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Context-awareness for mobile sensing: a survey and future directions
The evolution of smartphones together with increasing computational power have empowered developers to create innovative context-aware applications for recognizing user related social and cognitive activities in any situation and at any location. The existence and awareness of the context provides the capability of being conscious of physical environments or situations around mobile device users. This allows network services to respond proactively and intelligently based on such awareness. The key idea behind context-aware applications is to encourage users to collect, analyze and share local sensory knowledge in the purpose for a large scale community use by creating a smart network. The desired network is capable of making autonomous logical decisions to actuate environmental objects, and also assist individuals. However, many open challenges remain, which are mostly arisen due to the middleware services provided in mobile devices have limited resources in terms of power, memory and bandwidth. Thus, it becomes critically important to study how the drawbacks can be elaborated and resolved, and at the same time better understand the opportunities for the research community to contribute to the context-awareness. To this end, this paper surveys the literature over the period of 1991-2014 from the emerging concepts to applications of context-awareness in mobile platforms by providing up-to-date research and future research directions. Moreover, it points out the challenges faced in this regard and enlighten them by proposing possible solutions
Multi-Sensor Context-Awareness in Mobile Devices and Smart Artefacts
The use of context in mobile devices is receiving increasing attention in mobile and ubiquitous computing research. In this article we consider how to augment mobile devices with awareness of their environment and situation as context. Most work to date has been based on integration of generic context sensors, in particular for location and visual context. We propose a different approach based on integration of multiple diverse sensors for awareness of situational context that can not be inferred from location, and targeted at mobile device platforms that typically do not permit processing of visual context. We have investigated multi-sensor context-awareness in a series of projects, and report experience from development of a number of device prototypes. These include development of an awareness module for augmentation of a mobile phone, of the Mediacup exemplifying context-enabled everyday artifacts, and of the Smart-Its platform for aware mobile devices. The prototypes have been explored in various applications to validate the multi-sensor approach to awareness, and to develop new perspectives of how embedded context-awareness can be applied in mobile and ubiquitous computing
Unsupervised Understanding of Location and Illumination Changes in Egocentric Videos
Wearable cameras stand out as one of the most promising devices for the
upcoming years, and as a consequence, the demand of computer algorithms to
automatically understand the videos recorded with them is increasing quickly.
An automatic understanding of these videos is not an easy task, and its mobile
nature implies important challenges to be faced, such as the changing light
conditions and the unrestricted locations recorded. This paper proposes an
unsupervised strategy based on global features and manifold learning to endow
wearable cameras with contextual information regarding the light conditions and
the location captured. Results show that non-linear manifold methods can
capture contextual patterns from global features without compromising large
computational resources. The proposed strategy is used, as an application case,
as a switching mechanism to improve the hand-detection problem in egocentric
videos.Comment: Submitted for publicatio
The Evolution of First Person Vision Methods: A Survey
The emergence of new wearable technologies such as action cameras and
smart-glasses has increased the interest of computer vision scientists in the
First Person perspective. Nowadays, this field is attracting attention and
investments of companies aiming to develop commercial devices with First Person
Vision recording capabilities. Due to this interest, an increasing demand of
methods to process these videos, possibly in real-time, is expected. Current
approaches present a particular combinations of different image features and
quantitative methods to accomplish specific objectives like object detection,
activity recognition, user machine interaction and so on. This paper summarizes
the evolution of the state of the art in First Person Vision video analysis
between 1997 and 2014, highlighting, among others, most commonly used features,
methods, challenges and opportunities within the field.Comment: First Person Vision, Egocentric Vision, Wearable Devices, Smart
Glasses, Computer Vision, Video Analytics, Human-machine Interactio
Leveraging run-time feedback for efficient ASR acceleration
© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.In this work, we propose Locality-AWare-Scheme (LAWS) for an Automatic Speech Recognition (ASR) accelerator in order to significantly reduce its energy consumption and memory requirements, by leveraging the locality among consecutive segments of the speech signal. LAWS diminishes ASR's workload by up to 60% by removing most of the off-chip accesses during the ASR's decoding process. We furthermore improve LAWS's effectiveness by selectively adapting the amount of ASR's workload, based on run-time feedback. In particular, we exploit the fact that the confidence of the ASR system varies along the recognition process. When confidence is high, the ASR system can be more restrictive and reduce the amount of work. The end design provides a saving of 87% in memory requests, 2.3x reduction in energy consumption, and a speedup of 2.1x with respect to the state-of-the-art ASR accelerator.Peer ReviewedPostprint (author's final draft
AmIE: An Ambient Intelligent Environment for Assisted Living
In the modern world of technology Internet-of-things (IoT) systems strives to
provide an extensive interconnected and automated solutions for almost every
life aspect. This paper proposes an IoT context-aware system to present an
Ambient Intelligence (AmI) environment; such as an apartment, house, or a
building; to assist blind, visually-impaired, and elderly people. The proposed
system aims at providing an easy-to-utilize voice-controlled system to locate,
navigate and assist users indoors. The main purpose of the system is to provide
indoor positioning, assisted navigation, outside weather information, room
temperature, people availability, phone calls and emergency evacuation when
needed. The system enhances the user's awareness of the surrounding environment
by feeding them with relevant information through a wearable device to assist
them. In addition, the system is voice-controlled in both English and Arabic
languages and the information are displayed as audio messages in both
languages. The system design, implementation, and evaluation consider the
constraints in common types of premises in Kuwait and in challenges, such as
the training needed by the users. This paper presents cost-effective
implementation options by the adoption of a Raspberry Pi microcomputer,
Bluetooth Low Energy devices and an Android smart watch.Comment: 6 pages, 8 figures, 1 tabl
Pedestrian Detection with Wearable Cameras for the Blind: A Two-way Perspective
Blind people have limited access to information about their surroundings,
which is important for ensuring one's safety, managing social interactions, and
identifying approaching pedestrians. With advances in computer vision, wearable
cameras can provide equitable access to such information. However, the
always-on nature of these assistive technologies poses privacy concerns for
parties that may get recorded. We explore this tension from both perspectives,
those of sighted passersby and blind users, taking into account camera
visibility, in-person versus remote experience, and extracted visual
information. We conduct two studies: an online survey with MTurkers (N=206) and
an in-person experience study between pairs of blind (N=10) and sighted (N=40)
participants, where blind participants wear a working prototype for pedestrian
detection and pass by sighted participants. Our results suggest that both of
the perspectives of users and bystanders and the several factors mentioned
above need to be carefully considered to mitigate potential social tensions.Comment: The 2020 ACM CHI Conference on Human Factors in Computing Systems
(CHI 2020
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