Testing non-wearable fall detection methods in the homes of older adults

Skubic, Marjorie; Harris, Bradford H.; Stone, Erik E.; Ho, K. C.; Su, Bo-Yu; Rantz, Marilyn

doi:10.1109/embc.2016.7590763

Cited by 26 publications

(22 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…There are also concerns that the sensing devices for the task of fall detection may be invasive and breach the privacy of the person [19,32]. Some non-invasive sensing devices, such as a thermal or depth camera, may not fully reveal the identity of a person, but it is difficult to extract discriminative features to identify unseen falls, especially in a highly skewed data scenario [27].…”

mentioning

confidence: 99%

DeepFall: Non-Invasive Fall Detection with Deep Spatio-Temporal Convolutional Autoencoders

Nogas

Khan

Mihailidis

2019

J Healthc Inform Res

View full text Add to dashboard Cite

Human falls rarely occur; however, detecting falls is very important from the health and safety perspective. Due to the rarity of falls, it is difficult to employ supervised classification techniques to detect them. Moreover, in these highly skewed situations it is also difficult to extract domain specific features to identify falls. In this paper, we present a novel framework, DeepFall, which formulates the fall detection problem as an anomaly detection problem. The DeepFall framework presents the novel use of deep spatio-temporal convolutional autoencoders to learn spatial and temporal features from normal activities using non-invasive sensing modalities. We also present a new anomaly scoring method that combines the reconstruction score of frames across a video sequences to detect unseen falls. We tested the DeepFall framework on three publicly available datasets collected through non-invasive sensing modalities, thermal camera and depth cameras and show superior results in comparison to traditional autoencoder and convolutional autoencoder methods to identify unseen falls.

show abstract

mentioning

confidence: 99%

DeepFall: Non-Invasive Fall Detection with Deep Spatio-Temporal Convolutional Autoencoders

Nogas

Khan

Mihailidis

2019

J Healthc Inform Res

View full text Add to dashboard Cite

show abstract

“…A camera (or video camera) is usually used as a sensor, and it is installed on a wall in a fixed position. This method is targeted to detect movements in a given period of time [13,17,18,19,20,21,22]. If the image-processing algorithm detects that a certain object does not move for more than a certain period of time, or if it identifies some unusual activities, it means that the object (human) has fallen [13].…”

Section: B Image-based Methodsmentioning

confidence: 99%

Human Fall Detection using Accelerometer and Gyroscope Sensors in Unconstrained Smartphone Positions

Astriani*,

Heryadi,

Kusuma

et al. 2019

IJRTE

View full text Add to dashboard Cite

This study explored several methods for detecting body falls based on the data captured by the sensors (accelerometer and gyroscope) built in a smartphone carried by a person. The data for this study were collected by recording many sample units from each of the following human activity categories: stand-fall, walk-fall, stand-jump, stand-sit, stand, and walk. Several time-series data captured by the sensors were used as human motion features. One of the challenges of this study was the existence of human body motions whose features resembled those of body falls. In addition, unfixed smartphone positioning made human body falls harder to detect and can lead to high rate of misclassification (not detected as fall). This incident can caused serious bone fracture or even death if the person not handled as immediately as possible because of misclassification. To address this problem, we modified Resultant Acceleration and ∠ Y formulas to address the problem of unconstrained smartphone positions. We proposed to combine five methods such as AGVeSR, Alim, ∠α, GyroReDi, and AGPeak to build a robust detector model to reduce the misclassification. The experiment results showed that the accuracy of the combination of both sensors (accelerometer and gyroscope) outperformed the accuracy of accelerometer only by more than 15%. The decision fusion that used voting involving five methods could boost the accuracy rate by up to 4.15%

show abstract

“…However, smart homes that provide security should also be sensitive to health issues that can jeopardize the well-being of residents, as described in Scenario 2. This includes detection of falls, lack of movement, and significant changes in behavioral patterns [50]–[54]. In the same way that the health of a smart home resident can be monitored by a secure smart home system, so the health of the physical home environment can and should be monitored.…”

Section: Introductionmentioning

confidence: 99%

Smart secure homes: a survey of smart home technologies that sense, assess, and respond to security threats

Dahmen

Cook

Wang

et al. 2017

J Reliable Intell Environ

View full text Add to dashboard Cite

Smart home design has undergone a metamorphosis in recent years. The field has evolved from designing theoretical smart home frameworks and performing scripted tasks in laboratories. Instead, we now find robust smart home technologies that are commonly used by large segments of the population in a variety of settings. Recent smart home applications are focused on activity recognition, health monitoring, and automation. In this paper, we take a look at another important role for smart homes: security. We first explore the numerous ways smart homes can and do provide protection for their residents. Next, we provide a comparative analysis of the alternative tools and research that has been developed for this purpose. We investigate not only existing commercial products that have been introduced but also discuss the numerous research that has been focused on detecting and identifying potential threats. Finally, we close with open challenges and ideas for future research that will keep individuals secure and healthy while in their own homes.

show abstract

Testing non-wearable fall detection methods in the homes of older adults

Cited by 26 publications

References 20 publications

DeepFall: Non-Invasive Fall Detection with Deep Spatio-Temporal Convolutional Autoencoders

DeepFall: Non-Invasive Fall Detection with Deep Spatio-Temporal Convolutional Autoencoders

Human Fall Detection using Accelerometer and Gyroscope Sensors in Unconstrained Smartphone Positions

Smart secure homes: a survey of smart home technologies that sense, assess, and respond to security threats

Contact Info

Product

Resources

About