scholarly journals A Novel Artificial Intelligence Based Internet of Things for Fall Detection of Elderly Care Monitoring

2021 ◽  
pp. 18-31
Author(s):  
Gopinath Gopinath ◽  
◽  
◽  
◽  
◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Elderly Care ◽  
Recognition Rate ◽  
Model Performance ◽  
Fall Detection ◽  
The Elderly ◽  
Severe Injuries ◽  
Independent Life ◽  
Early Fall ◽  
Post Traumatic

A fall of an older adult often leads to severe injuries and is found to be a significant reason for the death due to post-traumatic complications. Many falls happen in the home atmosphere and prevail unrecognized. Thus, the need for reliable early fall detection is necessary for fast help. Lately, the emergence of wearables, smartphones, IoT, etc., made it possible to develop systems fall detection which aids in the remote monitoring of the elderly. The goal is to allow intelligent algorithms and smartphones to detect falls for elderly care and to monitor them regularly. This work presents the Artificial Intelligence of Things for Fall Detection (AIOTFD) system using a slime mould algorithm (SMA) to optimize the final data. The features extracted using SqueezeNet further CNN based SMA used for data optimization. The validation of the AIOTFD model performance is evaluated through the Multiple Cameras Fall Dataset (MCFD) and UR Fall Detection dataset (URFD). The empirical results accentuated the assuring realization of the model compared to other state-of the art methods.The obtained results shows our proposed AIOTFD attains accuracy of 99.82% and 99.79% and databases can be used for additional investigation and optimizations to increase the recognition rate to enhance the independent life of the elderly.

scholarly journals Deep Learning Based Fall Detection Algorithms for Embedded Systems, Smartwatches, and IoT Devices Using Accelerometers

Technologies ◽  
2020 ◽  
Vol 8 (4) ◽  
pp. 72
Author(s):  
Dimitri Kraft ◽  
Karthik Srinivasan ◽  
Gerald Bieber
Keyword(s):  
Deep Learning ◽  
Embedded Systems ◽  
Recognition Rate ◽  
Elderly Person ◽  
Fall Detection ◽  
The Elderly ◽  
Detection Algorithms ◽  
Independent Life ◽  
Iot Devices ◽  
Serious Injuries

A fall of an elderly person often leads to serious injuries or even death. Many falls occur in the home environment and remain unrecognized. Therefore, a reliable fall detection is absolutely necessary for a fast help. Wrist-worn accelerometer based fall detection systems are developed, but the accuracy and precision are not standardized, comparable, or sometimes even known. In this work, we present an overview about existing public databases with sensor based fall datasets and harmonize existing wrist-worn datasets for a broader and robust evaluation. Furthermore, we are analyzing the current possible recognition rate of fall detection using deep learning algorithms for mobile and embedded systems. The presented results and databases can be used for further research and optimizations in order to increase the recognition rate to enhance the independent life of the elderly. Furthermore, we give an outlook for a convenient application and wrist device.

scholarly journals eHomeSeniors Dataset: An Infrared Thermal Sensor Dataset for Automatic Fall Detection Research

Sensors ◽  
2019 ◽  
Vol 19 (20) ◽  
pp. 4565 ◽  
Author(s):  
Fabián Riquelme ◽  
Cristina Espinoza ◽  
Tomás Rodenas ◽  
Jean-Gabriel Minonzio ◽  
Carla Taramasco
Keyword(s):  
Older Adults ◽  
Young People ◽  
Elderly Care ◽  
Fall Detection ◽  
The Elderly ◽  
Research Area ◽  
Wearable Devices ◽  
Learning Approaches ◽  
Negative Consequences ◽  
Public Datasets

Automatic fall detection is a very active research area, which has grown explosively since the 2010s, especially focused on elderly care. Rapid detection of falls favors early awareness from the injured person, reducing a series of negative consequences in the health of the elderly. Currently, there are several fall detection systems (FDSs), mostly based on predictive and machine-learning approaches. These algorithms are based on different data sources, such as wearable devices, ambient-based sensors, or vision/camera-based approaches. While wearable devices like inertial measurement units (IMUs) and smartphones entail a dependence on their use, most image-based devices like Kinect sensors generate video recordings, which may affect the privacy of the user. Regardless of the device used, most of these FDSs have been tested only in controlled laboratory environments, and there are still no mass commercial FDS. The latter is partly due to the impossibility of counting, for ethical reasons, with datasets generated by falls of real older adults. All public datasets generated in laboratory are performed by young people, without considering the differences in acceleration and falling features of older adults. Given the above, this article presents the eHomeSeniors dataset, a new public dataset which is innovative in at least three aspects: first, it collects data from two different privacy-friendly infrared thermal sensors; second, it is constructed by two types of volunteers: normal young people (as usual) and performing artists, with the latter group assisted by a physiotherapist to emulate the real fall conditions of older adults; and third, the types of falls selected are the result of a thorough literature review.

scholarly journals An Intelligent Error Correction Algorithm for Elderly Care Robots

2021 ◽  
Vol 11 (16) ◽  
pp. 7316
Author(s):  
Xin Zhang ◽  
Zhiquan Feng ◽  
Xiaohui Yang ◽  
Tao Xu ◽  
Xiaoyu Qiu ◽  
...  
Keyword(s):  
Error Correction ◽  
Gesture Recognition ◽  
Elderly Care ◽  
Recognition Rate ◽  
The Elderly ◽  
Correction Algorithm ◽  
The Neural Network ◽  
Recognition Systems ◽  
Error Correction Algorithm ◽  
Game Rule

With the development of deep learning, gesture recognition systems based on the neural network have become quite advanced, but the application effect in the elderly is not ideal. Due to the change of the palm shape of the elderly, the gesture recognition rate of most elderly people is only about 70%. Therefore, in this paper, an intelligent gesture error correction algorithm based on game rules is proposed on the basis of the AlexNet. Firstly, this paper studies the differences between the palms of the elderly and young people. It also analyzes the misread gesture by using the probability statistics method and establishes a misread-gesture database. Then, based on the misreading-gesture library, the maximum channel number of different gestures in the fifth layer is studied by using the similar curve algorithm and the Pearson algorithm. Finally, error correction is completed under the game rule. The experimental results show that the gesture recognition rate of the elderly can be improved to more than 90% by using the proposed intelligent error correction algorithm. The elderly-accompanying robot can understand people’s intentions more accurately, which is well received by users.

scholarly journals Possible Life Saver: A Review on Human Fall Detection Technology

Robotics ◽  
2020 ◽  
Vol 9 (3) ◽  
pp. 55 ◽  
Author(s):  
Zhuo Wang ◽  
Vignesh Ramamoorthy ◽  
Udi Gal ◽  
Allon Guez
Keyword(s):  
Elderly Population ◽  
Fall Detection ◽  
The Elderly ◽  
Severe Injuries ◽  
Detection Systems ◽  
Detection Technology ◽  
Ambient Systems ◽  
Measure Of Performance ◽  
Privacy Issues ◽  
Current Systems

Among humans, falls are a serious health problem causing severe injuries and even death for the elderly population. Besides, falls are also a major safety threat to bikers, skiers, construction workers, and others. Fortunately, with the advancements of technologies, the number of proposed fall detection systems and devices has increased dramatically and some of them are already in the market. Fall detection devices/systems can be categorized based on their architectures as wearable devices, ambient systems, image processing-based systems, and hybrid systems, which employ a combination of two or more of these methodologies. In this review paper, a comparison is made among these major fall detection systems, devices, and algorithms in terms of their proposed approaches and measure of performance. Issues with the current systems such as lack of portability and reliability are presented as well. Development trends such as the use of smartphones, machine learning, and EEG are recognized. Challenges with privacy issues, limited real fall data, and ergonomic design deficiency are also discussed.

scholarly journals Development of fall detection and activity recognition using threshold based method and neural network

2020 ◽  
Vol 17 (3) ◽  
pp. 1338
Author(s):  
Sai Siong Jun ◽  
Hafiz Rashidi Ramli ◽  
Azura Che Soh ◽  
Noor Ain Kamsani ◽  
Raja Kamil Raja Ahmad ◽  
...  
Keyword(s):  
Neural Network ◽  
Activity Recognition ◽  
Human Activity ◽  
Daily Living ◽  
Fall Detection ◽  
The Elderly ◽  
Recognition System ◽  
Severe Injuries ◽  
Series Of Experiments

Falls are dangerous and contribute to over 80% of injury-related hospitalization especially amongst the elderly. Hence, fall detection is important for preventing severe injuries and accidental deaths. Meanwhile, recognizing human activity is important for monitoring health status and quality of life as it can be applied in geriatric care and healthcare in general. This research presents the development of a fall detection and human activity recognition system using Threshold Based Method (TBM) and Neural Network (NN). Intentional forward fall and six other activities of daily living (ADLs), which include running, jumping, walking, sitting, lying, and standing are performed by 15 healthy volunteers in a series of experiments. There are four important stages involved in fall detection and ADL recognition, which are signal filtering, segmentation, features extraction and classification. For classification, TBM achieved an accuracy of 98.41% and 95.40% for fall detection and activity recognition respectively whereas NN achieved an accuracy of 97.78% and 96.77% for fall detection and activity recognition respectively.

scholarly journals CHARMIE: A Collaborative Healthcare and Home Service and Assistant Robot for Elderly Care

2021 ◽  
Vol 11 (16) ◽  
pp. 7248
Author(s):  
Tiago Ribeiro ◽  
Fernando Gonçalves ◽  
Inês S. Garcia ◽  
Gil Lopes ◽  
António F. Ribeiro
Keyword(s):  
Elderly Care ◽  
Fall Detection ◽  
The Elderly ◽  
Obstacle Detection ◽  
Human Robot Interaction ◽  
Map Building ◽  
Healthcare Facilities ◽  
Assistive Robots ◽  
Wide Range ◽  
Set Up

The global population is ageing at an unprecedented rate. With changes in life expectancy across the world, three major issues arise: an increasing proportion of senior citizens; cognitive and physical problems progressively affecting the elderly; and a growing number of single-person households. The available data proves the ever-increasing necessity for efficient elderly care solutions such as healthcare service and assistive robots. Additionally, such robotic solutions provide safe healthcare assistance in public health emergencies such as the SARS-CoV-2 virus (COVID-19). CHARMIE is an anthropomorphic collaborative healthcare and domestic assistant robot capable of performing generic service tasks in non-standardised healthcare and domestic environment settings. The combination of its hardware and software solutions demonstrates map building and self-localisation, safe navigation through dynamic obstacle detection and avoidance, different human-robot interaction systems, speech and hearing, pose/gesture estimation and household object manipulation. Moreover, CHARMIE performs end-to-end chores in nursing homes, domestic houses, and healthcare facilities. Some examples of these chores are to help users transport items, fall detection, tidying up rooms, user following, and set up a table. The robot can perform a wide range of chores, either independently or collaboratively. CHARMIE provides a generic robotic solution such that older people can live longer, more independent, and healthier lives.

Directional Human Fall Recognition Using a Pair of Accelerometer and Gyroscope Sensors

2011 ◽  
Vol 135-136 ◽  
pp. 449-454
Author(s):  
Myeong Jun Lim ◽  
Jin Ho Cho ◽  
Young Sun Cho ◽  
Tae Seong Kim
Keyword(s):  
Recognition Rate ◽  
Joint Inflammation ◽  
Severe Case ◽  
Fall Detection ◽  
The Elderly ◽  
Recognition Algorithm ◽  
Neural Nets ◽  
Linear Discriminant ◽  
Detection Algorithms ◽  
Ar Modeling

Human fall in the elderly population is one of the major causes of injury or bone fracture: it can be a cause of various injuries (e.g., fracture, concussion, and joint inflammation). It also could be a possible cause of death in a severe case. To detect human fall, various fall detection algorithms have been devised. Most fall detection algorithms rely on signals from a single accelerometer or gyroscope and use a threshold-based method to detect the human fall. However, these algorithms need careful adjustment of a threshold for each subject and cannot detect the direction of falls. In this study, we propose a novel fall recognition algorithm using a pair of a tri-axial accelerometer and a tri-axial gyroscope. Our fall recognition algorithm utilizes a set of augmented features including autoregressive (AR) modeling coefficients of signals, signal magnitude area (SMA), and gradients of angles from the sensors. After Linear Discriminant Analysis (LDA) of the augmented features, an Artificial Neural Nets (ANNs) is utilized to recognize four directional human falls: namely forward fall, backward fall, right-side fall, and left-side fall. Our recognition results show the mean recognition rate of 95.8%. Our proposed fall recognition technique should be useful in the investigation of fall-related injuries and possibly in the prevention of falls for the elderly.

HOME CARE IN OUTPATIENT CARE – STATE AND DEVELOPMENT IN BULGARIA

2018 ◽  
Vol 28 (2) ◽  
pp. 571-574
Author(s):  
Ivanka Stambolova ◽  
Stefan Stambolov
Keyword(s):  
Home Care ◽  
Outpatient Care ◽  
Care Service ◽  
Elderly Care ◽  
Care Center ◽  
Elderly Person ◽  
Chronically Ill ◽  
The Elderly ◽  
Legal Regulation ◽  
Health And Social Care

In outpatient care the home care, including hospices, is recognized as a model for providing quality, cost-effective and charitable care. The focus is mainly on the care that helps everyday lifeof the patient as well as the relatives, rather than on treatment, and in most cases it takes place in the patients' home. In Europe, in recent years there has been a real "boom" in home care due to demographic processes linked to increased needs for elderly care and chronically ill under the conditions of limited financial resources.In outpatient medical care in our country by means of a national framework contract there are regulated visits to the patient's home by a doctor, as well as visits by medical staff employed by him - nurse, midwife, medical assistant / paramedic / for manipulation, counseling and monitoring. At the same time there is no regulated legal activity in the Republic of Bulgaria, which is essentially the subject of home care.Since 1994 „Caritas“ has carried out the "Home Care" service, which provides a complex - health and social care for over 360 sick adults in a place where the elderly person feels the most comfortable - in their own home. „Caritas Home Care“ is provided by mobile teams of nurses and social assistants who visit the elderly at home and provide them with the necessary care according to their health and social needs.With the establishment of the first „Home Care Center“ in Lozenets region, Sofia, with the support of the PHARE ACCESS program in 2003, the Bulgarian Red Cross introduces in Bulgaria an integrated model for provision of health care and social services in the home of adults, chronically ill and people with permanent disabilities. To date, there are a number of problems in home care related to the realization of home care for patients in need in out-of-hospital settings: lack of legal regulation for home care, lack of qualified staff in outpatient care; lack of organization and structures for care; unsettled funding and the inability of the part of the population that is most in need of care to pay for it, there is no regulation to control the activity. Although home care began over 20 years ago, our country is yet to make its way to the European program called „Home care in Europe“.

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