Human Fall Detection Using Machine Learning Methods: A Survey

Singh, Komal; Rajput, Akshay; Sharma, Sachin

doi:10.33889/ijmems.2020.5.1.014

Cited by 25 publications

(15 citation statements)

References 73 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Kimaya Desai [12] performed human fall detection by deploying a simple 32-bit microcontroller on the wearable belt. Threshold methods and machine classification algorithms are widely used in wearable devices for fall detection [25]. Threshold methods are roughly divided into static threshold methods [26] and dynamic threshold methods [27].…”

Section: Related Workmentioning

confidence: 99%

Fall Detection with CNN-Casual LSTM Network

Wang

Zhan

et al. 2021

Information

View full text Add to dashboard Cite

Falls are one of the main causes of elderly injuries. If the faller can be found in time, further injury can be effectively avoided. In order to protect personal privacy and improve the accuracy of fall detection, this paper proposes a fall detection algorithm using the CNN-Casual LSTM network based on three-axis acceleration and three-axis rotation angular velocity sensors. The neural network in this system includes an encoding layer, a decoding layer, and a ResNet18 classifier. Furthermore, the encoding layer includes three layers of CNN and three layers of Casual LSTM. The decoding layer includes three layers of deconvolution and three layers of Casual LSTM. The decoding layer maps spatio-temporal information to a hidden variable output that is more conducive relative to the work of the classification network, which is classified by ResNet18. Moreover, we used the public data set SisFall to evaluate the performance of the algorithm. The results of the experiments show that the algorithm has high accuracy up to 99.79%.

show abstract

Section: Related Workmentioning

confidence: 99%

Fall Detection with CNN-Casual LSTM Network

Wang

Zhan

et al. 2021

Information

View full text Add to dashboard Cite

show abstract

“…In the last few years, many approaches have been proposed to detect fall of elderly people [ 4 , 5 , 6 , 7 ]. These works can be grouped in three main categories [ 4 ]: ( i ) methods that use wearable sensors to monitor the person and detect abnormal activities during time; ( ii ) solutions that use ambient/fusion to collect room information such as floor vibration and sound, with recent works that also utilize other technologies such as smartphones, Wi-Fi, etc.…”

Section: Related Workmentioning

confidence: 99%

“…In the last few years, a lot of solutions have been developed to decrease the danger caused by falls. For example, there are a lot of works aiming to monitor persons by using cameras, sensors and ambient/fusion [ 4 , 5 , 6 , 7 ]. These methods analyze the motion of persons and aim to distinguish between falls and daily activities.…”

Section: Introductionmentioning

confidence: 99%

Fall Detection of Elderly People Using the Manifold of Positive Semidefinite Matrices

et al. 2021

View full text Add to dashboard Cite

Falls are one of the most critical health care risks for elderly people, being, in some adverse circumstances, an indirect cause of death. Furthermore, demographic forecasts for the future show a growing elderly population worldwide. In this context, models for automatic fall detection and prediction are of paramount relevance, especially AI applications that use ambient, sensors or computer vision. In this paper, we present an approach for fall detection using computer vision techniques. Video sequences of a person in a closed environment are used as inputs to our algorithm. In our approach, we first apply the V2V-PoseNet model to detect 2D body skeleton in every frame. Specifically, our approach involves four steps: (1) the body skeleton is detected by V2V-PoseNet in each frame; (2) joints of skeleton are first mapped into the Riemannian manifold of positive semidefinite matrices of fixed-rank 2 to build time-parameterized trajectories; (3) a temporal warping is performed on the trajectories, providing a (dis-)similarity measure between them; (4) finally, a pairwise proximity function SVM is used to classify them into fall or non-fall, incorporating the (dis-)similarity measure into the kernel function. We evaluated our approach on two publicly available datasets URFD and Charfi. The results of the proposed approach are competitive with respect to state-of-the-art methods, while only involving 2D body skeletons.

show abstract

“…8,9 The human fall detection systems were initially distinguished by the type of devices and sensors used. [10][11][12][13] Systems using wearable devices must be worn by a person under or over their clothes. They include different sensors such as gyroscopes, accelerometers, tilt-meters, myowaves and oscilloscopes.…”

Section: Introductionmentioning

confidence: 99%

“…Today, the influx of increasingly ingenious algorithmic detection methods over the past decade highlights the possibility of achieving very high levels of fall detection for all existing systems. 12,13 Consequently, the dilemma effectiveness-ethical consideration (named also securityprivacy balance) seems to be the real challenge for human fall detection by ICTs, particularly in the older population 8 and in nursing homes. 20 Infrared sensors with a low resolution could be an efficient solution to answer this dilemma.…”

Section: Introductionmentioning

confidence: 99%

Human Fall Detection Using Passive Infrared Sensors with Low Resolution: A Systematic Review

Ben-Sadoun¹,

Michel²,

Annweiler³

et al. 2022

CIA

View full text Add to dashboard Cite

Systems using passive infrared sensors with a low resolution were recently proposed to answer the dilemma effectiveness-ethical considerations for human fall detection by Information and Communication Technologies (ICTs) in older adults. How effective is this type of system? We performed a systematic review to identify studies that investigated the metrological qualities of passive infrared sensors with a maximum resolution of 16×16 pixels to identify falls. The search was conducted on PubMed, ScienceDirect, SpringerLink, IEEE Xplore Digital Library, and MDPI until November 26-28, 2020. We focused on studies testing only these types of sensor. Thirteen articles were "conference papers", five were "original articles" and one was a found in arXiv.org (an open access repository of scientific research). Since four authors "duplicated" their study in two different journals, our review finally analyzed 15 studies. The studies were very heterogeneous with regard to experimental procedures and detection methods, which made it difficult to draw formal conclusions. All studies tested their systems in controlled conditions, mostly in empty rooms. Except for two studies, the overall performance reported for the detection of falls exceeded 85-90% of accuracy, precision, sensitivity or specificity. Systems using two or more sensors and particular detection methods (eg, 3D CNN, CNN with 10-fold cross-validation, LSTM with CNN, LSTM and Voting algorithms) seemed to give the highest levels of performance (> 90%). Future studies should test more this type of system in real-life conditions.

show abstract

Human Fall Detection Using Machine Learning Methods: A Survey

Cited by 25 publications

References 73 publications

Fall Detection with CNN-Casual LSTM Network

Fall Detection with CNN-Casual LSTM Network

Fall Detection of Elderly People Using the Manifold of Positive Semidefinite Matrices

Human Fall Detection Using Passive Infrared Sensors with Low Resolution: A Systematic Review

Contact Info

Product

Resources

About