A Deep Convolutional Neural Network-XGB for Direction and Severity Aware Fall Detection and Activity Recognition

Syed, Abbas; Sierra-Sosa, Daniel; Kumar, Anup; Elmaghraby, Adel

doi:10.3390/s22072547

Cited by 22 publications

(17 citation statements)

References 48 publications

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“…Eventually, the 3D and semantic data can be abridged into simple representation of the difficulties the users have. In Syed et al (2022), a fall detection and activity detection mechanism that did not consider different actions but considered fall detection was presented. After appropriate data augmentation, it can be sent to CNNs for extracting features with an XGB last phase to classify the different output classes.…”

Section: Related Studiesmentioning

confidence: 99%

Piece-Wise Linear Chaotic Mapping-based Beluga Whale Optimization Algorithm-based Indoor Activity Monitoring for Elderly and Visually Impaired Persons

Alzahrani,

Rizwanullah,

Osman

2023

Journal of Disability Research

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Currently, the methods of mobile communications and Internet of Things (IoT) are designed to collect human and environmental data for various intelligent applications and services. Remote monitoring of disabled and elderly people living in smart homes is challenging. Localization and positioning in indoor surroundings need unique solutions. Moreover, positioning remains a crucial feature of any navigation system that assists visually impaired persons (VIPs) in mobility. Other indispensable features of a common indoor navigation system are obstacle avoidance, pathfinding, and abilities for user communication. In recent times, the arrival of smartphones, artificial intelligence, IoT, wearables, etc. makes it possible to devise indoor monitoring systems for smart homecare. Therefore, this study presents an Improved Beluga Whale Optimization Algorithm with fuzzy-based Indoor Activity Monitoring (IBWOA-FIMS) for elderly and VIPs. The presented IBWOA-FIMS technique mainly focused on the identification and classification of indoor activities of elderly and disabled people. To accomplish this, the IBWOA-FIMS technique employs an adaptive neuro fuzzy inference system (ANFIS) model for the indoor monitoring process. In order to improve the monitoring results of the IBWOA-FIMS technique, the IBWOA is used to adjust the parameters related to the ANFIS model. For illustrating the enhanced indoor monitoring results of the IBWOA-FIMS technique, a series of simulations were performed. The simulation values portrayed the betterment of the IBWOA-FIMS technique in terms of different metrics.

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Section: Related Studiesmentioning

confidence: 99%

Piece-Wise Linear Chaotic Mapping-based Beluga Whale Optimization Algorithm-based Indoor Activity Monitoring for Elderly and Visually Impaired Persons

Alzahrani,

Rizwanullah,

Osman

2023

Journal of Disability Research

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“…Of course, the main challenge of fall detection systems is to reduce false positive (FP) warnings and also to reduce false negative (FN) warnings [24][25][26][27]. There are various criteria for evaluating the performance of machine learning algorithms for classification problems; the following parameters can be mentioned [28][29][30].…”

Section: Evaluation Criteriamentioning

confidence: 99%

Fall Detection using Deep Learning Algorithms and Analysis of Wearable Sensor Data by Presenting a New Sampling Method

Hatkeposhti

Tabari

GolsorkhTabariAmiri

2022

IJE

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Fall is one of the most critical health challenges in the community, which can cause severe injuries and even death. The primary purpose of this study is to develop a deep neural network using wearable sensor data to detect falls. Most datasets in this field suffer from the problem of data imbalance so that the instances belonging to the Fall classes are significantly less than the data of the normal class. This study offers a dynamic sampling technique for increasing the balance rate between the samples belonging to fall and normal classes to improve the accuracy of the learning algorithms. The Sisfall dataset was used in which human activity is divided into three categories: normal activity (BKG), moments before the fall (Alert), and role on the ground (Fall). Three deep learning models, CNN, LSTM, and a hybrid model called Conv-LSTM, were implemented on this dataset, and their performance was evaluated. Accordingly, the Conv-LSTM hybrid model presents 96.23%, 98.59%, and 99.38% in the Sensitivity parameter for the BKG, Alert, and Fall classes, respectively. For the accuracy parameter, we have managed to reach 97.12%. In addition, by using noise smoothing and removal techniques, we can hit a 97.83% accuracy rate. The results indicate the proposed model's superiority compared to other similar studies.

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“…Deep learning techniques are becoming increasingly popular in FDS. [38] developed an approach for fall detection that takes into consideration fall direction, severity, and activity identification. Inertial sensor data from the SisFall dataset was utilized to develop the method.…”

Section: Deep Learning Based Fall Detectionmentioning

confidence: 99%

“…3) Deep learning: [38] used SisFall dataset. [39] used SisFall dataset and UP-FallDetection dataset.…”

Section: Comparisonmentioning

confidence: 99%

Fall Detection, Wearable Sensors & Artificial Intelligence: A Short Review

Ishtiaq

Saeed

Khan

et al. 2022

JAREE

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Falls are a major public health concern among the elderly and the number of gadgets designed to detect them has increased significantly in recent years. This document provides a detailed summary of research done on fall detection systems, with comparisons across different types of studies. Its purpose is to be a resource for doctors and engineers who are planning or conducting field research. Following the examination, datasets, limitations, and future imperatives in fall detection were discussed in detail. The quantity of research using context-aware approaches continues to rise, but there is a new trend toward integrating fall detection into smartphones, as well as the use of artificial intelligence in the detection algorithm. Concerns with real-world performance, usability, and reliability are also highlighted.

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A Deep Convolutional Neural Network-XGB for Direction and Severity Aware Fall Detection and Activity Recognition

Cited by 22 publications

References 48 publications

Piece-Wise Linear Chaotic Mapping-based Beluga Whale Optimization Algorithm-based Indoor Activity Monitoring for Elderly and Visually Impaired Persons

Piece-Wise Linear Chaotic Mapping-based Beluga Whale Optimization Algorithm-based Indoor Activity Monitoring for Elderly and Visually Impaired Persons

Fall Detection using Deep Learning Algorithms and Analysis of Wearable Sensor Data by Presenting a New Sampling Method

Fall Detection, Wearable Sensors & Artificial Intelligence: A Short Review

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