A Real-Time Patient Monitoring Framework for Fall Detection

International Journal of Electrical and Computer Engineering (IJECE)

Elder citizens face sudden fall, which can lead to injuries of both destructive and non-virulent. These sudden falls are later more precarious than diseases like heart attack, blood sugar, blood pressure because these can be untreated for a lengthy time which can lead to death. Elder citizen who experiences a precipitous fall, carry out their communal life narrowed. Therefore, a shrewd and adequate anti-fallen system is required for aiding elderly health care, specifically to those who live individually. So, it can identify and anticipate a precipitous fall through appropriate human activity recognition. In this study, we have suggested an end-edge-cloud based wearable EdgeFall architecture for elderly care. We have performed simulation setups to clarify the query of why we need such a strategy, and its validity. We have achieved maximum 91.87% accuracy with 1.6% false alarm rate (FAR). These empirical results indicate the superiority of using tightly couple multiple information for...

Electronics, 2022

Human falls are an issue that especially affects elderly people, resulting in permanent disabilities or even in the person’s death. Preventing human falls is a social desire, but it is almost impossible to achieve because it is not possible to ensure full prevention. A possible solution is the detection of human falls in near real-time so that help can quickly be provided. This has the potential to greatly reduce the severity of the fall in long-term health consequences. This work proposes a solution based on the internet of things devices installed in people’s homes. The proposed non-wearable solution is non-intrusive and can be deployed not only in homes but also in hospitals, rehabilitation facilities, and elderly homes. The solution uses a three-layered computation architecture composed of edge, fog, and cloud. A mathematical model using the Morlet wavelet and an artificial intelligence model using artificial neural networks are used for human fall classification; both approache...

International Journal of Medical Engineering and Informatics, 2020

According to the statistics of national council on aging (NCOA), these days rate of deaths in the old aged people has reached a critical state that for every 11 seconds an older adult is being treated for a fall and for every 19 minutes a death is taking place. Statistics by north eastern Ohio state reveals that, the rate of emergency room treatment of fall related injuries in persons aged 75 and older approached 80 per 1,000 per year in women and 60 per 1,000 per year in men. Falls in elders vary from that of the falls in kids, as the healing power in the old people slackens with their age. We present a frugal and affordable system that could monitor the motion of the old people and can detect their fall. We use IoT, sensors and data analytics to build this system. The system is tested for its functioning and is fool proof.

ECTI Transactions on Computer and Information Technology (ECTI-CIT), 2017

Caring for a rapid and ever-increasing older population, providing social support and monitoring emotional wellness, is the most immediate and most urgent challenge prompted by the global aging of baby boomers. Elderly assisted systems do not only promote independent lifestyles, enhancing their quality of life, but also reduce stress and worry of families and friends. While being physically active is beneficial and much encouraged, it does increase the risk of falls. We developed an affordable fall detection and response tracking application on the cloud platform; mobile cloud computing is a major evolution with rising impact in information technology and enterprises. Our system, named REDLE, features push notifications for fall alerts and real-time maps for tracking and providing locations and phone numbers of nearby hospitals. Implemented on Android, it captures signals from an embedded tri-axial accelerometer and a global positioning system sensor. Coupled with an efficient threshold-based fall detection algorithm for instantaneous responses, REDLE achieved a near perfect fall detection rate and accurate tracking. Users enjoyed the smoothness of our interactive interface, and complimented on its ease of use and familiarity.

IRJET, 2022

IoT) are the main techniques that have been developed to gather various information data for different services and application. Monitoring of elderly and disabled people is very challenging and due to accident which may occur due to some activities like falls. In smart homes fall is considered as the main reason for death of post traumatic complication by using artificial intelligence(AI),Internetofthings,wearables,smartphones etc.we can avoid this issues.this will helps the survival rate of the person who needs help.This leads to a suitable design fall detection system for smart homecare.In this paper we are trying to implement an IoT enabled elderely fall detection model using optimal deep convolutional nuero network for smart homecare(IMEFD-ODCNN) for smart home care.The main aim of the IMEFD-OCDNN model is to enable smartphones and intelligent deep learning algorithms to detect the event of falls in the smarthomes.Falling is among the foremost damaging event elderly people may experience. With the ever-growing aging population, there's an urgent need for the event of fall detection systems. due to the rapid development of sensor networks and also the Internet of Things (IoT), human-computer interaction using sensor fusion has been considered a good method to handle the matter of fall detection

2018 IEEE 9th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON)

Detecting patterns in real time streaming data has been an interesting and challenging data analytics problem. With the proliferation of a variety of sensor devices, real-time analytics of data from the Internet of Things (IoT) to learn regular and irregular patterns has become an important machine learning problem to enable predictive analytics for automated notification and decision support. In this work, we address the problem of learning an irregular human activity pattern, fall, from streaming IoT data from wearable sensors. We present a deep neural network model for detecting fall based on accelerometer data giving 98.75 percent accuracy using an online physical activity monitoring dataset called "MobiAct", which was published by Vavoulas et al. The initial model was developed using IBM Watson studio and then later transferred and deployed on IBM Cloud with the streaming analytics service supported by IBM Streams for monitoring real-time IoT data. We also present the systems architecture of the real-time fall detection framework that we intend to use with Mbientlab's wearable health monitoring sensors for real time patient monitoring at retirement homes or rehabilitation clinics.

International Journal of Innovative Technology and Exploring Engineering, 2020

Statistics suggest that, falls in elderly people are more common than we usually anticipate. These falls are often the leading cause of grave injuries and sometimes even lead to death. Astonishingly, falls are the reason of death in 70% of people in the age group of 75 and older. “More than 90 percent of hip fractures occur as a result of falls, with most of these fractures occurring in persons over 70 years of age. One third of community-dwelling elderly persons and 60 percent of nursing home residents fall each year.” (George F. Fuller, COL, MC, USA, White House Medical Clinic, Washington, D.C.; Am Fam Physician, 2000). With such statistical data in hand, it has become an urgent need to device a system to alert the people nearby and attract the help that is direly needed in the situations like these. The purpose of this project is to provide the necessary help through a device that would detect the fall through a series of sensors. The device essentially, measures the speed, orien...

IEEE Access

Recently, the techniques of Internet of Things (IoT) and mobile communications have been developed to gather human and environment information data for a variety of intelligent services and applications. Remote monitoring of elderly and disabled people living in smart homes is highly challenging due to probable accidents which might occur due to daily activities such as falls. For elderly people, fall is considered as a major reason for death of post-traumatic complication. So, early identification of elderly people falls in smart homes is needed to increase the survival rate of the person or offer required support. Recently, the advent of artificial intelligence (AI), IoT, wearables, smartphones, etc. makes it feasible to design fall detection systems for smart homecare. In this view, this paper presents an IoT enabled elderly fall detection model using optimal deep convolutional neural network (IMEFD-ODCNN) for smart homecare. The goal of the IMEFD-ODCNN model is to enable smartphones and intelligent deep learning (DL) algorithms to detect the occurrence of falls in the smart home. Primarily, the input video captured by the IoT devices is pre-processed in different ways like resizing, augmentation, and min-max based normalization. Besides, SqueezeNet model is employed as a feature extraction technique to derive appropriate feature vectors for fall detection. In addition, the hyperparameter tuning of the SqueezeNet model takes place using the salp swarm optimization (SSO) algorithm. Finally, sparrow search optimization algorithm (SSOA) with variational autoencoder (VAE), called SSOA-VAE based classifier is employed for the classification of fall and non-fall events. Finally, in case of fall event detected, the smartphone sends an alert to the caretakers and hospital management. The performance validation of the IMEFD-ODCNN model takes place on UR fall detection dataset and multiple cameras fall dataset. The experimental outcomes highlighted the promising performance of the IMEFD-ODCNN model over the recent methods with the maximum accuracy of 99.76% and 99.57% on the multiple cameras fall and UR fall detection dataset.

TELKOMNIKA Telecommunication Computing Electronics and Control, 2024

Health monitoring systems based on the internet of things (IoT) improve patient well-being and reduce mortality risks. Machine learning techniques are most helpful in early fall prediction and detection. In this paper, fall prediction analysis and decision-making are done with existing benchmark clinical records. Classification techniques are incorporated to track the consistency and precision of data acquired by the IoT-based remote health monitoring for elderly people, especially those who are living alone. This work undertakes two approaches to early predicting a patient's acute illness. The first approach has analyzed the existing benchmark patient activity data with different features. This approach builds the classification model for fall incidence with the help of machine learning models. In second approach, we collect real-time sensor data such as blood pressure and heart rate from IoT sensor gadgets which are transmitted to the prediction model for early prediction. Experimental results prove that the random forest (RF) classifiers and XGBoost provides the maximum accuracy.

Sensors

This paper presents SmartFall, an Android app that uses accelerometer data collected from a commodity-based smartwatch Internet of Things (IoT) device to detect falls. The smartwatch is paired with a smartphone that runs the SmartFall application, which performs the computation necessary for the prediction of falls in real time without incurring latency in communicating with a cloud server, while also preserving data privacy. We experimented with both traditional (Support Vector Machine and Naive Bayes) and non-traditional (Deep Learning) machine learning algorithms for the creation of fall detection models using three different fall datasets (Smartwatch, Notch, Farseeing). Our results show that a Deep Learning model for fall detection generally outperforms more traditional models across the three datasets. This is attributed to the Deep Learning model’s ability to automatically learn subtle features from the raw accelerometer data that are not available to Naive Bayes and Support V...