A Novel Machine Learning Based Wearable Belt For Fall Detection

Desai, Kimaya; Mane, Pritam; Dsilva, Manish; Zare, Amogh; Shingala, Parth; Ambawade, Dayanand

doi:10.1109/gucon48875.2020.9231114

Cited by 23 publications

(13 citation statements)

References 15 publications

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“…They achieved an accuracy of 97.5%. In [12], researchers proposed a hip-grip belt that contains an IMU sensor combined with an accelerometer and a gyroscope, a GSM module, a micro-controller, and a battery. They combined the data acquired from the three-axis accelerometer and the three-axis gyroscope, and then used the logistic regression classifier to classify the action.…”

Section: A Literature Reviewmentioning

confidence: 99%

Vision-Based Fall Detection Using ST-GCN

Keskes

Noumeir

2021

IEEE Access

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Falls are a growing issue in society and has become a hot topic in the healthcare domain. Falls are more likely to occur to due to age or health problems such as cardiovascular issues and muscle weakness. In this work we focus on fall detection. The aftereffects of falls often lead to the use of prescription pain medications. We are motivated to help prevent suicide attempts by overdose in the Canadian correctional services. Most previous studies were based on hand-crafted features which limit the robustness and generality of the system. We therefore propose a general vision-based system, using Spatial Temporal Graph Convolutional Networks (ST-GCN). This system has proven its efficiency and robustness in the action recognition domain. Contrary to previous works, this model can be applied directly to new data without the need to retrain the model while offering good accuracy. Additionally, with the help of transfer learning we can solve the insufficient data problem. By using three public datasets: the NTU RGB-D dataset, the TST Fall detection dataset v2 and the Fallfree dataset to validate our method, we achieved a 100% accuracy, surpassing the state-of-the-art.

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Section: A Literature Reviewmentioning

confidence: 99%

Vision-Based Fall Detection Using ST-GCN

Keskes

Noumeir

2021

IEEE Access

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“…Nowadays, existing fall detection technology can be roughly divided into three categories [4]: vision-based sensors [8,9], ambient sensors [10,11], and wearable sensors [12,13]. Vision-based sensors obtain motion information by monitoring equipment and extracting human body image inclination [14] or human bone annotations from the obtained video or picture information [15] to detect whether a fall has occurred.…”

Section: Related Workmentioning

confidence: 99%

“…Seyed Amirhossein Mousavi [24] proposed a method of using smartphones and acceleration signals to detect falls by using smartphone sensors and reporting the person's position, with an accuracy rate of 96.33%. 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].…”

Section: Related Workmentioning

confidence: 99%

Fall Detection with CNN-Casual LSTM Network

Wang

Zhan

et al. 2021

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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%.

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“…Many researchers have focused on human fall detection and have carried out a great deal of work. In the past decade, a large number of fall detection programs have been proposed, which can be divided into the following three categories: video-based [ 3 , 4 , 5 , 6 , 7 ], ambient sensor-based [ 8 , 9 , 10 , 11 ] and wearable sensor-based [ 12 , 13 , 14 , 15 , 16 , 17 , 18 ], as shown in Figure 1 . The sensitivity and the specificity of the video-based methods can reach 97% and 99%, respectively [ 4 ], indicating that they can accurately identify the occurrence of falls.…”

Section: Introductionmentioning

confidence: 99%

A Class-Imbalanced Deep Learning Fall Detection Algorithm Using Wearable Sensors

Zhang

Wang

2021

Sensors

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Falling represents one of the most serious health risks for elderly people; it may cause irreversible injuries if the individual cannot obtain timely treatment after the fall happens. Therefore, timely and accurate fall detection algorithm research is extremely important. Recently, a number of researchers have focused on fall detection and made many achievements, and most of the relevant algorithm studies are based on ideal class-balanced datasets. However, in real-life applications, the possibilities of Activities of Daily Life (ADL) and fall events are different, so the data collected by wearable sensors suffers from class imbalance. The previously developed algorithms perform poorly on class-imbalanced data. In order to solve this problem, this paper proposes an algorithm that can effectively distinguish falls from a large amount of ADL signals. Compared with the state-of-the-art fall detection algorithms, the proposed method can achieve the highest score in multiple evaluation methods, with a sensitivity of 99.33%, a specificity of 91.86%, an F-Score of 98.44% and an AUC of 98.35%. The results prove that the proposed algorithm is effective on class-imbalanced data and more suitable for real-life application compared to previous works.

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A Novel Machine Learning Based Wearable Belt For Fall Detection

Cited by 23 publications

References 15 publications

Vision-Based Fall Detection Using ST-GCN

Vision-Based Fall Detection Using ST-GCN

Fall Detection with CNN-Casual LSTM Network

A Class-Imbalanced Deep Learning Fall Detection Algorithm Using Wearable Sensors

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