capturing to the algorithm's prediction of 56ms
or equivalently 18 fps, with a total power consumption of 62.9mW, equivalent to
a 9.3 hours of continuous run time on a 154mAh battery. These results
outperform MCUNet (TinyNAS+TinyEngine), which runs a simpler task (image
classification) at just 7.3 fps per second
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