scholarly journals Design and Implementation of a Wearable Accelerometer-Based Motion/Tilt Sensing Internet of Things Module and Its Application to Bed Fall Prevention

Biosensors ◽  
2021 ◽  
Vol 11 (11) ◽  
pp. 428
Author(s):  
Wen-Yen Lin ◽  
Chien-Hung Chen ◽  
Ming-Yih Lee
Keyword(s):  
Internet Of Things ◽  
Fall Prevention ◽  
Fall Detection ◽  
Low Complexity ◽  
Patient Falls ◽  
Basic Module ◽  
Preventive Actions ◽  
Postural Changes ◽  
Health Care Applications ◽  
Fall Accidents

Accelerometer-based motion sensing has been extensively applied to fall detection. However, such applications can only detect fall accidents; therefore, a system that can prevent fall accidents is desirable. Bed falls account for more than half of patient falls and are preceded by a clear warning indicator: the patient attempting to get out of bed. This study designed and implemented an Internet of Things module, namely, Bluetooth low-energy-enabled Accelerometer-based Sensing In a Chip-packaging (BASIC) module, with a tilt-sensing algorithm based on the patented low-complexity COordinate Rotation DIgital Computer (CORDIC)-based algorithm for tilt angle conversions. It is applied for detecting the postural changes (from lying down to sitting up) and to protect individuals at a high risk of bed falls by prompting caregivers to take preventive actions and assist individuals trying to get up. This module demonstrates how motion and tilt sensing can be applied to bed fall prevention. The module can be further miniaturized or integrated into a wearable device and commercialized in smart health-care applications for bed fall prevention in hospitals and homes.

Fall Detection via Inaudible Acoustic Sensing

2021 ◽  
Vol 5 (3) ◽  
pp. 1-21
Author(s):  
Jie Lian ◽  
Xu Yuan ◽  
Ming Li ◽  
Nian-Feng Tzeng
Keyword(s):  
Continuous Wave ◽  
Detection System ◽  
Fall Detection ◽  
Superior Performance ◽  
Medical Assistance ◽  
Acoustic Sensing ◽  
Value Decomposition ◽  
Human Motions ◽  
Audio Device ◽  
Fall Accidents

The fall detection system is of critical importance in protecting elders through promptly discovering fall accidents to provide immediate medical assistance, potentially saving elders' lives. This paper aims to develop a novel and lightweight fall detection system by relying solely on a home audio device via inaudible acoustic sensing, to recognize fall occurrences for wide home deployment. In particular, we program the audio device to let its speaker emit 20kHz continuous wave, while utilizing a microphone to record reflected signals for capturing the Doppler shift caused by the fall. Considering interferences from different factors, we first develop a set of solutions for their removal to get clean spectrograms and then apply the power burst curve to locate the time points at which human motions happen. A set of effective features is then extracted from the spectrograms for representing the fall patterns, distinguishable from normal activities. We further apply the Singular Value Decomposition (SVD) and K-mean algorithms to reduce the data feature dimensions and to cluster the data, respectively, before input them to a Hidden Markov Model for training and classification. In the end, our system is implemented and deployed in various environments for evaluation. The experimental results demonstrate that our system can achieve superior performance for detecting fall accidents and is robust to environment changes, i.e., transferable to other environments after training in one environment.

Internet of Things in E-Health

2017 ◽  
pp. 191-197 ◽  
Author(s):  
Branka Rodić Trmčić ◽  
Aleksandra Labus ◽  
Svetlana Mitrović ◽  
Vesna Buha ◽  
Gordana Stanojević
Keyword(s):  
Internet Of Things ◽  
Health Behavior ◽  
Well Being ◽  
Sensor Data ◽  
Main Task ◽  
Preventive Actions ◽  
Framework Model ◽  
The Impact ◽  
Health And Well Being ◽  
Improve Health

The main task of Internet of Things in eHealth solutions is to collect data, connect people, things and processes. This provides a wealth of information that can be useful in decision-making, improving health and well-being. The aim of this study is to identify framework of sensors and application health services to detect sources of stress and stressors and make them visible to users. Also, we aim at extracting relationship between event and sensor data in order to improve health behavior. Evaluation of the proposed framework model will be performed. Model is based on Internet of Things in eHealth and is going to aim to improve health behavior. Following the established pattern of behavior realized through wearable system users will be proposed a preventive actions model. Further, it will examine the impact of changing health behavior on habits, condition and attitudes in relation to well-being and prevention.

Health Monitoring System for Individuals Using Internet of Things

2020 ◽  
pp. 150-164
Author(s):  
Rajkumar Rajasekaran ◽  
Govinda K. ◽  
Jolly Masih ◽  
Sruthi M.
Keyword(s):  
Body Temperature ◽  
Internet Of Things ◽  
Health Monitoring ◽  
Health Personnel ◽  
Body Movement ◽  
Family Members ◽  
The Elderly ◽  
Preventive Actions ◽  
The Family ◽  
Work Health

Most of the elderly citizens are either living by themselves or locked up at home when the rest of the family members go to work. Health of the elderly deteriorates gradually with age, but people fail to notice these changes in everyday life. The elderly are at risk of not receiving attention immediately in the case of emergencies. Internet of things can be used to alert family members and health personnel immediately when an abnormality in the elderly person's health is sensed to prevent discovery of illness at an irrecoverable stage. Internet of things can monitor parameters like heart pulse rate, body temperature, body movement, position, and location, and raise an alert to take immediate preventive actions. Making this system portable is one of the most necessary requirements because it will be worn by the user. That introduces various conditions in itself. For instance, the system should not disturb the patient or be heavy.

scholarly journals Improving falls in nursing homes: a post-fall huddle quality improvement project

2019 ◽  
Vol 8 (2) ◽  
pp. 33
Author(s):  
Tekekee Buckner ◽  
Daisy Sherry
Keyword(s):  
Nursing Homes ◽  
Fall Prevention ◽  
Fall Risk ◽  
Risk Of Falling ◽  
Improvement Project ◽  
Patient Falls ◽  
Root Cause ◽  
Assessment Program ◽  
Increased Risk ◽  
Pertinent Data

Falls are one of the most common preventable health problems in adults 65 years and older (AHRQ, 2013). A fall in this population can have a devastating effect often leading to a significant change in morbidity or death. Adults in assisting living, nursing homes, and skilled facilities (SNF) have an increased risk of falling and having a subsequent fall due to an acute illness, weakness, or confusion. This makes individualizing a plan of care to prevent a secondary fall and identifying the root cause of falls within a facility imperative.In our agency, the fall rate is nearly triple that of the national benchmark. To address this problem, a Post-Fall Huddle project was implemented. The literature recommends and supports the practice of a post-fall assessment program in fall reduction to identify intrinsic and extrinsic fall risk etiologies. There was found to be a reduction in the absolute values of recurrent patient falls per quarterly reporting after the implementation of the post-fall huddle. The results also provided pertinent data that can be used for recommendations in future fall prevention for the SNF 

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