Real time falls prevention and detection with biofeedback monitoring solution for mobile environments

Horta, Edgar T.; Lopes, Ivo C.; Rodrigues, Joel J. P. C.; Misra, Sudip

doi:10.1109/healthcom.2013.6720746

Cited by 18 publications

(11 citation statements)

References 8 publications

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“…Pre-FPIs aim to overcome intrinsic fall risk factors such as vision, balance, muscle strength and cognitive decline [ 41 , 42 ]. With regard to intrinsic fall risk factors, functional ability was the main focus of a number of studies [ 43 , 44 , 45 ]. In these studies, various technologies were used to proactively mitigate observed deficits in functional ability.…”

Section: Fall Prevention Systemsmentioning

confidence: 99%

Fall Prediction and Prevention Systems: Recent Trends, Challenges, and Future Research Directions

Rajagopalan

Litvan

Jung

2017

Sensors

127

101

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Fall prediction is a multifaceted problem that involves complex interactions between physiological, behavioral, and environmental factors. Existing fall detection and prediction systems mainly focus on physiological factors such as gait, vision, and cognition, and do not address the multifactorial nature of falls. In addition, these systems lack efficient user interfaces and feedback for preventing future falls. Recent advances in internet of things (IoT) and mobile technologies offer ample opportunities for integrating contextual information about patient behavior and environment along with physiological health data for predicting falls. This article reviews the state-of-the-art in fall detection and prediction systems. It also describes the challenges, limitations, and future directions in the design and implementation of effective fall prediction and prevention systems.

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Section: Fall Prevention Systemsmentioning

confidence: 99%

Fall Prediction and Prevention Systems: Recent Trends, Challenges, and Future Research Directions

Rajagopalan

Litvan

Jung

2017

Sensors

127

101

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show abstract

“…In some cases, the smartphone operates under a more ambitious monitoring scheme not only intended to detect falls but also other biosignals. For example, the systems presented by Horta et al in [52,53] integrate a smartphone and a set of Bluetooth-enabled medical sensors aimed at providing biofeedback monitoring. In other cases, the additional sensors are incorporated to the FDS in order to help the personal device with the detection decision.…”

Section: Consumption In Hybrid Fall Detection Architectures (Combininmentioning

confidence: 99%

Consumption Analysis of Smartphone based Fall Detection Systems with Multiple External Wireless Sensors

González-Cañete

Casilari

2020

Sensors

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Fall Detection Systems (FDSs) based on wearable technologies have gained much research attention in recent years. Due to the networking and computing capabilities of smartphones, these widespread personal devices have been proposed to deploy cost-effective wearable systems intended for automatic fall detection. In spite of the fact that smartphones are natively provided with inertial sensors (accelerometers and gyroscopes), the effectiveness of a smartphone-based FDS can be improved if it also exploits the measurements collected by small low-power wireless sensors, which can be firmly attached to the user’s body without causing discomfort. For these architectures with multiple sensing points, the smartphone transported by the user can act as the core of the FDS architecture by processing and analyzing the data measured by the external sensors and transmitting the corresponding alarm whenever a fall is detected. In this context, the wireless communications with the sensors and with the remote monitoring point may impact on the general performance of the smartphone and, in particular, on the battery lifetime. In contrast with most works in the literature (which disregard the real feasibility of implementing an FDS on a smartphone), this paper explores the actual potential of current commercial smartphones to put into operation an FDS that incorporates several external sensors. This study analyzes diverse operational aspects that may influence the consumption (as the use of a GPS sensor, the coexistence with other apps, the retransmission of the measurements to an external server, etc.) and identifies practical scenarios in which the deployment of a smartphone-based FDS is viable.

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“…Typically, ECG is used to assess the electrical and muscular operations of the heart but ECG sensing can Advances in Human-Computer Interaction 3 also determine abnormalities that might lead to a fall [23,24].…”

Section: Classification Of Fall Factorsmentioning

confidence: 99%

A Review on Fall Prediction and Prevention System for Personal Devices: Evaluation and Experimental Results

Hemmatpour

Ferrero

Montrucchio

et al. 2019

Advances in Human-Computer Interaction

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Injuries due to unintentional falls cause high social cost in which several systems have been developed to reduce them. Recently, two trends can be recognized. Firstly, the market is dominated by fall detection systems, which activate an alarm after a fall occurrence, but the focus is moving towards predicting and preventing a fall, as it is the most promising approach to avoid a fall injury. Secondly, personal devices, such as smartphones, are being exploited for implementing fall systems, because they are commonly carried by the user most of the day. This paper reviews various fall prediction and prevention systems, with a particular interest to the ones that can rely on the sensors embedded in a smartphone, i.e., accelerometer and gyroscope. Kinematic features obtained from the data collected from accelerometer and gyroscope have been evaluated in combination with different machine learning algorithms. An experimental analysis compares the evaluated approaches by evaluating their accuracy and ability to predict and prevent a fall. Results show that tilt features in combination with a decision tree algorithm present the best performance.

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Real time falls prevention and detection with biofeedback monitoring solution for mobile environments

Cited by 18 publications

References 8 publications

Fall Prediction and Prevention Systems: Recent Trends, Challenges, and Future Research Directions

Fall Prediction and Prevention Systems: Recent Trends, Challenges, and Future Research Directions

Consumption Analysis of Smartphone based Fall Detection Systems with Multiple External Wireless Sensors

A Review on Fall Prediction and Prevention System for Personal Devices: Evaluation and Experimental Results

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