Real-Time Low-Cost Active and Assisted Living for the Elderly

2019 ◽  
pp. 153-161
Author(s):  
António Henrique Almeida ◽  
Ivo Santos ◽  
Joel Rodrigues ◽  
Luis Frazão ◽  
José Ribeiro ◽  
...  
Keyword(s):  
Real Time ◽  
Assisted Living ◽  
Low Cost ◽  
The Elderly

scholarly journals Differentiation of Patients with Balance Insufficiency (Vestibular Hypofunction) versus Normal Subjects Using a Low-Cost Small Wireless Wearable Gait Sensor

Biosensors ◽  
2019 ◽  
Vol 9 (1) ◽  
pp. 29 ◽  
Author(s):  
Tam Nguyen ◽  
Jonathan Young ◽  
Amanda Rodriguez ◽  
Steven Zupancic ◽  
Donald Lie
Keyword(s):  
Real Time ◽  
Low Cost ◽  
Normal Subjects ◽  
The Elderly ◽  
Measurement Unit ◽  
Balance Disorders ◽  
Remote Sensor ◽  
Data Collection And Analysis ◽  
Dynamic Gait Index ◽  
Dynamic Gait

Balance disorders present a significant healthcare burden due to the potential for hospitalization or complications for the patient, especially among the elderly population when considering intangible losses such as quality of life, morbidities, and mortalities. This work is a continuation of our earlier works where we now examine feature extraction methodology on Dynamic Gait Index (DGI) tests and machine learning classifiers to differentiate patients with balance problems versus normal subjects on an expanded cohort of 60 patients. All data was obtained using our custom designed low-cost wireless gait analysis sensor (WGAS) containing a basic inertial measurement unit (IMU) worn by each subject during the DGI tests. The raw gait data is wirelessly transmitted from the WGAS for real-time gait data collection and analysis. Here we demonstrate predictive classifiers that achieve high accuracy, sensitivity, and specificity in distinguishing abnormal from normal gaits. These results show that gait data collected from our very low-cost wearable wireless gait sensor can effectively differentiate patients with balance disorders from normal subjects in real-time using various classifiers. Our ultimate goal is to be able to use a remote sensor such as the WGAS to accurately stratify an individual’s risk for falls.

scholarly journals IndoorCare: Low-Cost Elderly Activity Monitoring System through Image Processing

Sensors ◽  
2021 ◽  
Vol 21 (18) ◽  
pp. 6051
Author(s):  
Daniel Fuentes ◽  
Luís Correia ◽  
Nuno Costa ◽  
Arsénio Reis ◽  
José Ribeiro ◽  
...  
Keyword(s):  
Low Income ◽  
Assisted Living ◽  
Rural Areas ◽  
Low Cost ◽  
Elderly Care ◽  
Ambient Assisted Living ◽  
The Elderly ◽  
Case Scenario ◽  
Care Services ◽  
Socially Excluded

The Portuguese population is aging at an increasing rate, which introduces new problems, particularly in rural areas, where the population is small and widely spread throughout the territory. These people, mostly elderly, have low income and are often isolated and socially excluded. This work researches and proposes an affordable Ambient Assisted Living (AAL)-based solution to monitor the activities of elderly individuals, inside their homes, in a pervasive and non-intrusive way, while preserving their privacy. The solution uses a set of low-cost IoT sensor devices, computer vision algorithms and reasoning rules, to acquire data and recognize the activities performed by a subject inside a home. A conceptual architecture and a functional prototype were developed, the prototype being successfully tested in an environment similar to a real case scenario. The system and the underlying concept can be used as a building block for remote and distributed elderly care services, in which the elderly live autonomously in their homes, but have the attention of a caregiver when needed.

scholarly journals Low-Cost WASN for Real-Time Soundmap Generation

2021 ◽  
Vol 6 (1) ◽  
pp. 57
Author(s):  
Gerardo José Ginovart-Panisello ◽  
Ester Vidaña-Vila ◽  
Selene Caro-Via ◽  
Carme Martínez-Suquía ◽  
Marc Freixes ◽  
...  
Keyword(s):  
Real Time ◽  
Acoustic Analysis ◽  
Road Traffic ◽  
Low Cost ◽  
Traffic Noise ◽  
The Elderly ◽  
Road Traffic Noise ◽  
Public Places ◽  
Changing Patterns ◽  
Real Scenario

Recent advances in technology have enabled the development of affordable low-cost acoustic monitoring systems, as a response of several fields of application that require a close acoustic analysis in real-time: road traffic noise in crowded cities, biodiversity conservation in natural parks, behavioural tracking in the elderly living alone and even surveillance in public places for safety reasons. This paper presents a low-cost wireless acoustic sensor network developed to gather acoustic data to build a 24/7 real-time soundmap. Each node of the network comprises an omnidirectional microphone and a computation unit, which processes acoustic information locally to obtain nonsensitive data (i.e., equivalent continuous loudness levels or acoustic event labels) that are sent to a cloud server. Moreover, it has also been studied the placement of the acoustic sensors in a real scenario, following acoustics criteria. The ultimate goal of the deployed system is to enable the following functions: (i) to measure the Leq in real-time in a predefined window, (ii) to identify changing patterns in the previous measurements so that anomalous situations can be detected and (iii) to prevent and attend potential irregular situations. The proposed network aims to encourage the use of real-time non-invasive devices to obtain behavioural and environmental information, in order to take decisions in real-time.

RFID and Assisted Living for the Elderly

2010 ◽  
pp. 119-136 ◽  
Author(s):  
David Parry ◽  
Judith Symonds
Keyword(s):  
Assisted Living ◽  
Radio Frequency Identification ◽  
Low Cost ◽  
The Elderly ◽  
Living System ◽  
Support Tool ◽  
Home Support ◽  
Frequency Identification ◽  
Partially Sighted

Radio-frequency Identification (RFID) offers a potentially flexible and low cost method of locating objects and tracking people within buildings. RFID systems generally require less infrastructure to be installed than other solutions but have their own limitations. As part of an assisted living system, RFID tools may be useful to locate lost objects, support blind and partially sighted people with daily living activities, and assist in the rehabilitation of adults with acquired brain injury. This chapter outlines the requirements and the role of RFID in assisting people in these three areas. The development of a prototype RFID home support tool is described and some of the issues and challenges raised are discussed. The system is designed to support assisted living for elderly and infirm people in a simple, usable and extensible way in particular for supporting the finding and identification of commonly used and lost objects such as spectacles. This approach can also be used to extend the tagged domain to commonly visited areas, and provide support for the analysis of common activities, and rehabilitation.

scholarly journals Machine-Learning-Based Elderly Stroke Monitoring System Using Electroencephalography Vital Signals

2021 ◽  
Vol 11 (4) ◽  
pp. 1761
Author(s):  
Yoon-A Choi ◽  
Sejin Park ◽  
Jong-Arm Jun ◽  
Chee Meng Benjamin Ho ◽  
Cheol-Sig Pyo ◽  
...  
Keyword(s):  
Heart Disease ◽  
Real Time ◽  
Monitoring System ◽  
Imaging Techniques ◽  
Low Cost ◽  
The Elderly ◽  
Healthcare Services ◽  
Early Disease Detection ◽  
Magnetic Resonance Imaging Mri ◽  
And Performance

Stroke is the third highest cause of death worldwide after cancer and heart disease, and the number of stroke diseases due to aging is set to at least triple by 2030. As the top three causes of death worldwide are all related to chronic disease, the importance of healthcare is increasing even more. Models that can predict real-time health conditions and diseases using various healthcare services are attracting increasing attention. Most diagnosis and prediction methods of stroke for the elderly involve imaging techniques such as magnetic resonance imaging (MRI). It is difficult to rapidly and accurately diagnose and predict stroke diseases due to the long testing times and high costs associated with MRI. Thus, in this paper, we design and implement a health monitoring system that can predict the precursors of stroke diseases in the elderly in real time during daily walking. First, raw electroencephalography (EEG) data from six channels were preprocessed via Fast Fourier Transform (FFT). The raw EEG power values were then extracted from the raw spectra: alpha (α), beta (β), gamma (γ), delta (δ), and theta (θ) as well as the low β, high β, and θ to β ratio, respectively. The experiments in this paper confirm that the important features of EEG biometric signals alone during walking can accurately determine stroke precursors and occurrence in the elderly with more than 90% accuracy. Further, the Random Forest algorithm with quartiles and Z-score normalization validates the clinical significance and performance of the system proposed in this paper with a 92.51% stroke prediction accuracy. The proposed system can be implemented at a low cost, and it can be applied for early disease detection and prediction using the precursor symptoms of real-time stroke. Furthermore, it is expected that it will be able to detect other diseases such as cancer and heart disease in the future.

scholarly journals Modeling elderly behavioral patterns in single-person households

2021 ◽  
Author(s):  
Márcio Renê Brandão Soussa ◽  
Valter de Senna ◽  
Valéria Loureiro da Silva ◽  
Charles Lima Soares
Keyword(s):  
Computational Model ◽  
Assisted Living ◽  
Low Cost ◽  
Elderly Person ◽  
Computational Cost ◽  
Kernel Estimator ◽  
Well Being ◽  
The Elderly ◽  
Ease Of Use ◽  
Motion Sensors

AbstractThis paper proposes and describes an unsupervised computational model that monitors an elderly person who lives alone and issues alarms when a risk to the elderly person’s well-being is identified. This model is based on data extracted exclusively from passive infrared motion sensors connected to a ZigBee wireless network. The proposed monitoring system and model is non-intrusive, does not capture any images, and does not require any interaction with the monitored person. Thus, it is more likely to be adopted by members of the elderly population who might reject other more intrusive or complex types of technology. The developed computational model for activity discovery employs a kernel estimator and local outlier factor calculation, which are reliable and have a low computational cost. This model was tested with data collected over a period of 25 days from two elderly volunteers who live alone and have fairly different routines. The results demonstrate the model’s ability to learn relevant behaviors, as well as identify and issue alarms for atypical activities that can be suggestive of health problems. This low-cost, minimalistic sensor network approach is especially suited to the reality of underdeveloped (and developing) countries where assisted living communities are not available and low cost and ease of use are paramount.

Introduction

2018 ◽  
Author(s):  
Mikhail Kogan ◽  
Kyle Meehan
Keyword(s):  
Health Care ◽  
Health Care Providers ◽  
Assisted Living ◽  
Independent Living ◽  
Low Cost ◽  
The Elderly ◽  
Care Providers ◽  
Patient Centered ◽  
Geriatric Population ◽  
Outpatient Settings

Integrative geriatrics is the new field of medicine that advocates for whole-person, patient-centered, primarily nonpharmacological approaches to the medical care of the elderly. Most current geriatric practices overprescribe medications and procedures and underuse nonpharmacological low-cost and high-touch methods. Integrative geriatrics interventions such as nutrition, movement therapies, and mind–body and spirituality approaches allow patients to follow a different path to their health care. This book provides detailed evidence-based information for all health care providers and advocates who work with the geriatric population. Directed toward providers in outpatient settings and to those who work in nursing homes, assisted living, independent living, and senior community centers, it also provides valuable information for leaders and politicians who are setting up policies and procedures for the care of the elderly who are looking for safer, less costly, and more patient-centered approaches.

scholarly journals AI-Based Stroke Disease Prediction System Using Real-Time Electromyography Signals

2020 ◽  
Vol 10 (19) ◽  
pp. 6791
Author(s):  
Jaehak Yu ◽  
Sejin Park ◽  
Soon-Hyun Kwon ◽  
Chee Meng Benjamin Ho ◽  
Cheol-Sig Pyo ◽  
...  
Keyword(s):  
Random Forest ◽  
Real Time ◽  
Prediction Models ◽  
Short Term Memory ◽  
Low Cost ◽  
The Elderly ◽  
Prediction System ◽  
Appropriate Treatment ◽  
Prediction Systems ◽  
Time Diagnosis

Stroke is a leading cause of disabilities in adults and the elderly which can result in numerous social or economic difficulties. If left untreated, stroke can lead to death. In most cases, patients with stroke have been observed to have abnormal bio-signals (i.e., ECG). Therefore, if individuals are monitored and have their bio-signals measured and accurately assessed in real-time, they can receive appropriate treatment quickly. However, most diagnosis and prediction systems for stroke are image analysis tools such as CT or MRI, which are expensive and difficult to use for real-time diagnosis. In this paper, we developed a stroke prediction system that detects stroke using real-time bio-signals with artificial intelligence (AI). Both machine learning (Random Forest) and deep learning (Long Short-Term Memory) algorithms were used in our system. EMG (Electromyography) bio-signals were collected in real time from thighs and calves, after which the important features were extracted, and prediction models were developed based on everyday activities. Prediction accuracies of 90.38% for Random Forest and of 98.958% for LSTM were obtained for our proposed system. This system can be considered an alternative, low-cost, real-time diagnosis system that can obtain accurate stroke prediction and can potentially be used for other diseases such as heart disease.

LOW COST REAL TIME ROCKET TELEMETRY SYSTEM DESIGN

2019 ◽  
Author(s):  
Gabriel de Almeida Souza ◽  
Larissa Barbosa ◽  
Glênio Ramalho ◽  
Alexandre Zuquete Guarato
Keyword(s):  
System Design ◽  
Real Time ◽  
Low Cost ◽  
Telemetry System
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