Journal articles: 'Automatic fall detection'

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Asma Abdallah Nasser Al-Risi, Shamsa Salim Mattar Albadi, Shima Hamdan Said Almaamari, Saleem Raja Abdul Samad, and Pradeepa Ganesan. "Automated Fall Detection for Disabled Individuals Using Mobile Phone Sensors and Machine Learning: A Survey." International Journal of Data Informatics and Intelligent Computing 3, no. 2 (2024): 27–33. http://dx.doi.org/10.59461/ijdiic.v3i2.106.

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Fall risks to health and safety are especially dangerous for those with impairments. An automated fall detection system is necessary, especially in medical and senior care. The elderly and individuals with impairments are particularly susceptible to falls, which frequently result in severe injuries and complications, thereby presenting a considerable threat to their overall health. The early discovery and response to a fall incidence can reduce immobilization and consequent health complications, saving lives. Automatic fall detection systems quickly and reliably indicate falls and dispatch medical or emergency assistance. Researchers have introduced various automatic fall detection methods using machines or deep learning. Most fall detection systems depend on wearable or stationary sensors, which restricts the user's mobility and accessibility. Conversely, mobile sensor-based fall detection leverages the widespread presence of smartphones by obtaining motion information via their integrated accelerometers and gyroscopes. Our primary objective is to develop a reliable fall detection method using a mobile phone sensor and machine learning. This paper examines several methods employed in the identification of falls and emphasizes the significance of utilizing mobile phone sensors in the process of fall detection. It also discusses recent research in this domain and highlights research challenges. This could potentially foster further innovation in the field.

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H, Mr Vinay Kumar, S. Hamsaveni, Vijay Kumar, Mohammed Umer .K, and Syeda Sania Quadri. "Automatic Fall Detection and Heartbeat Monitoring for Elderly." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (2023): 2883–85. http://dx.doi.org/10.22214/ijraset.2023.52245.

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Abstract: Falls and fall-related injuries are major incidents, especially for elderly people, which often mark the onset of major deterioration of health. More than one-third of home-dwelling people aged 65 or aboveand two-thirds of those in residential care fall once or more each year. Reliable fall detection, as well as prevention, is an important research topic for monitoring elderly living alone in residential or hospital units. The aim of this study is to review the existing fall detection systems and some of the key research challenges faced by the research community in this field. We categorize the existing platforms into two groups: wearable and ambient devices; the classification methods are divided into rule-based and machine learning techniques. The relative merit and potential drawbacks are discussed, and we also outline some of the outstanding research challenges that emerging new platforms need to address.

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Alizadeh, Jalal, Martin Bogdan, Joseph Classen, and Christopher Fricke. "Support Vector Machine Classifiers Show High Generalizability in Automatic Fall Detection in Older Adults." Sensors 21, no. 21 (2021): 7166. http://dx.doi.org/10.3390/s21217166.

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Falls are a major cause of morbidity and mortality in neurological disorders. Technical means of detecting falls are of high interest as they enable rapid notification of caregivers and emergency services. Such approaches must reliably differentiate between normal daily activities and fall events. A promising technique might be based on the classification of movements based on accelerometer signals by machine-learning algorithms, but the generalizability of classifiers trained on laboratory data to real-world datasets is a common issue. Here, three machine-learning algorithms including Support Vector Machine (SVM), k-Nearest Neighbors (kNN), and Random Forest (RF) were trained to detect fall events. We used a dataset containing intentional falls (SisFall) to train the classifier and validated the approach on a different dataset which included real-world accidental fall events of elderly people (FARSEEING). The results suggested that the linear SVM was the most suitable classifier in this cross-dataset validation approach and reliably distinguished a fall event from normal everyday activity at an accuracy of 93% and similarly high sensitivity and specificity. Thus, classifiers based on linear SVM might be useful for automatic fall detection in real-world applications.

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Daher, Mohamad, Maan El Badaoui El Najjar, and Mohamad Khalil. "Automatic Fall Detection System using Sensing Floors." International Journal of Computing and Information Sciences 12, no. 1 (2016): 75–82. http://dx.doi.org/10.21700/ijcis.2016.110.

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Liu, Hong, and Changling Zuo. "An Improved Algorithm of Automatic Fall Detection." AASRI Procedia 1 (2012): 353–58. http://dx.doi.org/10.1016/j.aasri.2012.06.054.

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Lim, Dongha, Chulho Park, Nam Ho Kim, Sang-Hoon Kim, and Yun Seop Yu. "Fall-Detection Algorithm Using 3-Axis Acceleration: Combination with Simple Threshold and Hidden Markov Model." Journal of Applied Mathematics 2014 (2014): 1–8. http://dx.doi.org/10.1155/2014/896030.

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Falls are a serious medical and social problem among the elderly. This has led to the development of automatic fall-detection systems. To detect falls, a fall-detection algorithm that combines a simple threshold method and hidden Markov model (HMM) using 3-axis acceleration is proposed. To apply the proposed fall-detection algorithm and detect falls, a wearable fall-detection device has been designed and produced. Several fall-feature parameters of 3-axis acceleration are introduced and applied to a simple threshold method. Possible falls are chosen through the simple threshold and are applied to two types of HMM to distinguish between a fall and an activity of daily living (ADL). The results using the simple threshold, HMM, and combination of the simple method and HMM were compared and analyzed. The combination of the simple threshold method and HMM reduced the complexity of the hardware and the proposed algorithm exhibited higher accuracy than that of the simple threshold method.

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Anishchenko, Lesya, Andrey Zhuravlev, and Margarita Chizh. "Fall Detection Using Multiple Bioradars and Convolutional Neural Networks." Sensors 19, no. 24 (2019): 5569. http://dx.doi.org/10.3390/s19245569.

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A lack of effective non-contact methods for automatic fall detection, which may result in the development of health and life-threatening conditions, is a great problem of modern medicine, and in particular, geriatrics. The purpose of the present work was to investigate the advantages of utilizing a multi-bioradar system in the accuracy of remote fall detection. The proposed concept combined usage of wavelet transform and deep learning to detect fall episodes. The continuous wavelet transform was used to get a time-frequency representation of the bio-radar signal and use it as input data for a pre-trained convolutional neural network AlexNet adapted to solve the problem of detecting falls. Processing of the experimental results showed that the designed multi-bioradar system can be used as a simple and view-independent approach implementing a non-contact fall detection method with an accuracy and F1-score of 99%.

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Feldwieser, Florian, Michael Marchollek, Markus Meis, Matthias Gietzelt, and Elisabeth Steinhagen-Thiessen. "Acceptance of seniors towards automatic in home fall detection devices." Journal of Assistive Technologies 10, no. 4 (2016): 178–86. http://dx.doi.org/10.1108/jat-07-2015-0021.

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Purpose Senior citizen falls are one of the highest-cost factors of healthcare within this population group. Various approaches for automatic fall detection exist. However, little is known about the seniors’ acceptance of these systems. The purpose of this paper is to investigate the acceptance of automatic fall detection devices as well as the technological commitment and the health status in community-dwelling adults with a predefined risk of falling. Design/methodology/approach Seniors with a risk of falling were equipped with either an accelerometer or an accelerometer with an additional visual and optical fall detection system in a sub-group of the study population for a period of eight weeks. Pre- and post-study questionnaires were used to assess attitudes and acceptance toward technology. Findings In total, 14 subjects with a mean age of 75.1 years completed the study. Acceptance toward all sensors was high and subjects were confident in their ability to handle technology. Medical assessments showed only very mild physical and no mental impairments. Measures that assured subjects privacy protection were welcomed. Sensor technology should be as unobtrusive as possible. Originality/value Privacy protection and uncomplicated use of the fall detection equipment led to high acceptance in seniors with high-technical commitment and good health status. Issues to further improve acceptance could be identified. Future research on different populations is necessary.

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Liu, Yen-Hung, Patrick C. K. Hung, Farkhund Iqbal, and Benjamin C. M. Fung. "Automatic Fall Risk Detection Based on Imbalanced Data." IEEE Access 9 (2021): 163594–611. http://dx.doi.org/10.1109/access.2021.3133297.

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Yun Li, K. C. Ho, and M. Popescu. "A Microphone Array System for Automatic Fall Detection." IEEE Transactions on Biomedical Engineering 59, no. 5 (2012): 1291–301. http://dx.doi.org/10.1109/tbme.2012.2186449.

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Ismail, Mohamed Maher Ben, and Ouiem Bchir. "Automatic Fall Detection Using Membership Based Histogram Descriptors." Journal of Computer Science and Technology 32, no. 2 (2017): 356–67. http://dx.doi.org/10.1007/s11390-017-1725-z.

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Fan, Xinnan, Qian Gong, Rong Fan, et al. "Substation Personnel Fall Detection Based on Improved YOLOX." Electronics 12, no. 20 (2023): 4328. http://dx.doi.org/10.3390/electronics12204328.

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With the continuous promotion of smart substations, staff fall detection has become a key issue in automatic detection of substations. The injuries and safety hazards caused by falls among substation personnel are numerous. If a timely response can be made in the event of a fall, the injuries caused by falls can be reduced. In order to address the issues of low accuracy and poor real-time performance in detecting human falls in complex substation scenarios, this paper proposes an improved algorithm based on YOLOX. A customized feature extraction module is introduced to the YOLOX feature fusion network to extract diverse multiscale features. A recursive gated convolutional module is added to the head to enhance the expressive power of the features. Meanwhile, the SIoU(Soft Intersection over Union) loss function is utilized to provide more accurate position information for bounding boxes, thereby improving the model accuracy. Experimental results show that the improved algorithm achieves an mAP value of 78.45%, which is a 1.31% improvement over the original YOLOX. Compared to other similar algorithms, the proposed algorithm achieves high accuracy prediction of human falls with fewer parameters, demonstrating its effectiveness.

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Zanaj, Elma, Deivis Disha, Susanna Spinsante, and Ennio Gambi. "A Wearable Fall Detection System based on LoRa LPWAN Technology." Journal of communications software and systems 16, no. 3 (2020): 232–42. http://dx.doi.org/10.24138/jcomss.v16i3.1039.

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The fall problem affects approximately one third of people aged over 65 years. Falls and fall-related injuries are one of the major causes of morbidity and mortality in the elderly population. Since many years, research activities have been targeted towards the development of technological solutions for the automatic detection and notification of falls. Among them, wearable based systems offer the advantage of being available ideally everywhere and cost-effective in terms of economy and computational burden. However, their use poses different challenges, from acceptability to battery usage. The choice of the communication technology, in particular, plays a fundamental role in the realization of a suitable solution, able to meet the target users’ needs. In this paper, we present a fall detection system, based on a pair of instrumented shoes. They communicate the alarming events to a supervising system through the LoRa LPWAN technology, without the need of a portable gateway. Experimental results demonstrate the effectiveness of the chosen communication technology and fall detection reliability.

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Merrouche, Fairouz, and Nadia Baha. "Fall Detection Depth-Based Using Tilt Angle and Shape Deformation." International Journal of Computer Vision and Image Processing 8, no. 4 (2018): 26–40. http://dx.doi.org/10.4018/ijcvip.2018100103.

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The population of elderly people is in growth. Falls risk their life, to disabilities, and to fears. Automatic fall detection systems provide them secure living; helping them to be independent at home. Computer vision offers efficient systems over many developed systems. In this article, the authors propose a new vision-based fall detection using depth camera. It combines human shape analysis, centroid detection and motion where it exploits the 3D information provided by a Kinect to compute the tilt angle to discriminate falls. Experimental tests were done with SDUFall dataset that contains 20 subjects performing five daily activities and falls, demonstrate the efficiency of the proposed system, and show that our method is promising achieving satisfactory results up to 84.66%.

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Li, Ruisheng. "A Review of Fall Detection Research Based on Deep Learning." Applied and Computational Engineering 115, no. 1 (2024): 93–102. https://doi.org/10.54254/2755-2721/2025.18481.

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In recent years, with the intensification of societal aging, falls have emerged as one of the primary health risks threatening the well-being of the elderly, making the effective detection of fall events an urgent problem to address. Fall detection technology based on deep learning has increasingly become a focal point of research due to its powerful automatic feature extraction capabilities. This paper systematically reviews the recent research in fall detection, categorizing the technologies into two main types according to the devices used for data collection: image-based and non-image-based detection methods. The study highlights recent advances in fall detection using Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and Temporal Convolutional Networks (T-CNN) frameworks, examining the application and advantages of these methods in processing various types of data, such as video sequences and sensor data. Through an in-depth analysis of the operational mechanisms of multiple deep learning architectures and their performance on different datasets, this paper elucidates the latest advancements and challenges facing current fall detection technologies. Additionally, commonly used fall detection datasets are introduced, including their data scales and application contexts. Finally, this paper provides a summary of recent progress in fall detection techniques, outlines future directions, and presents recommendations for advancement across several dimensions.

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Alves, José, Joana Silva, Eduardo Grifo, Carlos Resende, and Inês Sousa. "Wearable Embedded Intelligence for Detection of Falls Independently of on-Body Location." Sensors 19, no. 11 (2019): 2426. http://dx.doi.org/10.3390/s19112426.

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Falls are one of the most common problems in the elderly population. Therefore, each year more solutions for automatic fall detection are emerging. This paper proposes a single accelerometer algorithm for wearable devices that works for three different body locations: chest, waist and pocket, without a calibration step being required. This algorithm is able to be fully executed on a wearable device and no external devices are necessary for data processing. Additionally, a study of the accelerometer sampling rate, that allows the algorithm to achieve a better performance, was performed. The algorithm was validated with a continuous dataset with daily living activities and 272 simulated falls. Considering the trade-off between sensitivity and the number of false alarms the most suitable sampling rate found was 50 Hz. The proposed algorithm was able to achieve a trade-off of no false alarms and 89.5% of fall detection rate when wearing the sensor on the user’s waist with a medium sensitivity level of the algorithm. In conclusion, this paper presents a reliable solution for automatic fall detection that can be adapted to different usages and conditions, since it can be used in different body locations and its sensitivity can be adapted to different subjects according to their physical activity level.

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Riquelme, Fabián, Cristina Espinoza, Tomás Rodenas, Jean-Gabriel Minonzio, and Carla Taramasco. "eHomeSeniors Dataset: An Infrared Thermal Sensor Dataset for Automatic Fall Detection Research." Sensors 19, no. 20 (2019): 4565. http://dx.doi.org/10.3390/s19204565.

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Automatic fall detection is a very active research area, which has grown explosively since the 2010s, especially focused on elderly care. Rapid detection of falls favors early awareness from the injured person, reducing a series of negative consequences in the health of the elderly. Currently, there are several fall detection systems (FDSs), mostly based on predictive and machine-learning approaches. These algorithms are based on different data sources, such as wearable devices, ambient-based sensors, or vision/camera-based approaches. While wearable devices like inertial measurement units (IMUs) and smartphones entail a dependence on their use, most image-based devices like Kinect sensors generate video recordings, which may affect the privacy of the user. Regardless of the device used, most of these FDSs have been tested only in controlled laboratory environments, and there are still no mass commercial FDS. The latter is partly due to the impossibility of counting, for ethical reasons, with datasets generated by falls of real older adults. All public datasets generated in laboratory are performed by young people, without considering the differences in acceleration and falling features of older adults. Given the above, this article presents the eHomeSeniors dataset, a new public dataset which is innovative in at least three aspects: first, it collects data from two different privacy-friendly infrared thermal sensors; second, it is constructed by two types of volunteers: normal young people (as usual) and performing artists, with the latter group assisted by a physiotherapist to emulate the real fall conditions of older adults; and third, the types of falls selected are the result of a thorough literature review.

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Hader, Ghada Khaled, Mohamed Maher Ben Ismail, and Ouiem Bchir. "Automatic fall detection using region-based convolutional neural network." International Journal of Injury Control and Safety Promotion 27, no. 4 (2020): 546–57. http://dx.doi.org/10.1080/17457300.2020.1819341.

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Taramasco, Carla, Miguel Pineiro, Pablo Ormeño-Arriagada, Diego Robles, and David Araya. "Multimodal dataset for sensor fusion in fall detection." PeerJ 13 (April 1, 2025): e19004. https://doi.org/10.7717/peerj.19004.

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The necessity for effective automatic fall detection mechanisms in older adults is driven by the growing demographic of elderly individuals who are at substantial health risk from falls, particularly when residing alone. Despite the existence of numerous fall detection systems (FDSs) that utilize machine learning and predictive modeling, accurately distinguishing between everyday activities and genuine falls continues to pose significant challenges, exacerbated by the varied nature of residential settings. Adaptable solutions are essential to cater to the diverse conditions under which falls occur. In this context, sensor fusion emerges as a promising solution, harnessing the unique physical properties of falls. The success of developing effective detection algorithms is dependent on the availability of comprehensive datasets that integrate data from multiple synchronized sensors. Our research introduces a novel multisensor dataset designed to support the creation and evaluation of advanced multisensor fall detection algorithms. This dataset was compiled from simulations of ten different fall types by ten participants, ensuring a wide array of scenarios. Data were collected using four types of sensors: a mobile phone equipped with a single-channel, three-dimensional accelerometer; a far infrared (FIR) thermal camera; an $8×8$ LIDAR; and a 60–64 GHz radar. These sensors were selected for their combined effectiveness in capturing detailed aspects of fall events while mitigating privacy issues linked to visual recordings. Characterization of the dataset was undertaken using two key metrics: the instantaneous norm of the signal and the temporal difference between consecutive frames. This analysis highlights the distinct variations between fall and non-fall events across different sensors and signal characteristics. Through the provision of this dataset, our objective is to facilitate the development of sensor fusion algorithms that surpass the accuracy and reliability of traditional single-sensor FDSs.

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Vavoulas, George, Matthew Pediaditis, Charikleia Chatzaki, Emmanouil G. Spanakis, and Manolis Tsiknakis. "The MobiFall Dataset." International Journal of Monitoring and Surveillance Technologies Research 2, no. 1 (2014): 44–56. http://dx.doi.org/10.4018/ijmstr.2014010103.

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Fall detection is receiving significant attention in the field of preventive medicine, wellness management and assisted living, especially for the elderly. As a result, several fall detection systems are reported in the research literature or exist as commercial systems. Most of them use accelerometers and/ or gyroscopes attached on a person's body as the primary signal sources. These systems use either discrete sensors as part of a product designed specifically for this task or sensors that are embedded in mobile devices such as smartphones. The latter approach has the advantage of offering well tested and widely available communication services, e.g. for calling emergency when necessary. Nevertheless, automatic fall detection continues to present significant challenges, with the recognition of the type of fall being the most critical. The aim of this work is to introduce a human fall and activity dataset to be used in testing new detection methods, as well as performing objective comparisons between different reported algorithms for fall detection and activity recognition, based on inertial-sensor data from smartphones. The dataset contains signals recorded from the accelerometer and gyroscope sensors of a latest technology smartphone for four different types of falls and nine different activities of daily living. Utilizing this dataset, the results of an elaborate evaluation of machine learning-based fall detection and fall classification are presented and discussed in detail.

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Šeketa, Goran, Lovro Pavlaković, Dominik Džaja, Igor Lacković, and Ratko Magjarević. "Event-Centered Data Segmentation in Accelerometer-Based Fall Detection Algorithms." Sensors 21, no. 13 (2021): 4335. http://dx.doi.org/10.3390/s21134335.

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Automatic fall detection systems ensure that elderly people get prompt assistance after experiencing a fall. Fall detection systems based on accelerometer measurements are widely used because of their portability and low cost. However, the ability of these systems to differentiate falls from Activities of Daily Living (ADL) is still not acceptable for everyday usage at a large scale. More work is still needed to raise the performance of these systems. In our research, we explored an essential but often neglected part of accelerometer-based fall detection systems—data segmentation. The aim of our work was to explore how different configurations of windows for data segmentation affect detection accuracy of a fall detection system and to find the best-performing configuration. For this purpose, we designed a testing environment for fall detection based on a Support Vector Machine (SVM) classifier and evaluated the influence of the number and duration of segmentation windows on the overall detection accuracy. Thereby, an event-centered approach for data segmentation was used, where windows are set relative to a potential fall event detected in the input data. Fall and ADL data records from three publicly available datasets were utilized for the test. We found that a configuration of three sequential windows (pre-impact, impact, and post-impact) provided the highest detection accuracy on all three datasets. The best results were obtained when either a 0.5 s or a 1 s long impact window was used, combined with pre- and post-impact windows of 3.5 s or 3.75 s.

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Alanazi, Thamer, and Ghulam Muhammad. "Human Fall Detection Using 3D Multi-Stream Convolutional Neural Networks with Fusion." Diagnostics 12, no. 12 (2022): 3060. http://dx.doi.org/10.3390/diagnostics12123060.

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Human falls, especially for elderly people, can cause serious injuries that might lead to permanent disability. Approximately 20–30% of the aged people in the United States who experienced fall accidents suffer from head trauma, injuries, or bruises. Fall detection is becoming an important public healthcare problem. Timely and accurate fall incident detection could enable the instant delivery of medical services to the injured. New advances in vision-based technologies, including deep learning, have shown significant results in action recognition, where some focus on the detection of fall actions. In this paper, we propose an automatic human fall detection system using multi-stream convolutional neural networks with fusion. The system is based on a multi-level image-fusion approach of every 16 frames of an input video to highlight movement differences within this range. This results of four consecutive preprocessed images are fed to a new proposed and efficient lightweight multi-stream CNN model that is based on a four-branch architecture (4S-3DCNN) that classifies whether there is an incident of a human fall. The evaluation included the use of more than 6392 generated sequences from the Le2i fall detection dataset, which is a publicly available fall video dataset. The proposed method, using three-fold cross-validation to validate generalization and susceptibility to overfitting, achieved a 99.03%, 99.00%, 99.68%, and 99.00% accuracy, sensitivity, specificity, and precision, respectively. The experimental results prove that the proposed model outperforms state-of-the-art models, including GoogleNet, SqueezeNet, ResNet18, and DarkNet19, for fall incident detection.

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Lee, Yongkuk, Suresh Pokharel, Asra Al Muslim, Dukka KC, Kyoung Hag Lee, and Woon-Hong Yeo. "Experimental Study: Deep Learning-Based Fall Monitoring among Older Adults with Skin-Wearable Electronics." Sensors 23, no. 8 (2023): 3983. http://dx.doi.org/10.3390/s23083983.

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Older adults are more vulnerable to falling due to normal changes due to aging, and their falls are a serious medical risk with high healthcare and societal costs. However, there is a lack of automatic fall detection systems for older adults. This paper reports (1) a wireless, flexible, skin-wearable electronic device for both accurate motion sensing and user comfort, and (2) a deep learning-based classification algorithm for reliable fall detection of older adults. The cost-effective skin-wearable motion monitoring device is designed and fabricated using thin copper films. It includes a six-axis motion sensor and is directly laminated on the skin without adhesives for the collection of accurate motion data. To study accurate fall detection using the proposed device, different deep learning models, body locations for the device placement, and input datasets are investigated using motion data based on various human activities. Our results indicate the optimal location to place the device is the chest, achieving accuracy of more than 98% for falls with motion data from older adults. Moreover, our results suggest a large motion dataset directly collected from older adults is essential to improve the accuracy of fall detection for the older adult population.

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Barkunan, S. R., V. Bhanumathi, and V. Balakrishnan. "Automatic irrigation system with rain fall detection in agricultural field." Measurement 156 (May 2020): 107552. http://dx.doi.org/10.1016/j.measurement.2020.107552.

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Ismail, Mohamed Maher Ben, and Ouiem Bchir. "Erratum to: Automatic Fall Detection Using Membership Based Histogram Descriptors." Journal of Computer Science and Technology 33, no. 1 (2018): 237. http://dx.doi.org/10.1007/s11390-018-1815-6.

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P, Nishanth. "Machine Learning based Human Fall Detection System." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (2021): 2677–82. http://dx.doi.org/10.22214/ijraset.2021.35394.

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Falls have become one of the reasons for death. It is common among the elderly. According to World Health Organization (WHO), 3 out of 10 living alone elderly people of age 65 and more tend to fall. This rate may get higher in the upcoming years. In recent years, the safety of elderly residents alone has received increased attention in a number of countries. The fall detection system based on the wearable sensors has made its debut in response to the early indicator of detecting the fall and the usage of the IoT technology, but it has some drawbacks, including high infiltration, low accuracy, poor reliability. This work describes a fall detection that does not reliant on wearable sensors and is related on machine learning and image analysing in Python. The camera's high-frequency pictures are sent to the network, which uses the Convolutional Neural Network technique to identify the main points of the human. The Support Vector Machine technique uses the data output from the feature extraction to classify the fall. Relatives will be notified via mobile message. Rather than modelling individual activities, we use both motion and context information to recognize activities in a scene. This is based on the notion that actions that are spatially and temporally connected rarely occur alone and might serve as background for one another. We propose a hierarchical representation of action segments and activities using a two-layer random field model. The model allows for the simultaneous integration of motion and a variety of context features at multiple levels, as well as the automatic learning of statistics that represent the patterns of the features.

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Fu, Fangping. "A video-based fall detection using 3D sparse convolutional neural network in elderly care services." Machine Graphics and Vision 34, no. 1 (2025): 53–74. https://doi.org/10.22630/mgv.2025.34.1.3.

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Falls in the elderly have become one of the major risks for the growing elderly population. Therefore, the application of automatic fall detection system for the elderly is particularly important. In recent years, a large number of deep learning methods (such as CNN) have been applied to such research. This paper proposed a sparse convolution method 3D Sparse Convolutions and the corresponding 3D Sparse Convolutional Neural Network (3D-SCNN), which can achieve faster convolution at the approximate accuracy, thereby reducing computational complexity while maintaining high accuracy in video analysis and fall detection task. Additionally, the preprocessing stage involves a dynamic key frame selection method, using the jitter buffers to adjust frame selection based on current network conditions and buffer state. To ensure feature continuity, overlapping cubes of selected frames are intentionally employed, with dynamic resizing to adapt to network dynamics and buffer states. Experiments are conducted on Multi-camera fall dataset and UR fall dataset, and the results show that its accuracy exceeds the three compared methods, and outperforms the traditional 3D-CNN methods in both accuracy and losses.

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Geng, Peng, Hui Xie, Houqin Shi, Rui Chen, and Ying Tong. "Pedestrian Fall Event Detection in Complex Scenes Based on Attention-Guided Neural Network." Mathematical Problems in Engineering 2022 (April 28, 2022): 1–10. http://dx.doi.org/10.1155/2022/4110246.

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To address automatic detection of pedestrian fall events and provide feedback in emergency situations, this paper proposes an attention-guided real-time and robust method for pedestrian detection in complex scenes. First, the YOLOv3 network is used to effectively detect pedestrians in the videos. Then, an improved DeepSort algorithm is used to track by detection. After tracking, the authors extract effective features from the tracked bounding box, use the output of the last convolutional layer, and introduce the attention weight factor into the tracking module for final fall event prediction. Finally, the authors use the sliding window for storing feature maps and SVM classifier to redetect fall events. The experimental results on the CityPersons dataset, Montreal fall dataset, and self-built dataset indicate that this approach has good performance in complex scenes. The pedestrian detection rate is 87.05%, the accuracy of fall event detection reaches 98.55%, and the delay is within 120 ms.

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Wu, Xiaodan, Yumeng Zheng, Chao-Hsien Chu, Lingyu Cheng, and Jungyoon Kim. "Applying deep learning technology for automatic fall detection using mobile sensors." Biomedical Signal Processing and Control 72 (February 2022): 103355. http://dx.doi.org/10.1016/j.bspc.2021.103355.

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Liu, Liang, Mihail Popescu, Marjorie Skubic, Marilyn Rantz, and Paul Cuddihy. "An automatic in-home fall detection system using Doppler radar signatures." Journal of Ambient Intelligence and Smart Environments 8, no. 4 (2016): 453–66. http://dx.doi.org/10.3233/ais-160388.

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Liu, Jian, and Thurmon E. Lockhart. "Automatic individual calibration in fall detection – an integrative ambulatory measurement framework." Computer Methods in Biomechanics and Biomedical Engineering 16, no. 5 (2013): 504–10. http://dx.doi.org/10.1080/10255842.2011.627329.

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Casilari, Eduardo, Jose A. Santoyo-Ramón, and Jose M. Cano-García. "UMAFall: A Multisensor Dataset for the Research on Automatic Fall Detection." Procedia Computer Science 110 (2017): 32–39. http://dx.doi.org/10.1016/j.procs.2017.06.110.

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Dhakne, Dr Amol, Charushila Kakade, Nilesh Katkar, Sayali Kamble, and Rupali Bhor. "Automatic Covid Detection using CT Images." International Journal for Research in Applied Science and Engineering Technology 10, no. 5 (2022): 4515–21. http://dx.doi.org/10.22214/ijraset.2022.43450.

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Abstract: COVID-19, the disease caused by the novel corona virus, can cause lung complications such as pneumonia and, in the most severe cases, acute respiratory distress syndrome, or ARDS. Another possible complication of COVID-19 is sepsis, which can cause long-term damage to the lungs and other organs. COVID-19 virus is primarily transmitted through droplets produced when an infected person coughs, sneezes, or exhales. These droplets are too heavy to float in the air and quickly fall to the ground or other surfaces. As everyone is aware, the corona virus disease 2019 (COVID-19) spread throughout the world in early 2020, causing the world to face an existential health crisis. Thus, automating the detection of lung infections from computed tomography (CT) images has the potential to supplement the traditional healthcare strategy for combating COVID-19. However, segmenting infected regions from CT slices is difficult due to high variation in infection characteristics and low-intensity contrast between infections and normal tissues. Furthermore, collecting a large amount of data in a short period of time is impractical. Our proposed solution will analyse a CT image of the lung and detect the infected portion of the lung, as well as the percentage of the affected portion. The system will identify the infection severity and will help patients to take essential measures. Keywords: Preprocessing, Segmentation, Feature Ex-traction, Classification, CT Images, Image Preprocessing.

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Inturi, Anitha Rani, Vazhora Malayil Manikandan, Mahamkali Naveen Kumar, Shuihua Wang, and Yudong Zhang. "Synergistic Integration of Skeletal Kinematic Features for Vision-Based Fall Detection." Sensors 23, no. 14 (2023): 6283. http://dx.doi.org/10.3390/s23146283.

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According to the World Health Organisation, falling is a major health problem with potentially fatal implications. Each year, thousands of people die as a result of falls, with seniors making up 80% of these fatalities. The automatic detection of falls may reduce the severity of the consequences. Our study focuses on developing a vision-based fall detection system. Our work proposes a new feature descriptor that results in a new fall detection framework. The body geometry of the subject is analyzed and patterns that help to distinguish falls from non-fall activities are identified in our proposed method. An AlphaPose network is employed to identify 17 keypoints on the human skeleton. Thirteen keypoints are used in our study, and we compute two additional keypoints. These 15 keypoints are divided into five segments, each of which consists of a group of three non-collinear points. These five segments represent the left hand, right hand, left leg, right leg and craniocaudal section. A novel feature descriptor is generated by extracting the distances from the segmented parts, angles within the segmented parts and the angle of inclination for every segmented part. As a result, we may extract three features from each segment, giving us 15 features per frame that preserve spatial information. To capture temporal dynamics, the extracted spatial features are arranged in the temporal sequence. As a result, the feature descriptor in the proposed approach preserves the spatio-temporal dynamics. Thus, a feature descriptor of size [m×15] is formed where m is the number of frames. To recognize fall patterns, machine learning approaches such as decision trees, random forests, and gradient boost are applied to the feature descriptor. Our system was evaluated on the UPfall dataset, which is a benchmark dataset. It has shown very good performance compared to the state-of-the-art approaches.

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Liang, Tingxuan, Ruizhi Liu, Lei Yang, Yue Lin, C. J. Richard Shi, and Hongtao Xu. "Fall Detection System Based on Point Cloud Enhancement Model for 24 GHz FMCW Radar." Sensors 24, no. 2 (2024): 648. http://dx.doi.org/10.3390/s24020648.

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Automatic fall detection plays a significant role in monitoring the health of senior citizens. In particular, millimeter-wave radar sensors are relevant for human pose recognition in an indoor environment due to their advantages of privacy protection, low hardware cost, and wide range of working conditions. However, low-quality point clouds from 4D radar diminish the reliability of fall detection. To improve the detection accuracy, conventional methods utilize more costly hardware. In this study, we propose a model that can provide high-quality three-dimensional point cloud images of the human body at a low cost. To improve the accuracy and effectiveness of fall detection, a system that extracts distribution features through small radar antenna arrays is developed. The proposed system achieved 99.1% and 98.9% accuracy on test datasets pertaining to new subjects and new environments, respectively.

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Zhang, Jinxi, Zhen Li, Yu Liu, et al. "An Effective Deep Learning Framework for Fall Detection: Model Development and Study Design." Journal of Medical Internet Research 26 (August 5, 2024): e56750. http://dx.doi.org/10.2196/56750.

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Background Fall detection is of great significance in safeguarding human health. By monitoring the motion data, a fall detection system (FDS) can detect a fall accident. Recently, wearable sensors–based FDSs have become the mainstream of research, which can be categorized into threshold-based FDSs using experience, machine learning–based FDSs using manual feature extraction, and deep learning (DL)–based FDSs using automatic feature extraction. However, most FDSs focus on the global information of sensor data, neglecting the fact that different segments of the data contribute variably to fall detection. This shortcoming makes it challenging for FDSs to accurately distinguish between similar human motion patterns of actual falls and fall-like actions, leading to a decrease in detection accuracy. Objective This study aims to develop and validate a DL framework to accurately detect falls using acceleration and gyroscope data from wearable sensors. We aim to explore the essential contributing features extracted from sensor data to distinguish falls from activities of daily life. The significance of this study lies in reforming the FDS by designing a weighted feature representation using DL methods to effectively differentiate between fall events and fall-like activities. Methods Based on the 3-axis acceleration and gyroscope data, we proposed a new DL architecture, the dual-stream convolutional neural network self-attention (DSCS) model. Unlike previous studies, the used architecture can extract global feature information from acceleration and gyroscope data. Additionally, we incorporated a self-attention module to assign different weights to the original feature vector, enabling the model to learn the contribution effect of the sensor data and enhance classification accuracy. The proposed model was trained and tested on 2 public data sets: SisFall and MobiFall. In addition, 10 participants were recruited to carry out practical validation of the DSCS model. A total of 1700 trials were performed to test the generalization ability of the model. Results The fall detection accuracy of the DSCS model was 99.32% (recall=99.15%; precision=98.58%) and 99.65% (recall=100%; precision=98.39%) on the test sets of SisFall and MobiFall, respectively. In the ablation experiment, we compared the DSCS model with state-of-the-art machine learning and DL models. On the SisFall data set, the DSCS model achieved the second-best accuracy; on the MobiFall data set, the DSCS model achieved the best accuracy, recall, and precision. In practical validation, the accuracy of the DSCS model was 96.41% (recall=95.12%; specificity=97.55%). Conclusions This study demonstrates that the DSCS model can significantly improve the accuracy of fall detection on 2 publicly available data sets and performs robustly in practical validation.

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Ozcan, Koray, Anvith Katte Mahabalagiri, Mauricio Casares, and Senem Velipasalar. "Automatic Fall Detection and Activity Classification by a Wearable Embedded Smart Camera." IEEE Journal on Emerging and Selected Topics in Circuits and Systems 3, no. 2 (2013): 125–36. http://dx.doi.org/10.1109/jetcas.2013.2256832.

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Youssfi Alaoui, Abdessamad, Youness Tabii, Rachid Oulad Haj Thami, Mohamed Daoudi, Stefano Berretti, and Pietro Pala. "Fall Detection of Elderly People Using the Manifold of Positive Semidefinite Matrices." Journal of Imaging 7, no. 7 (2021): 109. http://dx.doi.org/10.3390/jimaging7070109.

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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.

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Núñez-Marcos, Adrián, Gorka Azkune, and Ignacio Arganda-Carreras. "Vision-Based Fall Detection with Convolutional Neural Networks." Wireless Communications and Mobile Computing 2017 (2017): 1–16. http://dx.doi.org/10.1155/2017/9474806.

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One of the biggest challenges in modern societies is the improvement of healthy aging and the support to older persons in their daily activities. In particular, given its social and economic impact, the automatic detection of falls has attracted considerable attention in the computer vision and pattern recognition communities. Although the approaches based on wearable sensors have provided high detection rates, some of the potential users are reluctant to wear them and thus their use is not yet normalized. As a consequence, alternative approaches such as vision-based methods have emerged. We firmly believe that the irruption of the Smart Environments and the Internet of Things paradigms, together with the increasing number of cameras in our daily environment, forms an optimal context for vision-based systems. Consequently, here we propose a vision-based solution using Convolutional Neural Networks to decide if a sequence of frames contains a person falling. To model the video motion and make the system scenario independent, we use optical flow images as input to the networks followed by a novel three-step training phase. Furthermore, our method is evaluated in three public datasets achieving the state-of-the-art results in all three of them.

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Mazzetta, Ivan, Alessandro Zampogna, Antonio Suppa, Alessandro Gumiero, Marco Pessione, and Fernanda Irrera. "Wearable Sensors System for an Improved Analysis of Freezing of Gait in Parkinson’s Disease Using Electromyography and Inertial Signals." Sensors 19, no. 4 (2019): 948. http://dx.doi.org/10.3390/s19040948.

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We propose a wearable sensor system for automatic, continuous and ubiquitous analysis of Freezing of Gait (FOG), in patients affected by Parkinson’s disease. FOG is an unpredictable gait disorder with different clinical manifestations, as the trembling and the shuffling-like phenotypes, whose underlying pathophysiology is not fully understood yet. Typical trembling-like subtype features are lack of postural adaptation and abrupt trunk inclination, which in general can increase the fall probability. The targets of this work are detecting the FOG episodes, distinguishing the phenotype and analyzing the muscle activity during and outside FOG, toward a deeper insight in the disorder pathophysiology and the assessment of the fall risk associated to the FOG subtype. To this aim, gyroscopes and surface electromyography integrated in wearable devices sense simultaneously movements and action potentials of antagonist leg muscles. Dedicated algorithms allow the timely detection of the FOG episode and, for the first time, the automatic distinction of the FOG phenotypes, which can enable associating a fall risk to the subtype. Thanks to the possibility of detecting muscles contractions and stretching exactly during FOG, a deeper insight into the pathophysiological underpinnings of the different phenotypes can be achieved, which is an innovative approach with respect to the state of art.

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Lobanova, Vera, Valeriy Slizov, and Lesya Anishchenko. "Contactless Fall Detection by Means of Multiple Bioradars and Transfer Learning." Sensors 22, no. 16 (2022): 6285. http://dx.doi.org/10.3390/s22166285.

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Fall detection in humans is critical in the prevention of life-threatening conditions. This is especially important for elderly people who are living alone. Therefore, automatic fall detection is one of the most relevant problems in geriatrics. Bioradiolocation-based methods have already shown their efficiency in contactless fall detection. However, there is still a wide range of areas to improve the precision of fall recognition based on view-independent concepts. In particular, in this paper, we propose an approach based on a more complex multi-channel system (three or four bioradars) in combination with the wavelet transform and transfer learning. In the experiments, we have used several radar configurations for recording different movement types. Then, for the binary classification task, a pre-trained convolutional neural network AlexNet has been fine-tuned using scalograms. The proposed systems have shown a noticeable improvement in the fall recognition performance in comparison with the previously used two-bioradar system. The accuracy and Cohen’s kappa of the two-bioradar system are 0.92 and 0.86 respectively, whereas the accuracy and Cohen’s kappa of the four-bioradar system are 0.99 and 0.99 respectively. The three-bioradar system’s performance turned out to be in between two of the aforementioned systems and its calculated accuracy and Cohen’s kappa are 0.98 and 0.97 respectively. These results may be potentially used in the design of a contactless multi-bioradar fall detection system.

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Tahir, Ahsen, Gordon Morison, Dawn A. Skelton, and Ryan M. Gibson. "A Novel Functional Link Network Stacking Ensemble with Fractal Features for Multichannel Fall Detection." Cognitive Computation 12, no. 5 (2020): 1024–42. http://dx.doi.org/10.1007/s12559-020-09749-x.

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Abstract Falls are a major health concern and result in high morbidity and mortality rates in older adults with high costs to health services. Automatic fall classification and detection systems can provide early detection of falls and timely medical aid. This paper proposes a novel Random Vector Functional Link (RVFL) stacking ensemble classifier with fractal features for classification of falls. The fractal Hurst exponent is used as a representative of fractal dimensionality for capturing irregularity of accelerometer signals for falls and other activities of daily life. The generalised Hurst exponents along with wavelet transform coefficients are leveraged as input feature space for a novel stacking ensemble of RVFLs composed with an RVFL neural network meta-learner. Novel fast selection criteria are presented for base classifiers founded on the proposed diversity indicator, obtained from the overall performance values during the training phase. The proposed features and the stacking ensemble provide the highest classification accuracy of 95.71% compared with other machine learning techniques, such as Random Forest (RF), Artificial Neural Network (ANN) and Support Vector Machine. The proposed ensemble classifier is 2.3× faster than a single Decision Tree and achieves the highest speedup in training time of 317.7× and 198.56× compared with a highly optimised ANN and RF ensemble, respectively. The significant improvements in training times of the order of 100× and high accuracy demonstrate that the proposed RVFL ensemble is a prime candidate for real-time, embedded wearable device–based fall detection systems.

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Mirmahboub, Behzad, Shadrokh Samavi, Nader Karimi, and Shahram Shirani. "Automatic Monocular System for Human Fall Detection Based on Variations in Silhouette Area." IEEE Transactions on Biomedical Engineering 60, no. 2 (2013): 427–36. http://dx.doi.org/10.1109/tbme.2012.2228262.

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Moulik, Soumen, and Shubhankar Majumdar. "FallSense: An Automatic Fall Detection and Alarm Generation System in IoT-Enabled Environment." IEEE Sensors Journal 19, no. 19 (2019): 8452–59. http://dx.doi.org/10.1109/jsen.2018.2880739.

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Prof., Deepali K. Shende, Sidheshwar Madrewar Mr., and Shivam Bhongade |. Mr. Shivam Dugade Mr. "Dementia Patient Activity Monitoring and Fall Detection using IoT for Elderly." International Journal of Trend in Scientific Research and Development 3, no. 4 (2019): 363–67. https://doi.org/10.31142/ijtsrd23656.

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There is drastic increase in population of dementia patients and also elderly since we are growing as modern society. To avoid the need of special care centers for people suffering from such disease usually it is suggested to stay in their own house. The product presented in this paper provides user current localization, Real time image processing, automatic fall detection and activity monitoring and also manage emergency situations. The Product aims to help patients retain their independence, whilst reducing the demand on care givers as well as providing patients freedom of independently walks outside. Prof. Deepali K. Shende | Mr. Sidheshwar Madrewar | Mr. Shivam Bhongade | Mr. Shivam Dugade "Dementia Patient Activity Monitoring and Fall Detection using IoT for Elderly" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-4 , June 2019, URL: https://www.ijtsrd.com/papers/ijtsrd23656.pdf

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González-Cañete, Francisco Javier, and Eduardo Casilari. "A Feasibility Study of the Use of Smartwatches in Wearable Fall Detection Systems." Sensors 21, no. 6 (2021): 2254. http://dx.doi.org/10.3390/s21062254.

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Over the last few years, the use of smartwatches in automatic Fall Detection Systems (FDSs) has aroused great interest in the research of new wearable telemonitoring systems for the elderly. In contrast with other approaches to the problem of fall detection, smartwatch-based FDSs can benefit from the widespread acceptance, ergonomics, low cost, networking interfaces, and sensors that these devices provide. However, the scientific literature has shown that, due to the freedom of movement of the arms, the wrist is usually not the most appropriate position to unambiguously characterize the dynamics of the human body during falls, as many conventional activities of daily living that involve a vigorous motion of the hands may be easily misinterpreted as falls. As also stated by the literature, sensor-fusion and multi-point measurements are required to define a robust and reliable method for a wearable FDS. Thus, to avoid false alarms, it may be necessary to combine the analysis of the signals captured by the smartwatch with those collected by some other low-power sensor placed at a point closer to the body’s center of gravity (e.g., on the waist). Under this architecture of Body Area Network (BAN), these external sensing nodes must be wirelessly connected to the smartwatch to transmit their measurements. Nonetheless, the deployment of this networking solution, in which the smartwatch is in charge of processing the sensed data and generating the alarm in case of detecting a fall, may severely impact on the performance of the wearable. Unlike many other works (which often neglect the operational aspects of real fall detectors), this paper analyzes the actual feasibility of putting into effect a BAN intended for fall detection on present commercial smartwatches. In particular, the study is focused on evaluating the reduction of the battery life may cause in the watch that works as the core of the BAN. To this end, we thoroughly assess the energy drain in a prototype of an FDS consisting of a smartwatch and several external Bluetooth-enabled sensing units. In order to identify those scenarios in which the use of the smartwatch could be viable from a practical point of view, the testbed is studied with diverse commercial devices and under different configurations of those elements that may significantly hamper the battery lifetime.

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Bottelin, Pierre, Laurent Baillet, Aurore Carrier, et al. "Toward Workable and Cost-Efficient Monitoring of Unstable Rock Compartments with Ambient Noise." Geosciences 11, no. 6 (2021): 242. http://dx.doi.org/10.3390/geosciences11060242.

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Ambient Vibration-Based Structural Health Monitoring (AVB–SHM) studies on prone-to-fall rock compartments have recently succeeded in detecting both pre-failure damaging processes and reinforcement provided by bolting. The current AVB–SHM instrumentation layout is yet generally an overkill, creating cost and power issues and sometimes requiring advanced signal processing techniques. In this article, we paved the way toward an innovative edge-computing approach tested on ambient vibration records made during the bolting of a ~760 m3 limestone rock column (Vercors, France). First, we established some guidelines for prone-to-fall rock column AVB–SHM by comparing several basic, computing-efficient, seismic parameters (i.e., Fast Fourier Transform, Horizontal to Vertical and Horizontal to Horizontal Spectral Ratios). All three parameters performed well in revealing the unstable compartment’s fundamental resonance frequency. HHSR appeared as the most consistent spectral estimator, succeeding in revealing both the fundamental and higher modes. Only the fundamental mode should be trustfully monitored with HVSR since higher peaks may be artifacts. Then, the first application of a novelty detection algorithm on an unstable rock column AVB–SHM case study showed the following: the feasibility of automatic removing the adverse thermomechanical fluctuations in column’s dynamic parameters based on machine learning, as well as the systematic detection of clear, permanent change in column’s dynamic behavior after grout injection and hardening around the bolts (i1 and i2). This implementation represents a significant workload reduction, compared to physical-based algorithms or numerical twin modeling, and shows better robustness with regard to instrumentation gaps. We believe that edge-computing monitoring systems combining basic seismic signal processing techniques and automatic detection algorithms could help facilitate AVB–SHM of remote natural structures such as prone-to-fall rock compartments.

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Perwira, Satria, Muhammad Idham Ananta Timur, and Agus Harjoko. "Sistem Deteksi Orang Jatuh Dengan Menggunakan Sensor Kamera Kinect Dengan Metode AdaBoost." IJEIS (Indonesian Journal of Electronics and Instrumentation Systems) 11, no. 2 (2021): 113. http://dx.doi.org/10.22146/ijeis.49974.

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Fall cases of elderly people aged 65 or above put their health at risk because it could lead to hip bone fracture, concussion, even death. Immediate help is needed if fall happened which is why an automatic and unobtrusive fall detection system is needed. There are three approaches in fall detection system; wearable, ambience, and vision-based. Wearable approach has the drawback of its obtrusive nature while ambience approach is prone to high false positive value. Vision-based approach is chosen because its unobtrusive nature and low false positive value. This study uses Kinect camera because of its ability on extracting skeletal data. The methods that are used in the fall detection system are AdaBoost method and joint velocity thresholding method. Thresholding method is used as a comparison to AdaBoost method. Both methods use skeletal data from the subject recorded by the Kinect camera. AdaBoost method compares the skeletal data with model that was made before while thresholding method compares the joint velocity value with the threshold value. System test is done using training data, test data, and real-time data. The average accuracy obtained from the system test with AdaBoost method is 91.75% and with thresholding method is 68.22%.

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Deshpande, Janhavi. "A Survey on Automated Emotion Recognition Using Different Classification Models and Approaches." International Journal for Research in Applied Science and Engineering Technology 11, no. 11 (2023): 2291–300. http://dx.doi.org/10.22214/ijraset.2023.57029.

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Abstract: Automated emotion detection, is a diverse field with a multitude of applications ranging from software engineering to web customization, education, etc. Several methods and approaches have been devised for automatic emotion recognition, which has taken inspiration from human/natural emotion recognition. I have studied and discussed categorical and dimensional models, which can be subdivided into Circumplex, PANA, vector, and Plutchik's models, for defining a myriad of emotions under varied circumstances. I have stratified the approaches used in emotion detection by trifurcating them into lexicon-based, statistical, and hybrid methods. And, I have presented information on different types of classifiers and classes of neural networks that fall under the category of statistical methods, in a systematized way. I have observed that Support Vector Machines provide the most accurate and clear-cut outcome.

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AL-Madani, Basem, Marius Svirskis, Gintautas Narvydas, Rytis Maskeliūnas, and Robertas Damaševičius. "Design of Fully Automatic Drone Parachute System with Temperature Compensation Mechanism for Civilian and Military Applications." Journal of Advanced Transportation 2018 (November 15, 2018): 1–11. http://dx.doi.org/10.1155/2018/2964583.

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Application of Unmanned Aerial Vehicles (a.k.a. drones) is becoming more popular and their safety is becoming a serious concern. Due to high cost of top-end drones and requirements for secure landing, development of reliable drone recovery systems is a hot topic now. In this paper, we describe the development of a parachute system with fall detection based on accelerometer-gyroscope MPU – 6050 and fall detection algorithm based on the Kalman filter to reduce acceleration errors while drone is flying. We developed the compensation algorithm for temperature-related accelerometer errors. The parachute system tests were performed from a small height on a soft surface. Later, the system was tested under real-world conditions. The system functioned effectively, resulting in parachute activation times of less than 0.5s. We also discuss the civilian and military applications of the developed recovery system in harsh (high temperature) environment.

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Journal articles on the topic 'Automatic fall detection'

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