Exploring smartphone sensors for fall detection

Figueiredo, Isabel N.; Leal, Carlos; Pinto, Luís; Bolito, Jason; Lemos, André

doi:10.1186/s13678-016-0004-1

Cited by 40 publications

(25 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Although TBA based techniques require less computation power in comparison to ML based techniques, the latter provides better accuracy. The power consumption of various fall detection systems has been extensively discussed in previous researches [21][22][23][24][25]. To overcome the limitations of a single technique, a combination of TBA and ML based systems are being widely developed.…”

Section: Introductionmentioning

confidence: 99%

A Review on Fall Detection Systems Using Data from Smartphone Sensors

Islam¹,

Neom²,

Imtiaz³

et al. 2019

ISI

View full text Add to dashboard Cite

Accidental falls have become one of the most frequent general health issues in recent years due to the rate of occurrence. Individuals aged above 65 are more prone to accidental falls. Accidental falls result in severe injuries such as concussion, head trauma, physical disabilities even to deaths in serious cases if the patients are not rescued in time. Thus, researchers are focusing on developing fall detection systems that facilitate the detection and quick rescue of fall victims. The smartphone-based fall detection systems use various built-in sensors of smartphones mostly Tri-axial accelerometer, magnetometer, gyroscope, and camera. The majority of the systems employ threshold based algorithms (TBA). Some systems use machine learning (ML) based algorithms or a combination of ML and TBA based algorithms to detect falls. Each of these types of systems has its trade-offs. The goal of this paper is to review fall detection systems based on data from smartphone sensors that employ either one of TBA, ML or combination of both. We also present the taxonomy based on systematic comparisons of existing studies for smartphone-based fall detection solutions.

show abstract

Section: Introductionmentioning

confidence: 99%

A Review on Fall Detection Systems Using Data from Smartphone Sensors

Islam¹,

Neom²,

Imtiaz³

et al. 2019

ISI

View full text Add to dashboard Cite

show abstract

“…Anyhow, the performed massive tests illustrate the difficulties of basic threshold-based detection mechanisms to reach a sensitivity higher than 0.8 (80% of well identified falls) when a high specificity (95%) is requested. In future studies more complex algorithms (such as those presented in [46]) should be compared. Anyhow, whenever a new detection technique is proposed, authors should also evaluate if the limitations (in terms of battery consumption or computing power) of current smartphones or, in particular, sensor motes, pose any important restriction to the actual implementation of the algorithm on real devices.…”

Section: Discussionmentioning

confidence: 99%

Analysis of a Smartphone-Based Architecture with Multiple Mobility Sensors for Fall Detection

2016

View full text Add to dashboard Cite

During the last years, many research efforts have been devoted to the definition of Fall Detection Systems (FDSs) that benefit from the inherent computing, communication and sensing capabilities of smartphones. However, employing a smartphone as the unique sensor in a FDS application entails several disadvantages as long as an accurate characterization of the patient’s mobility may force to transport this personal device on an unnatural position. This paper presents a smartphone-based architecture for the automatic detection of falls. The system incorporates a set of small sensing motes that can communicate with the smartphone to help in the fall detection decision. The deployed architecture is systematically evaluated in a testbed with experimental users in order to determine the number and positions of the sensors that optimize the effectiveness of the FDS, as well as to assess the most convenient role of the smartphone in the architecture.

show abstract

“…For example, it was proved [85,86] that the operation of this sensor normally requires more energy than the accelerometer. Figueiredo et al present a smartphone-based FDS in [44], in which diverse features (calculated from the acceleration and angular velocity) are used to produce the detection decision. In their analysis, the authors also highlight that the demanded energy and economical costs of the gyroscope are higher than those of an accelerometer.…”

Section: Related Workmentioning

confidence: 99%

A Study of the Use of Gyroscope Measurements in Wearable Fall Detection Systems

2020

View full text Add to dashboard Cite

Due to the serious impact of falls on the quality of life of the elderly and on the economical sustainability of health systems, the study of new monitoring systems capable of automatically alerting about falls has gained much research interest during the last decade. In the field of Human Activity Recognition, Fall Detection Systems (FDSs) can be contemplated as pattern recognition architectures able to discriminate falls from ordinary Activities of Daily Living (ADLs). In this regard, the combined application of cellular communications and wearable devices that integrate inertial sensors offers a cost-efficient solution to track the user mobility almost ubiquitously. Inertial Measurement Units (IMUs) typically utilized for these architectures, embed an accelerometer and a gyroscope. This paper investigates if the use of the angular velocity (captured by the gyroscope) as an input feature of the movement classifier introduces any benefit with respect to the most common case in which the classification decision is uniquely based on the accelerometry signals. For this purpose, the work assesses the performance of a deep learning architecture (a convolutional neural network) which is optimized to differentiate falls from ADLs as a function of the raw data measured by the two inertial sensors (gyroscope and accelerometer). The system is evaluated against on a well-known public dataset with a high number of mobility traces (falls and ADL) measured from the movements of a wide group of experimental users.

show abstract

Exploring smartphone sensors for fall detection

Cited by 40 publications

References 18 publications

A Review on Fall Detection Systems Using Data from Smartphone Sensors

A Review on Fall Detection Systems Using Data from Smartphone Sensors

Analysis of a Smartphone-Based Architecture with Multiple Mobility Sensors for Fall Detection

A Study of the Use of Gyroscope Measurements in Wearable Fall Detection Systems

Contact Info

Product

Resources

About