Home Camera-Based Fall Detection System for the Elderly

Miguel, Koldo de; Brunete, Alberto; Hernando, Miguel; Gambao, Ernesto

doi:10.3390/s17122864

Cited by 206 publications

(105 citation statements)

References 39 publications

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“…Computer vision-based motion detection takes a video of a person’s motion with an optical camera and uses advanced image processing algorithms to determine whether there is a frame with a motion feature to detect [ 18 ]. De Miguel et al developed a fall detection system based on a camera for the elderly that applies various algorithms to extract better features and used a K-nearest neighbors algorithm to achieve recognition [ 19 ]. Yu et al used an enhanced one-class support vector machine as a recognition algorithm and obtained features including the differences of barycenter position and orientation of a person over a time period as input [ 20 ].…”

Section: Related Workmentioning

confidence: 99%

Privacy-Preserved Fall Detection Method with Three-Dimensional Convolutional Neural Network Using Low-Resolution Infrared Array Sensor

Tateno

Meng

Qian

et al. 2020

Sensors

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Due to the rapid aging of the population in recent years, the number of elderly people in hospitals and nursing homes is increasing, which results in a shortage of staff. Therefore, the situation of elderly citizens requires real-time attention, especially when dangerous situations such as falls occur. If staff cannot find and deal with them promptly, it might become a serious problem. For such a situation, many kinds of human motion detection systems have been in development, many of which are based on portable devices attached to a user’s body or external sensing devices such as cameras. However, portable devices can be inconvenient for users, while optical cameras are affected by lighting conditions and face privacy issues. In this study, a human motion detection system using a low-resolution infrared array sensor was developed to protect the safety and privacy of people who need to be cared for in hospitals and nursing homes. The proposed system can overcome the above limitations and have a wide range of application. The system can detect eight kinds of motions, of which falling is the most dangerous, by using a three-dimensional convolutional neural network. As a result of experiments of 16 participants and cross-validations of fall detection, the proposed method could achieve 98.8% and 94.9% of accuracy and F1-measure, respectively. They were 1% and 3.6% higher than those of a long short-term memory network, and show feasibility of real-time practical application.

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Section: Related Workmentioning

confidence: 99%

Privacy-Preserved Fall Detection Method with Three-Dimensional Convolutional Neural Network Using Low-Resolution Infrared Array Sensor

Tateno

Meng

Qian

et al. 2020

Sensors

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“…The model developed by the authors consists of an algorithm to detect when a person is in the frame and a posture recognition algorithm to detect movements associated with falls that occur during the performance of sitting down and standing up movements. De Miguel et al [ 11 ] combined algorithms, ranging from Kalman filtering to optical flow analysis, with an algorithm designed for artificial vision. When a fall occurs, the system detects the event and sends an alarm notification message along with a picture to the caregiver.…”

Section: Related Workmentioning

confidence: 99%

Assessing the Feasibility of Augmenting Fall Detection Systems by Relying on UWB-Based Position Tracking and a Home Robot

Capra

Sapienza

Ros

et al. 2020

Sensors

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Falls in the home environment are a primary cause of injury in older adults. According to the U.S. Centers for Disease Control and Prevention, every year, one in four adults 65 years of age and older reports experiencing a fall. A variety of different technologies have been proposed to detect fall events. However, the need to detect all fall instances (i.e., to avoid false negatives) has led to the development of systems marked by high sensitivity and hence a significant number of false alarms. The occurrence of false alarms causes frequent and unnecessary calls to emergency response centers, which are critical resources that should be utilized only when necessary. Besides, false alarms decrease the level of confidence of end-users in the fall detection system with a negative impact on their compliance with using the system (e.g., wearing the sensor enabling the detection of fall events). Herein, we present a novel approach aimed to augment traditional fall detection systems that rely on wearable sensors and fall detection algorithms. The proposed approach utilizes a UWB-based tracking system and a home robot. When the fall detection system generates an alarm, the alarm is relayed to a base station that utilizes a UWB-based tracking system to identify where the older adult and the robot are so as to enable navigating the environment using the robot and reaching the older adult to check if he/she experienced a fall. This approach prevents unnecessary calls to emergency response centers while enabling a tele-presence using the robot when appropriate. In this paper, we report the results of a novel fall detection algorithm, the characteristics of the alarm notification system, and the accuracy of the UWB-based tracking system that we implemented. The fall detection algorithm displayed a sensitivity of 99.0% and a specificity of 97.8%. The alarm notification system relayed all simulated alarm notification instances with a maximum delay of 106 ms. The UWB-based tracking system was found to be suitable to locate radio tags both in line-of-sight and in no-line-of-sight conditions. This result was obtained by using a machine learning-based algorithm that we developed to detect and compensate for the multipath effect in no-line-of-sight conditions. When using this algorithm, the error affecting the estimated position of the radio tags was smaller than 0.2 m, which is satisfactory for the application at hand.

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“…The authors in (Gunale and Mukherji, 2015) proposed a system for patient monitoring based on KNN classification and the ratio of the fitted ellipse, orientation angle, silhouette threshold and motion coefficient as visual descriptors. The researchers in (de Miguel et al, 2017) proposed an elderly fall detection system which mainly subtracts the object (human body) from the frame background using standard background subtraction technique. Note that the ratio and angle of the bounding box contouring the human body and the ratio derivative are used as visual descriptors.…”

Section: Related Workmentioning

confidence: 99%

Fall Detection Using the Histogram of Oriented Gradients and Decision-Based Fusion

Ismail¹,

AlGabas²,

Bchir³

2020

Journal of Computer Science

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As the number of fall incidents among elderly people and patients are continuously growing, researches boosted their researches to propose efficient automatic fall detection systems. In particular, they formulated the fall detection problem as a supervised learning task where some visual features are extracted from the video frames and used to automatically identify the position of a human as "Fall" or "Non-Fall" based on a model learned using labeled training frames. Despite the promising reported results, existing fall detection systems exhibit noticeable room for improvement. Learner fusion which builds multiple models and aggregates their respective decisions is an alternative that would improve the fall detection performance. In this paper, an image-based fall detection system that captures the visual property and the spatial position of the human body using the Histogram of Oriented Gradient from the video frames is proposed. Then, the extracted features are used to train three classification models. Namely, the Naïve Bayes, the K-Nearest Neighbors and the Support Vector Machine algorithms are adopted. Next, the majority vote is used to aggregate the decisions of the individual learners. The proposed system was assessed using a standard dataset and yielded promising results. Standard performance measures along with the statistical significance t-test were used to prove that the fall detection system based on majority vote fusion outperforms the individual classifier based approaches.

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Home Camera-Based Fall Detection System for the Elderly

Cited by 206 publications

References 39 publications

Privacy-Preserved Fall Detection Method with Three-Dimensional Convolutional Neural Network Using Low-Resolution Infrared Array Sensor

Privacy-Preserved Fall Detection Method with Three-Dimensional Convolutional Neural Network Using Low-Resolution Infrared Array Sensor

Assessing the Feasibility of Augmenting Fall Detection Systems by Relying on UWB-Based Position Tracking and a Home Robot

Fall Detection Using the Histogram of Oriented Gradients and Decision-Based Fusion

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