12,388 research outputs found
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
Vision-based interface applied to assistive robots
This paper presents two vision-based interfaces for disabled people to command a mobile robot for personal assistance. The developed interfaces can be subdivided according to the algorithm of image processing implemented for the detection and tracking of two different body regions. The first interface detects and tracks movements of the user's head, and these movements are transformed into linear and angular velocities in order to command a mobile robot. The second interface detects and tracks movements of the user's hand, and these movements are similarly transformed. In addition, this paper also presents the control laws for the robot. The experimental results demonstrate good performance and balance between complexity and feasibility for real-time applications.Fil: PĂ©rez Berenguer, MarĂa Elisa. Universidad Nacional de San Juan. Facultad de IngenierĂa. Departamento de ElectrĂłnica y Automática. Gabinete de TecnologĂa MĂ©dica; Argentina. Consejo Nacional de Investigaciones CientĂficas y TĂ©cnicas; ArgentinaFil: Soria, Carlos Miguel. Consejo Nacional de Investigaciones CientĂficas y TĂ©cnicas. Centro CientĂfico TecnolĂłgico Conicet - San Juan. Instituto de Automática. Universidad Nacional de San Juan. Facultad de IngenierĂa. Instituto de Automática; ArgentinaFil: LĂłpez Celani, Natalia Martina. Universidad Nacional de San Juan. Facultad de IngenierĂa. Departamento de ElectrĂłnica y Automática. Gabinete de TecnologĂa MĂ©dica; Argentina. Consejo Nacional de Investigaciones CientĂficas y TĂ©cnicas; ArgentinaFil: Nasisi, Oscar Herminio. Universidad Nacional de San Juan. Facultad de IngenierĂa. Instituto de Automática; ArgentinaFil: Mut, Vicente Antonio. Consejo Nacional de Investigaciones CientĂficas y TĂ©cnicas. Centro CientĂfico TecnolĂłgico Conicet - San Juan. Instituto de Automática. Universidad Nacional de San Juan. Facultad de IngenierĂa. Instituto de Automática; Argentin
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
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
Human behavioural analysis with self-organizing map for ambient assisted living
This paper presents a system for automatically classifying the resting location of a moving object in an indoor environment. The system uses an unsupervised neural network (Self Organising Feature Map) fully implemented on a low-cost, low-power automated home-based surveillance system, capable of monitoring activity level of elders living alone independently. The proposed system runs on an embedded platform with a specialised ceiling-mounted video sensor for intelligent activity monitoring. The system has the ability to learn resting locations, to measure overall activity levels and to detect specific events such as potential falls. First order motion information, including first order moving average smoothing, is generated from the 2D image coordinates (trajectories). A novel edge-based object detection algorithm capable of running at a reasonable speed on the embedded platform has been developed. The classification is dynamic and achieved in real-time. The dynamic classifier is achieved using a SOFM and a probabilistic model. Experimental results show less than 20% classification error, showing the robustness of our approach over others in literature with minimal power consumption. The head location of the subject is also estimated by a novel approach capable of running on any resource limited platform with power constraints
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