A Simplistic and Cost-Effective Design for Real-World Development of an
Ambient Assisted Living System for Fall Detection and Indoor Localization:
Proof of Concept
- URL: http://arxiv.org/abs/2207.11623v1
- Date: Sun, 24 Jul 2022 00:13:32 GMT
- Title: A Simplistic and Cost-Effective Design for Real-World Development of an
Ambient Assisted Living System for Fall Detection and Indoor Localization:
Proof of Concept
- Authors: Nirmalya Thakur and Chia Y. Han
- Abstract summary: This work proposes a cost-effective and simplistic design paradigm for an Ambient Assisted Living system.
It can capture multimodal components of user behaviors during ADLs that are necessary for performing fall detection and indoor localization.
Proof of concept results from real-world experiments are presented to uphold the effective working of the system.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Falls, highly common in the constantly increasing global aging population,
can have a variety of negative effects on their health, well-being, and quality
of life, including restricting their capabilities to conduct Activities of
Daily Living (ADLs), which are crucial for one's sustenance. Timely assistance
during falls is highly necessary, which involves tracking the indoor location
of the elderly during their diverse navigational patterns associated with ADLs
to detect the precise location of a fall. With the decreasing caregiver
population on a global scale, it is important that the future of intelligent
living environments can detect falls during ADLs while being able to track the
indoor location of the elderly in the real world. To address these challenges,
this work proposes a cost-effective and simplistic design paradigm for an
Ambient Assisted Living system that can capture multimodal components of user
behaviors during ADLs that are necessary for performing fall detection and
indoor localization in a simultaneous manner in the real world. Proof of
concept results from real-world experiments are presented to uphold the
effective working of the system. The findings from two comparison studies with
prior works in this field are also presented to uphold the novelty of this
work. The first comparison study shows how the proposed system outperforms
prior works in the areas of indoor localization and fall detection in terms of
the effectiveness of its software design and hardware design. The second
comparison study shows that the cost for the development of this system is the
least as compared to prior works in these fields, which involved real-world
development of the underlining systems, thereby upholding its cost-effective
nature.
Related papers
- Trustworthiness in Retrieval-Augmented Generation Systems: A Survey [59.26328612791924]
Retrieval-Augmented Generation (RAG) has quickly grown into a pivotal paradigm in the development of Large Language Models (LLMs)
We propose a unified framework that assesses the trustworthiness of RAG systems across six key dimensions: factuality, robustness, fairness, transparency, accountability, and privacy.
arXiv Detail & Related papers (2024-09-16T09:06:44Z) - Computer-Aided Fall Recognition Using a Three-Stream Spatial-Temporal GCN Model with Adaptive Feature Aggregation [0.5235143203977018]
Prevention of falls is paramount in modern healthcare, particularly for the elderly.
A computer-aided fall detection system is inevitable to save elderly people's lives worldwide.
This paper proposes a novel three-stream spatial-temporal feature-based fall detection system.
arXiv Detail & Related papers (2024-08-22T08:40:04Z) - Active Learning for Derivative-Based Global Sensitivity Analysis with Gaussian Processes [70.66864668709677]
We consider the problem of active learning for global sensitivity analysis of expensive black-box functions.
Since function evaluations are expensive, we use active learning to prioritize experimental resources where they yield the most value.
We propose novel active learning acquisition functions that directly target key quantities of derivative-based global sensitivity measures.
arXiv Detail & Related papers (2024-07-13T01:41:12Z) - Maintenance Required: Updating and Extending Bootstrapped Human Activity Recognition Systems for Smart Homes [0.11029371407785957]
Off-the-shelf HAR systems are effective in limited capacity for an individual home.
Previous work has successfully targeted the initial phase.
We build on bootstrapped HAR systems and introduce an effective updating and extension procedure.
arXiv Detail & Related papers (2024-06-20T16:08:40Z) - Clairvoyance: A Pipeline Toolkit for Medical Time Series [95.22483029602921]
Time-series learning is the bread and butter of data-driven *clinical decision support*
Clairvoyance proposes a unified, end-to-end, autoML-friendly pipeline that serves as a software toolkit.
Clairvoyance is the first to demonstrate viability of a comprehensive and automatable pipeline for clinical time-series ML.
arXiv Detail & Related papers (2023-10-28T12:08:03Z) - DREAM: Decentralized Reinforcement Learning for Exploration and
Efficient Energy Management in Multi-Robot Systems [14.266876062352424]
Resource-constrained robots often suffer from energy inefficiencies, underutilized computational abilities due to inadequate task allocation, and a lack of robustness in dynamic environments.
This paper introduces DREAM - Decentralized Reinforcement Learning for Exploration and Efficient Energy Management in Multi-Robot Systems.
arXiv Detail & Related papers (2023-09-29T17:43:41Z) - A Domain-Agnostic Approach for Characterization of Lifelong Learning
Systems [128.63953314853327]
"Lifelong Learning" systems are capable of 1) Continuous Learning, 2) Transfer and Adaptation, and 3) Scalability.
We show that this suite of metrics can inform the development of varied and complex Lifelong Learning systems.
arXiv Detail & Related papers (2023-01-18T21:58:54Z) - Multimodal Approaches for Indoor Localization for Ambient Assisted
Living in Smart Homes [0.0]
It presents a Big-Data driven methodology that studies the multimodal components of user interactions.
Second, it introduces a context independent approach that can interpret the accelerometer and gyroscope data.
Third, it presents a methodology to detect the spatial coordinates of a user's indoor position that outperforms all similar works in this field.
arXiv Detail & Related papers (2021-06-29T17:46:21Z) - Learning to Plan Optimistically: Uncertainty-Guided Deep Exploration via
Latent Model Ensembles [73.15950858151594]
This paper presents Latent Optimistic Value Exploration (LOVE), a strategy that enables deep exploration through optimism in the face of uncertain long-term rewards.
We combine latent world models with value function estimation to predict infinite-horizon returns and recover associated uncertainty via ensembling.
We apply LOVE to visual robot control tasks in continuous action spaces and demonstrate on average more than 20% improved sample efficiency in comparison to state-of-the-art and other exploration objectives.
arXiv Detail & Related papers (2020-10-27T22:06:57Z) - Unsupervised Domain Adaptation for Spatio-Temporal Action Localization [69.12982544509427]
S-temporal action localization is an important problem in computer vision.
We propose an end-to-end unsupervised domain adaptation algorithm.
We show that significant performance gain can be achieved when spatial and temporal features are adapted separately or jointly.
arXiv Detail & Related papers (2020-10-19T04:25:10Z) - Intelligent Luminaire based Real-time Indoor Positioning for Assisted
Living [0.0]
This paper presents an experimental evaluation on the accuracy of indoor localisation.
The research was carried out as part of a European Union project targeting the creation of ICT solutions for older adult care.
arXiv Detail & Related papers (2020-09-05T07:19:20Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.