170 research outputs found
Real-time human ambulation, activity, and physiological monitoring:taxonomy of issues, techniques, applications, challenges and limitations
Automated methods of real-time, unobtrusive, human ambulation, activity, and wellness monitoring and data analysis using various algorithmic techniques have been subjects of intense research. The general aim is to devise effective means of addressing the demands of assisted living, rehabilitation, and clinical observation and assessment through sensor-based monitoring. The research studies have resulted in a large amount of literature. This paper presents a holistic articulation of the research studies and offers comprehensive insights along four main axes: distribution of existing studies; monitoring device framework and sensor types; data collection, processing and analysis; and applications, limitations and challenges. The aim is to present a systematic and most complete study of literature in the area in order to identify research gaps and prioritize future research directions
TS-RGBD Dataset: a Novel Dataset for Theatre Scenes Description for People with Visual Impairments
Computer vision was long a tool used for aiding visually impaired people to
move around their environment and avoid obstacles and falls. Solutions are
limited to either indoor or outdoor scenes, which limits the kind of places and
scenes visually disabled people can be in, including entertainment places such
as theatres. Furthermore, most of the proposed computer-vision-based methods
rely on RGB benchmarks to train their models resulting in a limited performance
due to the absence of the depth modality.
In this paper, we propose a novel RGB-D dataset containing theatre scenes
with ground truth human actions and dense captions annotations for image
captioning and human action recognition: TS-RGBD dataset. It includes three
types of data: RGB, depth, and skeleton sequences, captured by Microsoft
Kinect.
We test image captioning models on our dataset as well as some skeleton-based
human action recognition models in order to extend the range of environment
types where a visually disabled person can be, by detecting human actions and
textually describing appearances of regions of interest in theatre scenes
A discussion on the validation tests employed to compare human action recognition methods using the MSR Action3D dataset
This paper aims to determine which is the best human action recognition
method based on features extracted from RGB-D devices, such as the Microsoft
Kinect. A review of all the papers that make reference to MSR Action3D, the
most used dataset that includes depth information acquired from a RGB-D device,
has been performed. We found that the validation method used by each work
differs from the others. So, a direct comparison among works cannot be made.
However, almost all the works present their results comparing them without
taking into account this issue. Therefore, we present different rankings
according to the methodology used for the validation in orden to clarify the
existing confusion.Comment: 16 pages and 7 table
A Survey on Human-aware Robot Navigation
Intelligent systems are increasingly part of our everyday lives and have been
integrated seamlessly to the point where it is difficult to imagine a world
without them. Physical manifestations of those systems on the other hand, in
the form of embodied agents or robots, have so far been used only for specific
applications and are often limited to functional roles (e.g. in the industry,
entertainment and military fields). Given the current growth and innovation in
the research communities concerned with the topics of robot navigation,
human-robot-interaction and human activity recognition, it seems like this
might soon change. Robots are increasingly easy to obtain and use and the
acceptance of them in general is growing. However, the design of a socially
compliant robot that can function as a companion needs to take various areas of
research into account. This paper is concerned with the navigation aspect of a
socially-compliant robot and provides a survey of existing solutions for the
relevant areas of research as well as an outlook on possible future directions.Comment: Robotics and Autonomous Systems, 202
Ambient Assisted Living: Scoping Review of Artificial Intelligence Models, Domains, Technology, and Concerns
Background: Ambient assisted living (AAL) is a common name for various artificial intelligence (AI)—infused applications and platforms that support their users in need in multiple activities, from health to daily living. These systems use different approaches to learn about their users and make automated decisions, known as AI models, for personalizing their services and increasing outcomes. Given the numerous systems developed and deployed for people with different needs, health conditions, and dispositions toward the technology, it is critical to obtain clear and comprehensive insights concerning AI models used, along with their domains, technology, and concerns, to identify promising directions for future work. Objective: This study aimed to provide a scoping review of the literature on AI models in AAL. In particular, we analyzed specific AI models used in AАL systems, the target domains of the models, the technology using the models, and the major concerns from the end-user perspective. Our goal was to consolidate research on this topic and inform end users, health care professionals and providers, researchers, and practitioners in developing, deploying, and evaluating future intelligent AAL systems. Methods: This study was conducted as a scoping review to identify, analyze, and extract the relevant literature. It used a natural language processing toolkit to retrieve the article corpus for an efficient and comprehensive automated literature search. Relevant articles were then extracted from the corpus and analyzed manually. This review included 5 digital libraries: IEEE, PubMed, Springer, Elsevier, and MDPI. Results: We included a total of 108 articles. The annual distribution of relevant articles showed a growing trend for all categories from January 2010 to July 2022. The AI models mainly used unsupervised and semisupervised approaches. The leading models are deep learning, natural language processing, instance-based learning, and clustering. Activity assistance and recognition were the most common target domains of the models. Ambient sensing, mobile technology, and robotic devices mainly implemented the models. Older adults were the primary beneficiaries, followed by patients and frail persons of various ages. Availability was a top beneficiary concern. Conclusions: This study presents the analytical evidence of AI models in AAL and their domains, technologies, beneficiaries, and concerns. Future research on intelligent AAL should involve health care professionals and caregivers as designers and users, comply with health-related regulations, improve transparency and privacy, integrate with health care technological infrastructure, explain their decisions to the users, and establish evaluation metrics and design guidelines. Trial Registration: PROSPERO (International Prospective Register of Systematic Reviews) CRD42022347590; https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42022347590This work was part of and supported by GoodBrother, COST Action 19121—Network on Privacy-Aware Audio- and Video-Based Applications for Active and Assisted Living
Development of a human fall detection system based on depth maps
Assistive care related products are increasingly in demand with the recent
developments in health sector associated technologies. There are several studies
concerned in improving and eliminating barriers in providing quality health care
services to all people, especially elderly who live alone and those who cannot move
from their home for various reasons such as disable, overweight. Among them, human
fall detection systems play an important role in our daily life, because fall is the main
obstacle for elderly people to live independently and it is also a major health concern
due to aging population. The three basic approaches used to develop human fall
detection systems include some sort of wearable devices, ambient based devices or
non-invasive vision based devices using live cameras. Most of such systems are either
based on wearable or ambient sensor which is very often rejected by users due to the
high false alarm and difficulties in carrying them during their daily life activities. Thus,
this study proposes a non-invasive human fall detection system based on the height,
velocity, statistical analysis, fall risk factors and position of the subject using depth
information from Microsoft Kinect sensor. Classification of human fall from other
activities of daily life is accomplished using height and velocity of the subject
extracted from the depth information after considering the fall risk level of the user.
Acceleration and activity detection are also employed if velocity and height fail to
classify the activity. Finally position of the subject is identified for fall confirmation
or statistical analysis is conducted to verify the fall event. From the experimental
results, the proposed system was able to achieve an average accuracy of 98.3% with
sensitivity of 100% and specificity of 97.7%. The proposed system accurately
distinguished all the fall events from other activities of daily life
A protected discharge facility for the elderly: design and validation of a working proof-of-concept
With the increasing share of elderly population worldwide, the need for assistive
technologies to support clinicians in monitoring their health conditions is becoming
more and more relevant. As a quantitative tool, geriatricians recently proposed the
notion of frail elderly, which rapidly became a key element of clinical practices for the
estimation of well-being in aging population. The evaluation of frailty is commonly
based on self-reported outcomes and occasional physicians evaluations, and may
therefore contain biased results.
Another important aspect in the elderly population is hospitalization as a risk factor
for patient\u2019s well being and public costs. Hospitalization is the main cause of functional
decline, especially in older adults. The reduction of hospitalization time may
allow an improvement of elderly health conditions and a reduction of hospital costs.
Furthermore, a gradual transition from a hospital environment to a home-like one,
can contribute to the weaning of the patient from a condition of hospitalization to a
condition of discharge to his home. The advent of new technologies allows for the
design and implementation of smart environments to monitor elderly health status
and activities, fulfilling all the requirements of health and safety of the patients.
From these starting points, in this thesis I present data-driven methodologies to
automatically evaluate one of the main aspects contributing to the frailty estimation,
i.e., the motility of the subject. First I will describe a model of protected discharge
facility, realized in collaboration and within the E.O. Ospedali Galliera (Genoa, Italy),
where patients can be monitored by a system of sensors while physicians and nurses
have the opportunity to monitor them remotely. This sensorised facility is being
developed to assist elderly users after they have been dismissed from the hospital
and before they are ready to go back home, with the perspective of coaching them
towards a healthy lifestyle. The facility is equipped with a variety of sensors (vision,
depth, ambient and wearable sensors and medical devices), but in my thesis I primarily
focus on RGB-D sensors and present visual computing tools to automatically
estimate motility features. I provide an extensive system assessment I carried out onthree different experimental sessions with help of young as well as healthy aging volunteers. The results I present are in agreement with the assessment manually
performed by physicians, showing the potential capability of my approach to complement
current protocols of evaluation
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