Human fall is one of the very critical health issues, especially for elders and disabled people living alone. The number of elder populations is increasing steadily worldwide. Therefore, human fall ...detection is becoming an effective technique for assistive living for those people. For assistive living, deep learning and computer vision have been used largely. In this review article, we discuss deep learning (DL)-based state-of-the-arts non-intrusive (vision-based) fall detection techniques. We also present a survey on fall detection benchmark datasets. For a clear understanding, we briefly discuss different metrics which are used to evaluate the performance of the fall detection systems. This article also gives a future direction on vision-based human fall detection techniques.
Proactively detecting falls and preventing injuries are among the primary keys to a healthy life for the elderly. Near-fall remote monitoring in daily life could provide key information to prevent ...future falls and obtain quantitative rehabilitation status for patients with weak balance ability. In this study, we developed a deep learning-based novel classification algorithm to precisely categorize three classes (falls, near-falls, and activities of daily living (ADLs)) using a single inertial measurement unit (IMU) device attached to the waist. A total of 34 young participants were included in this study. An IMU containing accelerometer and gyroscope sensors was fabricated to acquire acceleration and angular velocity signals. A comprehensive experiment including thirty-six types of activities (10 types of falls, 10 types of near-falls, and 16 types of ADLs) was designed based on previous studies. A modified directed acyclic graph-convolution neural network (DAG-CNN) architecture with hyperparameter optimization was proposed to predict fall, near-fall, and ADLs. Prediction results of the modified DAG-CNN structure were found to be approximately 7% more accurate than the traditional CNN structure. For the case of near-falls, the modified DAG-CNN demonstrated excellent prediction performance with accuracy of over 98% by combining gyroscope and accelerometer features. Additionally, by combining acceleration and angular velocity the trained model showed better performance than each model of acceleration and angular velocity. It is believed that information to preemptively handle the risk of falls and quantitatively evaluate the rehabilitation status of the elderly with weak balance will be provided by monitoring near-falls.
Fall detection and prevention are crucial in elderly healthcare and humanoid robotic research as they help mitigate the damaging after-effects of falls. In this work, we have presented a ...deep-learning-based preimpact fall detection system (FDS) that detects a fall within 0.5 s of the fall initiation phase, thus providing a sufficient lead time of 0.5 s which is far better than the state-of-the-art. To achieve this, we have developed an automatic feature extraction methodology that can extract temporal features from all types of human fall data collected using wearable sensors. A deep neural classifier based on the ensemble of convolutional neural networks (CNNs) and long short-term memory networks (LSTMs) is trained on the extracted temporal features. The classifier has performed exceptionally well in detecting the Fall Initiation phase with a sensitivity of 99.24% and an F1-score of 98.79% for different types of falls. A sensitivity of 99.24% signifies that the model has sufficiently reduced the occurrence of false negatives, which is far more critical for an FDS. A concept of a transitional window is introduced to improve the reaction time of the FDS. We utilized two standard fall datasets, viz. SisFall and KFall, for the experimentation. Dataset fusion is employed to increase the generalizability of the system. This work can be utilized to design and develop fall detection devices for the Internet-of-Healthcare-Things applications (IoHT) and for imparting fall detection capabilities to humanoid robots and gait rehabilitation devices such as exoskeleton robots and smart prosthetic legs.
Given the high prevalence and detrimental effects of unintentional falls in the elderly, fall detection has become a pertinent public concern. A Fall Detection System (FDS) gathers information from ...sensors to distinguish falls from routine activities in order to provide immediate medical assistance. Hence, the integrity of collected data becomes imperative. Presence of missing values in data, caused by unreliable data delivery, lossy sensors, local interference and synchronization disturbances and so forth, greatly hamper the credibility and usefulness of data making it unfit for reliable fall detection. This paper presents a noise tolerant FDS performing in presence of missing values in data. The work focuses on Deep Learning (DL) particularly Recurrent Neural Networks (RNNs) with an underlying Bidirectional Long Short-Term Memory (BiLSTM) stack to implement FDS based on wearable sensors. The proposed technique is evaluated on two publicly available datasets-SisFall and UP-Fall Detection. Our system produces an accuracy of 97.21% and 97.41%, sensitivity of 96.97% and 99.77% and specificity of 93.18% and 91.45% on SisFall and UP-Fall Detection respectively, thus outperforming the existing state of the art on these benchmark datasets. The resultant outcomes suggest that the ability of BiLSTM to retain long term dependencies from past and future make it an appropriate model choice to handle missing values for wearable fall detection systems.
Fall Detection Systems (FDS) are automated systems designed to detect falls experienced by older adults or individuals. Early or real-time detection of falls may reduce the risk of major problems. ...This literature review explores the current state of research on FDS and its applications. The review shows various types and strategies of fall detection methods. Each type of fall detection is discussed with its pros and cons. Datasets of fall detection systems are also discussed. Security and privacy issues related to fall detection systems are also considered in the discussion. The review also examines the challenges of fall detection methods. Sensors, algorithms, and validation methods related to fall detection are also talked over. This work found that fall detection research has gradually increased and become popular in the last four decades. The effectiveness and popularity of all strategies are also discussed. The literature review underscores the promising potential of FDS and highlights areas for further research and development.
Falling is a severe hazard among older adults. Fall treatment is considered to be one of the most costly treatments, which usually extends to a long time. One bad fall can cause severe injuries that ...may lead to permanent disability or even death. Therefore, an efficient and cost-effective fall monitoring system is exceptionally indispensable. With the advancement in technology, wearable sensors and systems provide a lucrative way to continuously monitor the elderly people for detecting any fall incident that may occur. Most of these wearable fall monitoring systems focus only on detecting a fall incident. However, to avoid the risk of any future fall, it is essential to be aware of the cause of a fall incident also. Therefore, to address this challenge, a wearable sensor-based continuous fall monitoring system is proposed in this paper, which is capable of detecting a fall and identifying the falling pattern and the activity associated with the fall incident. The performance of the proposed scheme is investigated with a series of experiments using three machine learning algorithms, namely, <inline-formula> <tex-math notation="LaTeX">k </tex-math></inline-formula>-nearest neighbors (KNNs), support vector machine, and random forest (RF). The proposed methodology achieved the highest accuracy for fall detection, i.e., 99.80%, using KNNs classifier, whereas the highest accuracy achieved in recognizing different falling activities is 96.82% using RF classifier.
Fall detection and prevention are crucial in elderly healthcare and humanoid robotic research as they help mitigate the damaging after-effects of falls. In this work, we have presented a deep ...learning-based Pre-Impact fall detection system (FDS) that detects a fall within 0.5s of the fall initiation phase, thus providing a sufficient lead time of 0.5s which is far better than the state-of-the-art. To achieve this, we have developed an automatic feature extraction methodology that can extract temporal features from all types of human fall data collected using wearable sensors. A deep neural classifier based on the ensemble of convolutional neural network (CNN) and Long short-term memory network (LSTM) is trained on the extracted temporal features. The classifier has performed exceptionally well in detecting the Fall Initiation phase with a Sensitivity of 99.24% and an F1-score of 98.79% for different types of falls. A Sensitivity of 99.24% signifies that the model has sufficiently reduced the occurrence of false negatives, which is far more critical for an FDS. A concept of a transitional window is introduced to improve the reaction time of the FDS. We utilized two standard fall datasets, viz. SisFall and KFall for the experimentation. Dataset fusion is employed to increase the generalizability of the system. This work can be utilized to design and develop fall detection devices for the Internet of Healthcare applications (IoHT) and for imparting fall detection capabilities to humanoid robots and gait rehabilitation devices such as exoskeleton robots and smart prosthetic legs.
Accidents in industrial environments endanger the lives of workers facing challenging conditions. The major reason for fatal outcomes in such accidents arises from delays in reporting and providing ...timely medical assistance within the crucial first sixty minutes (golden hour) after the accidents. In this work, we present a safety system GoldAid that is specifically designed for industries to ensure quick incident reporting and expedite medical assistance within the critical golden hour. The proposed GoldAid system presents a significant integration of multiple sensors to provide fall detection, geo-tracking, long-range wireless communication, hazardous gas detection, vital monitoring & SOS functionalities within an Internet-of-Things framework. To cover all possible industrial fall scenarios, this work also presents a Convolutional Neural Network (CNN) model and an acceleration-threshold-based method for low-power edge devices. Moreover, to achieve minimum data loss and low latency in real-time incident reporting in a large industrial setup, a strategic model for placing multiple communication gateways is also proposed in this research. A prototype of the proposed GoldAid system is developed, and experimental results are also presented. Measurement results show that the proposed fall-detection models achieve >98.44% accuracy, and the system achieves a latency of <1 s with bit-error-rate (BER) of <3×10 -4 over a communication range of 300 m while reporting a fall incident in an actual thermal power plant.
Falling poses a significant challenge to the health and well-being of the elderly and people with various disabilities. Precise and prompt fall detection plays a crucial role in preventing falls and ...mitigating the impact of injuries. In this research, we propose a deep classifier for pre-impact fall detection which can detect a fall in the pre-impact phase with an inference time of 46-52 milliseconds. The proposed classifier is an ensemble of Convolutional Neural Networks (CNNs) and Bidirectional Gated Recurrent Units (BiGRU) with residual connections. We validated the performance of the proposed classifier on a comprehensive, publicly available preimpact fall dataset. The dataset covers 36 diverse activities, including 15 types of fall-related activities and 21 types of activities of daily living (ADLs). Furthermore, we evaluated the proposed model using three different inputs of varying dimensions: 6D input (comprising 3D accelerations and 3D angular velocities), 3D input (3D accelerations), and 1D input (magnitude of 3D accelerations). The reduction in the input space from 6D to 1D is aimed at minimizing the computation cost. We have attained commendable results outperforming the state-of-the-art approaches by achieving an average accuracy and F1 score of 98% for 6D input size. The potential implications of this research are particularly relevant in the realm of smart healthcare, with a focus on the elderly and differently-abled population.
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